“Total Returns to Single Family Rentals”, by Demers and Eisfeldt

The market value of US Single Family Rental assets totals more than $2.3 trillion. We believe that we provide the first systematic analysis of total returns to Single Family Rentals over a long time period, in a broad and granular cross section. We find that total nominal returns are approximately equalized across US cities, at about 8.5%, the same order of magnitude as equity returns.”

Note, however, that both stock and rental housing returns are both likely to be affected in the same direction by macroeconomic factors like GDP growth and interest rates.

“On average, net rental yields and house price appreciation have each contributed around half of the 8.5% total return. However, these two components are negatively correlated in the cross section. High price tier cities accrued more capital gains, while low price tier cities had higher net rental yields. Within cities, we show that lower-price-tier zip codes have higher total returns as a result of both higher yields and higher house price appreciation.”

“Why and How Systematic Strategies Decay”, by Falck et al

This paper investigates the determinants of the performance decay of known investment strategies after their publication.

The authors examine two non-exclusive possibilities: arbitrage (more money flowing into strategies and factors that have delivered high returns in the past after they become public knowledge) and overfitting (the risk that superior backtested performance resulted from chance, not a true signal, due to multiple strategies having been tested to find one that worked – at least in the past).

The authors find that “the year of publication [which captures both effects] alone explains 30% of the variance of Sharpe decay across factors…Proxies for overfitting add another 15% of explanatory power. Arbitrage-related variables only marginally contribute to return decay.”

A third hypothesis, which the authors don’t test, is that the continuing evolution of the complex adaptive system that generates financial market returns gradually invalidates the relationships on which backtested results were based, even if they weren’t overfitted.

With many commentators claiming that many asset classes are overvalued to a degree not seen since 2000 (e.g., see Robert Armstrong’s “Unhedged: Talking Bubbles with Jeremy Grantham” and John Hussman’s “The Myths Behind the Current Stock Market Bubble”, both in the Financial Times) two new research papers shed new light on how such bubbles arise.

In “When Do Social Learners Affect Collective Performance Negatively? The Predictions of a Dynamical-System Model”, Yang et al ask “whether a collective decision-making system can settle on the best available option when some members learn socially instead of evaluating the options on their own.”

They note that “This question is challenging to study, and previous research has reached mixed conclusions, because collective decision outcomes depend on the insufficiently understood complex system of cognitive strategies, task properties, and social influence processes.”

They “investigate how the interplay of the proportion of social learners, the relative merit of options, and the type of conformity response affect collective decision outcomes in a binary choice.”

Their model “predicts that when the proportion of social learners exceeds a critical threshold, a unstable state appears in which the majority can end up favoring either the higher- or lower-merit option, depending on fluctuations and initial conditions. Below this threshold, the high-merit option is chosen by the majority.

“The critical threshold is determined by the conformity response function and the relative merits of the two options…The more agents are motivated by “fitting in” rather than using social information to them determine the “right answer”, and the narrower the difference between the two options, the more likely the unstable state.”

In “Phase Transition And Cascading Collapse In Binary Decision-Making Dynamics”, Chen et al propose “an agent-based model to study the collective behaviors of individual binary decision-making process through competitive opinion dynamics on social networks.

“Three key factors are considered: bounded confidence that describes the cognitive scope of the population, stubbornness level that characterizes the opinion updating speed, and the opinion strength that represents the asymmetry power or attractiveness of the two choices.”

They find “that bounded confidence plays an important role in determining competing evolution results. As bounded confidence grows, population opinions experience a phase transition from co-existence” [of multiple views to a dominant view/decision].

“Of particular interest, we show how the combined effects of bounded confidence and asymmetry of opinion strength may reverse the initial supportive advantage in competitive dynamics” [i.e., as uncertainty increases, the more strongly held opinion can become more dominant].

“Notably, our model qualitatively reproduces the important dynamical pattern during
a brutal competition, namely, cascading collapse, as observed by real data…

“Intriguingly, we find that an increase agents’ cognitive heterogeneity can bring about randomness and unpredictability in the binary decision-making process, leading to the emergence of indeterministic oscillations.”

Our long-held, research-based view is that evolution has primed human beings’ survival instincts to increase their reliance on social learning and conformity to others’ behavior as uncertainty increases.

Our current age of hyperconnectivity has supercharged this process.
As a result, in multiple domains we observe narratives becoming more dominant, even as their underlying justifications become more fragile, setting the stage for violent, non-linear changes.

Both of these papers reinforce our view that a combination of increasing system complexity and hyperconnectivity, have led to a substantial increase not only in irreducible uncertainty, but also in the potential for rapid non-linear changes in many complex adaptive systems, including asset class valuations.

This is a new source of systematic risk, and we believe that it is still either unrecognized or underestimated by many.

“What Triggers Stock Market Jumps?” by Baker et al

SURPRISE

“What drives big moves in national stock markets? The benchmark view in economics and finance holds that stock price changes reflect rational responses to news about discount rates and corporate earnings. Under this view, we expect big daily moves to be accompanied by readily identifiable developments that affect discount rates and anticipated profitability.

“Moreover, contemporaneous news accounts should contain information about the proximate drivers of these moves. Of course, stock price behavior may not conform to the benchmark view.

“Keynes (1936), for example, famously argued that investors price stocks based not on their opinions about fundamental values but on their opinions about what others think about stock values.

“Even when speculative or irrational forces are in play, however, we expect contemporaneous news accounts to discuss the (perceived) drivers of big market moves. Thus, we turn to newspapers to distill information about what triggers big moves in national stock markets.

“Specifically, we examine next-day newspaper accounts of big daily moves (“jumps”) since 1900 in the United States, since 1930 in the United Kingdom, and since the 1980s in 14 other national markets. A threshold of 2.5 percent, up or down, for the U.S. stock market yields 1,150 jumps from 1900 to 2020. These jumps account for only 3.5 percent of trading days but nearly 20 percent of total daily variation (sum of absolute returns) and half of daily quadratic variation (sum of squared returns). Our jump thresholds for other countries range from 2 to 4 percent, with larger thresholds for markets with greater volatility. All told, we examine 6,200 daily stock market jumps across 16 national markets plus another 450 jumps in U.S. bond markets from 1970 to 2020…

“Leveraging our jump-day characterizations, we develop several novel findings. First, upward jumps attributed to policy-related news are more common than downward policy-driven jumps. This pattern holds in every country, and it has strengthened since 1980 in the United States and the United Kingdom, the two countries with pre-1980 coverage. From 1980 to 2020, upward policy jumps are twice as common as downward policy jumps in the United States.

“Over the same period, downward jumps attributed to non-policy factors are nearly twice as common as upward non-policy jumps. To put the point another way, policy-related developments trigger 43 percent of upward U.S. jumps since 1980 but only 20 percent of the downward jumps…
“Drilling down, we find that news about monetary policy and government spending is responsible for this pattern.

“One potential explanation is that positive (negative) monetary policy and government spending surprises are more (less) likely in the wake of bad economic news…

“Our second set of findings is that jumps attributed to monetary policy developments foreshadow considerably less volatility than other jumps…
“Our third set of findings pertains to clarity about the reasons for stock market jumps. Our clarity measure fluctuates over time in a positively autocorrelated manner, and it shows a clear upward trend.

“Over the past 90 years, the share of jumps due to unknown forces fell from about 35 percent to 10 percent in both the United States and the United Kingdom. The other components of our clarity index – journalist confidence, pairwise agreement rates, ease of coding – tell a similar story. As we discuss, there are sound reasons to think the trend toward greater clarity about stock market behavior reflects a combination of more transparency about corporate performance, better statistical information about the economy, falling communication costs, and the professionalization of news reporting…

“Finally, we find that news about U.S. economic and policy developments exerts an extraordinary influence on equity markets around the world. Excluding the United States and focusing on the other 15 national markets covered by our study, news about U.S.-related developments triggers 32 percent of all equity market jumps from 1980 to 2020…

“News about economic and policy developments related to European countries and supranational European institutions seldom drives jumps in non-European the countries, with the clear sustained exception of the European sovereign debt crises in the early 2010s.

“China-related news plays almost no role as a source of jumps in other countries before the mid 1990s, but China related news has since emerged as an important source of market jumps in other countries.”

“Exponential Numeracy”, by Bitterly et al

“Anticipating Trajectories of Exponential Growth”, by Hutzler et al

While not a surprise, these two research papers confirm the existence of an important, if underappreciated, source of many forecasting errors.

To be blunt, the human mind is wired to understand linear change. Most of us are befuddled by non-linear change. Unfortunately, time delayed, non-linear effects are common in complex adaptive systems like political economy and financial markets.

Bitterly et al note that, “by failing to understand exponential relationships, policy makers and individual decision makers undervalue the importance of early and aggressive interventions.”

Hutzler et al find that non only do “humans grossly underestimate exponential growth, but at the same time they are overconfident in their (poor) judgment.”

“Yale Endowment Model Architect Hunter Lewis Calls Time On It”, by Aziza Kasumov in the Financial Times

With most asset class valuations extremely high, a respected industry veteran has raised some important questions about traditional approaches to investment management.

Hunter Lewis is the co-founder of Cambridge Associates, which provides asset allocation and manager selection advice to many defined benefit pension plans, foundations, and family offices.

In Kasumov’s article, he criticizes the Yale Endowment asset allocation model (“defined by a heavy equities weighting and chunky allocations to private equity, venture capital and hedge funds”), terming it “backward looking, outdated and worn out.”

His key criticism is that, “private equity and venture capital have become too crowded and the so-called illiquidity premiums have been eaten up by large fees.”

These echo points we (and others) have made in the past (and will continue to make).

Goldman Sachs announced that it had completed its first Bitcoin-linked derivatives trades.

Just what the world needs, in the midst of overvalued global markets awash in liquidity and the speculative pursuit of higher returns…

Under pressure from activist investors frustrated by the company’s disappointing returns, Danone sacked its CEO, Emmanuel Faber, who had been a strong proponent of responsible, “stakeholder capitalism”, and darling of ESG focused investors.

SURPRISE

As the Financial Times noted, “It turns out investors, like CEOs, care about environmental, social and governance issues, but not when it affects their bottom line” (“Culture Wars: Danone Board Sours On CEO After Activist Pressure”). Other CEOs are sure to take note.

As the FT said in another article, “Faber’s fall comes at a critical point. A backlash against purpose-driven capitalism was overdue. Activists of the Friedman school are now doing battle with a new tribe of campaigning ESG investors, who believe their companies face extinction if they do not pursue environmental and social goals” (“Danone: A Case Study In The Pitfalls Of Purpose”).

“The Role of Cryptocurrencies in Investor Portfolios”, by Czasonis, Kritzman et al

SURPRISE

I’ve always found Mark Kritzman to be a very smart guy and a very clear thinker and writer. So I read this new paper with interest.

The authors ask an important question: “The role of cryptocurrencies as a vehicle for speculation has been well established. However, it is less clear if cryptocurrencies can also serve to manage risk.”

They start with this critical, but too often overlooked observation: “Even if an asset class displays a favorable correlation profile based on short-term returns, most investors care as much, if not more, about how asset classes interact over long horizons.

“An asset class that moves independently or even inversely with a portfolio’s main growth engine on a daily or monthly basis, but which also drifts in the same direction over the course of the investor’s investment horizon, does little to mitigate adverse long-term performance.”
However Kritzman and his fellow authors also recognize the challenges to addressing this issue: “Unfortunately, the longer-horizon interaction of asset classes is difficult to measure for two reasons.

“First, if the autocorrelations of either asset class or the cross-correlations between them are non-zero at any lag [i.e., annual returns are not independent and identically distributed over time], correlations based on shorter-interval returns will misestimate the correlation of longer-interval returns.

“Second, the correlation of longer interval returns, whether they are estimated as independent observations or overlapping observations, vary through time…

“Our focus therefore is to measure the extent to which other asset classes offer diversification when US stocks perform poorly and unification when they perform well”…

“After controlling for bias, we observe that when US stocks have negative returns, Treasury Bonds and Gold offer greater diversification benefits” than crytocurrencies. “The larger the loss in US stocks, the greater the co-movement between stocks and cryptocurrencies.”

However, “Though the average correlation between US stocks and cryptocurrencies is positive across all three return intervals [monthly, yearly, and 3 year], there are individual periods with highly divergent returns between the two assets. Moreover, the relationship between their three-year returns experienced a structural shift in 2018.
“Prior to 2018, their long-term co-movement was consistently positive; since then, it has often been negative, including over the most recent three-year period ended December 2020” …

The authors conclude that, “cryptocurrencies do not offer protection for investors with short horizons.

Regarding the diversification potential of cryptocurrencies for investors with long horizons, the authors find that investors without a (lottery) preference for speculative returns would require expected annual returns on cryptocurrencies of 30% or more to support any portfolio allocation to them.

“Real and Private Value Assets”, by Goetzmann et al

“Financial assets are contractual claims to benefits that flow from ownership or promises from income-producing entities. By contrast, the owner of a real asset has the right to possess and personally enjoy a particular piece of durable physical property that is typically unique (or at least in limited supply and subject to heterogeneity in quality).

“Ownership also gives the right to contract over the use of this property. The most well-known real asset type is of course real estate, be it residential, commercial, or agricultural. Other important real asset categories are physical infrastructure, and collectibles such as artworks…

“When can durable physical objects be considered assets? Commercial real estate and infrastructure projects yield cashflows, and acquirers’ intention is clearly to earn a return commensurate with the risks they are taking.

“By contrast, one might treat a cabin in the woods or an oil painting as simply a consumption good for which one pays a price and then enjoys a service flow.

“However, when a residential structure or a collectible is purchased with an expectation of a (possible) future resale—when there is an anticipated dimension of time with attendant concern for the object’s financial risk and return—then it becomes an asset…

“Real and private-value assets—defined here as the sum of real estate, infrastructure, collectibles, and non-corporate business equity—is an investment class worth an estimated $85 trillion in the U.S. alone…

“The price formation and trading process of real assets is unlike that for publicly traded equities.
“Real assets are characterized by infrequent trading in search and auction markets, market values that are hard to pin down exactly, and investment returns that can only be estimated with noise.
“Moreover, for assets such as owner-occupied housing and works of art, the use value derived from ownership is non-monetary and non-tradable, and is private in the sense that it depends on the identity of the owner.

“For private-value assets, any two potential buyers will be willing to pay different amounts—reflecting differences in preferences and relative wealth—even when they have identical resale strategies and agree on future monetary cashflows.

“Because of the illiquidity of the markets in which these assets are traded, variation in private values can translate into systematic differences in transaction prices and thus financial returns between market participants.

“Heterogeneity in beliefs about the future dynamics of private preferences—driving potential resale revenues and thus the common-value component of an asset—can further amplify the price uncertainty at any point in time.”

The country weights in the MSCI Emerging Markets Index “ain’t what they used to be.”

Back in 1995, the top 5 country weights were Malaysia (17%), South Africa (16%), Brazil (11%), Thailand (10%), and Mexico (8%).

Today they are China (38%), Taiwan (14%), South Korea (13%), India (10%), and Brazil (4%).

We’ve come a very long way from when the IFC’s Antoine van Agtmael coined the term “emerging markets” and created the first index to track their equity market returns back in the early 1980s.

“Institutional Investors and Infrastructure Investing”, by Andonov, Kraussl, and Rauh

SURPRISE

“There is a commonly heard explanation for why institutional investor demand for infrastructure has risen so much and continues to increase: that infrastructure is a new asset class with attractive attributes such as low sensitivity to swings in the business cycle, little correlation with equity markets, and long-lasting, inflation-linked cash flows.

“The underlying assets – in sectors such as renewable energy, traditional energy, transportation, utilities, information and communication technology (ICT), schools and hospitals – have long duration, are more tangible, belong to highly regulated industries, and in some cases are even backed by long concession agreements.

“As such, many institutional investors consider infrastructure a natural fit with their long-duration liabilities. The financial industry tends to support this presentation of the benefits of infrastructure investment, regulators are increasingly treating infrastructure more favorably than other private assets, and many institutional investors echo these views in their own statements of why they invest in infrastructure.

“But is infrastructure delivering cash flows and returns that would be consistent with this story?

“Infrastructure investing is organized primarily through four structures: closed private funds, direct deals, listed funds, and open-ended funds. Of these, closed private funds, which reached $486 billion of assets under management (AUM) as of 2019, represent the lion’s share of investor commitments, and also the majority of the dollar value of deals.

“The second most relevant structure is the direct deal. Direct deals are implemented by a small number of deep-pocketed investors, and not currently applicable to the majority of institutional investors as they require very large commitment to a single asset as well as specialized human capital to select and monitor these assets…

“We analyze the risk and return characteristics of infrastructure investments, as well as the drivers of their payout policy and performance. We test and reject the hypothesis that infrastructure investing through closed private funds on average delivers more stable and diversifying cash flows than other alternative asset classes.

“Instead, we find that infrastructure investment, as institutional investors primarily practice it, has pro-cyclical cash flows generated largely by quick deal exits. Despite the fact that infrastructure covers long-lived tangible assets, the business model of closed funds does not translate any potential differences in the underlying assets into different risk-return properties” …

“Despite weak risk-adjusted performance and failure to match the supposed characteristics of infrastructure assets, closed funds have received more commitments over time.”

“Does Private Equity Investment in Healthcare Benefit Patients? Evidence from Nursing Homes”, by Gupta et al

And we wonder why polls show declining support for capitalism, or perhaps our current highly financialized version of capitalism, among Millennials and Gen Z?

“The past two decades have seen a rapid increase in Private Equity (PE) investment in healthcare, a sector in which intensive government subsidy and market frictions could lead high-powered for-profit incentives to be misaligned with the social goal of affordable, quality care.

“This paper studies the effects of PE ownership on patient welfare at nursing homes…Our estimates show that PE ownership increases the short-term mortality of Medicare patients by 10%, implying 20,150 lives lost due to PE ownership over our twelve-year sample period. This is accompanied by declines in other measures of patient well-being, such as lower mobility, while taxpayer spending per patient episode increases by 11%.

“We observe operational changes that help to explain these effects, including declines in nursing staff and compliance with standards. Finally, we document a systematic shift in operating costs post-acquisition toward non-patient care items such as monitoring fees, interest, and lease payments.”

Unfortunately, this story is painfully familiar to many managers who have worked at companies that have been purchased by private equity firms.

“The ‘COVID’ Crash of the 2020 U.S. Stock Market”, by Shu et al

SURPRISE

“The 2020 U.S. stock market crash originated from a bubble which began to form as early as September 2018 …
We employed the log-periodic power law singularity (LPPLS) methodology to systematically investigate the 2020 stock market crash in the U.S. equities sectors with different levels of total market capitalizations through four major U.S. stock market indexes, including the Wilshire 5000 Total Market index, the S&P 500 index, the S&P MidCap 400 index, and the Russell 2000 index…

“Our results indicate that the price trajectories of these four stock market indexes prior to the 2020 stock market crash have clearly featured the obvious LPPLS bubble pattern and were indeed in a positive bubble regime. Contrary to the popular belief that the COVID-19 led to the 2020 stock market crash, the 2020 U.S. stock market crash was endogenous, stemming from the increasingly systemic instability of the stock market itself.”

“CIO Of £19bn Pension Pot Casts Doubt Over Returns From Illiquid Assets” by Josephine Cumbo in the Financial Times

SURPRISE

“The £19bn Local Pensions Partnership has hit out at asset managers that promote outsized returns to investors who lock up cash in long-term investments, saying the so-called illiquidity premium no longer existed for most assets.

“Richard Tomlinson, chief investment officer of the LPP, which provides retirement benefits to about 600,000 town hall workers, said it used to be the case that illiquid assets, such as infrastructure or real estate, would typically deliver “excess returns” but the situation had changed.

“Ten or 15 years ago, there was a premium paid in many areas for holding illiquids,” Tomlinson said in an interview with the Financial Times.

“It used to be an easy sale of say private credit — ‘hey, if you can lock your money up we can get you extra return’. For return-hungry investors this made sense, assuming they could wear the illiquidity.

“However, as more capital has flowed to these opportunities the returns offered have fallen. Suddenly there isn’t a premium to be had,” he said.

Competition for Attention in the ETF Space, by Ben-David et al

Count us as “shocked, just shocked” by these findings…

“Exchange-traded funds (ETFs) are the most prominent financial innovation of the last three decades. Early ETFs offered broad-based portfolios at low cost. As competition became more intense, issuers started offering specialized ETFs that track niche portfolios and charge high fees.

“Specialized ETFs hold stocks with salient characteristics| high past performance, media exposure, and sentiment that are appealing to retail and sentiment-driven investors.

After their launch, these products perform poorly as the hype around them vanishes, delivering negative risk-adjusted returns.

“Time To Look Again At The Financial System’s Dangerous Faultlines”, by Paul Tucker, former Governor of the Bank of England, in the Financial Times

SURPRISE

Given Tucker’s deep experience, we take his warnings very seriously.

“The west cannot afford another financial crisis. It would be a disaster in every possible way domestically, and a geopolitical gift to strategic competitors in Beijing and elsewhere.

“Last March and April, the fabric of our financial system was stretched almost beyond endurance. Only intervention from the north Atlantic central banks seems to have averted some kind of disaster triggered by markets grasping the pandemic was serious…

“For the dollar’s place as the world’s premier reserve currency to be secure, trading in Treasuries must remain reasonably liquid in all weathers. The same goes for government bond markets on the European side of the Atlantic.

“Three things are needed to tackle this part of the backlog of unfinished or neglected business for safeguarding stability.

“First, central banks need to dust down the plans developed a decade ago for them to act as market makers of last resort — buying and selling securities, subject to an insurance premium — when trading liquidity evaporates…

“Second, the plumbing and design of the main government bond and bond-lending markets need repairs, and possibly overhauling, if they are to cope with today’s extraordinary occasional bursts in selling activity…

“Third, and most significantly, the spectre of excessive leverage and liquidity mismatches among some types of funds and other investment vehicles really must now be addressed.

“Banking’s historical fragility is being replicated outside the industry, and without constraints or backstops. In general terms, this was foreseen: the re-regulation of banking after the 2008-09 collapse was obviously going to incentivise activity to migrate elsewhere.

“There were plans to develop policies for such shadow banking, distinguishing it from the vanilla capital markets activity that does not represent a threat to the resilient provision of essential credit, insurance and payments services. But the plans stalled, and when the shadow banking label was ditched for the much more positive sounding “market-based finance”, the issue was, in effect, whitewashed.”

“After Brookfield’s Asset Shuffle What Cards Are Left To Be Played?” by John Dizard in the Financial Times

For almost 40 years, I’ve known that John Dizard (like his brother Steve) is a very sharp guy. So I always read his columns with great interest, as he is a true insider.

In this column, John describes the hoops that Brookfield (a major Canadian property developer) is jumping through in its attempt to survive the COVID pandemic. As someone who spent a lot of years in restructuring and turnarounds, I found it a fascinating, if technical read.

But the bigger picture is this: So far, COVID-driven insolvencies have been held at bay by sharp moves like the ones Dizard describes. I also know from experience that the number of moves like this that a company can make is limited. A lot of potential insolvencies are now riding on the bet that vaccines and the Biden and EU fiscal stimulus packages will revive the economy before you can kick the can no further.

“Wealth Creation in the U.S. Public Stock Markets 1926 to 2019”, by Hendrik Bessembinder

This report quantifies long-run stock market outcomes in terms of the increases or decreases (relative to a Treasury bill benchmark) in shareholder wealth, when considering the full history of both net cash distributions and capital appreciation.

The study includes all of the 26,168 firms with publicly-traded U.S. common stock since 1926.

“Despite the fact that investments in the majority (57.8%) of stocks led to reduced rather than increased shareholder wealth, U.S. stock market investments increased shareholder wealth on net by $47.4 trillion between 1926 and 2019…

“The degree to which stock market wealth creation is concentrated in a few top-performing firms has increased over time, and was particularly strong during the most recent three years, when five firms accounted for 22% of net wealth creation.

“These results should be of interest to any long-term investor assessing the relative merits of broad diversification vs. narrow portfolio selection.”

“Waiting For The Last Dance The Hazards Of Asset Allocation In A Late-Stage Major Bubble”, by legendary investor Jeremy Grantham

Grantham makes two key points. The first is about the present state of various asset classes:

“The long, long bull market since 2009 has finally matured into a fully-fledged epic bubble. Featuring extreme overvaluation, explosive price increases, frenzied issuance, and hysterically speculative investor behavior, I believe this event will be recorded as one of the great bubbles of financial history, right along with the South Sea bubble, 1929, and 2000.”

Grantham’s second, and equally important one, is about why warnings to investors about downside risks are still all-too-rare.

"The combination of timing uncertainty and rapidly accelerating regret on the part of clients means that the career and business risk of fighting bubbles is too great for large commercial enterprises…

“Their best policy is clear and simple: always be extremely bullish. It is good for business and intellectually undemanding. It is appealing to most investors who much prefer optimism to realistic appraisal, as witnessed so vividly with COVID. And when it all ends, you will as a persistent bull have overwhelming company. This is why you have always had bullish advice in bubbles and always will."

“Global Markets Are Partying Like It Is 2008 (But a Crash Is Coming)”, by Desmond Lachman from AEI

“In late 2008, at a meeting with academics at the London School of Economics, Queen Elizabeth II asked why no one seemed to have anticipated the world’s worst financial crisis in the postwar period.

“The so-called Great Economic Recession which had begun in late 2008and would run until mid-2009, was set off by the sudden collapse of sky-high prices for housing and other assets —something that is obvious in retrospect but that, nevertheless, no one seemed to see coming.

“It would seem all too likely that now we are about to make the same mistake by being too sanguine about today’s asset and credit market bubbles.

“Certainly, the U.S. and global economies have snapped back well from the depths of the coronavirus economic recession

“It is also beyond doubt that effective vaccines have been developed and are now being distributed. However, as the Bank for International Settlements keeps warning us, global asset and credit market prices have once again risen well above their underlying value — in other words, they are in bubble territory.

“In addition, as our health experts keep warning us, we still have to get through a dark coronavirus winter before a sufficient part of the population has been vaccinated to allow a return to economic normality.

“Considering the virtual silence among economists about the danger today’s bubbles pose and about the risk of another leg down in the global economy, one has to wonder whether in a year or two, when the bubbles eventually do burst, the queen will not be asking the same sort of question.”

“How Selling To Yourself Became Private Equity’s Go-To Deal”, by Kaye Wiggins in the Financial Times

Like sky-high valuations and a new profusion of SPAC listings, PE firms selling their portfolio companies to themselves is almost certainly still another bubble indicator.

“Private equity firms have a new set of buyers for their portfolio companies: themselves.
“Blackstone, EQT, BC Partners and Hellman & Friedman are among the buyout groups to have sold companies to funds that they control this year, or made plans to do so.

“Although the model emerged before the pandemic, its use has been ignited by it. Lazard estimates that the value of such deals will hit $35bn this year, up from $7bn just four years ago.

“With the crisis leaving corporate boards warier of doing deals, the private equity industry has found it harder to keep its simple promise to investors of selling portfolio companies to outside buyers after a set period of ownership…

“The immediate post-Covid recession caused declines in M&A markets and in the ability of [private equity firms] to exit those businesses by selling them or taking them public,” said Holcombe Green, global head of private capital at Lazard. “When traditional routes to exit are reduced, the owners start to look for an alternative.”

“These transactions allow firms to hang on to good companies — an attractive prospect as the industry’s $2.5tn pile of unspent money drives up the competition for new acquisitions.

“They also provide a solution if a buyout fund nears the end of its ten-year life but has not yet sold its portfolio companies.

“To execute [these sales], a private equity firm creates a so-called continuation fund, finds investors to back it and then uses it to buy a portfolio company already owned by one of its other funds.”

“Scams In Modern Societies: How Does China Differ From The World?” by Jeff Yan

If you spend enough years working in business and finance in countries around the world, you become a connoisseur of scams and corruption. The creativity displayed is often impressive.

More than once, I’ve found myself thinking that the perpetrators could have been very legitimately successful if only they’d applied their obvious talent to honest pursuits, say at a private equity firm…
So it was with that background I read this paper and found it fascinating.

“We are all Behavioral, More or Less: A Taxonomy of Consumer Decision Making”, by Stango and Zinman

SURPRISE

This important new research highlights the importance of multiple biases that affect investors’ decisions. Given the weak effects of interventions that attempt to reduce these biases, the authors provide strong evidence for the importance of using structured decision processes (e.g., forecast combination to increase predictive accuracy) to offset their impact.

“Despite the growing impact of behavioral economics on social science research and applications, little is known about how the many potential behavioral biases fit into a taxonomy of consumer decision making. How common is it for people to exhibit multiple behavioral biases, and how heterogenous is the consumer-level portfolio of biases across consumers? How are biases correlated within-consumer, and how distinct are biases from other inputs to decision making?” …

Our first finding is that biases are more rule than exception. The median consumer exhibits 10 of 17 potential biases. No one exhibits all 17, but almost everyone exhibits multiple biases; e.g., the 5th percentile is 6.

“Our second finding is that cross-consumer heterogeneity in biases is substantial. The standard deviation of the number of biases exhibited is about 20% of its mean, and several results suggest that this variance is economically meaningful.

“Our third finding is that cross-consumer heterogeneity in biases is poorly explained by even a “kitchen sink” of other consumer characteristics, including classical decision inputs, demographics, and measures of survey effort. Most strikingly, we find more bias variance within classical sub-groups widely thought to proxy for behavioral biases than across them. E.g., we find more bias variation with the highest-education group than across the highest- and lowest-education groups.

“Our fourth finding is that our 17 biases are positively correlated with each other within consumer. Across all biases, the average pairwise correlation is 0.13”

“Volatility Expectations and Returns”, by Lochstoer and Muir

You could also substitute “uncertainty shocks” for “volatility shocks”…

“We provide evidence that agents have slow-moving beliefs about stock market volatility that lead to initial underreaction to volatility shocks followed by delayed overreaction. These dynamics are mirrored in the VIX and variance risk premiums which reflect investor expectations about volatility and are also supported in surveys and in firm-level option prices.”

“Prospects And Challenges Of Quantum Finance”, by Bouland et al

This is an excellent analysis of how accelerating progress in the area of quantum computing, and the exponential speedup of calculation it provides, will impact Monte Carlo simulation methods, portfolio optimization, and machine learning.

From a competitive perspective, it provides indicators that can be used to gauge the speed of progress in these applications.

Another month, another burst of articles about how active managers have yet again underperformed index funds.
I’ve had growing doubts about these stories, which will obviously strike some as rather strange, coming from “The Index Investor.”

Let me channel the ghost of Jack Bogle for a minute…

Back in the dark ages (1997) when we were launching the Index Investor and iShares and SPDRs were broadening their product lines, we had a few conversations with him in which he bemoaned the rise of ETFs, and warned that they would lead to excess trading (compared to index mutual funds) and an explosion of ever narrower indexes. In effect, they would become just a cheaper form of automated active management, whose virtuous index investor label would fool the punters and attract their cash. He worried this would ultimately sully the concept of passive investment in broadly defined index funds.
As was often the case, Jack was prescient; this is exactly what has happened.

Which raises a critical question: Have we reached the point where we should be comparing the performance of "old fashioned" active managers not just with each other, but also with that of ETFs that track narrowly defined indexes (e.g., "the dog food 20")?

Of course, that raises the question of where to draw the line between truly passive and automated active funds.
Here’s our take: At one extreme, investment products that track broad asset class indexes (e.g., the total stock market) should be considered passive. "Semi-Active" or "smart beta" products track recognized factors and contain a wide range of underlying assets.

Beyond here lie various approaches to de-facto active management based on narrowly defined indexes. What I would love to see one day is a comparison of “active managers” that includes the universe of narrowly defined index products. To be sure, because of the cost difference, traditional active managers may still come up short versus “indexed active” products. But at least it would be a fair fight.

“Measuring Voters’ Knowledge of Political News”, by Angelucci and Prat

We have frequently written about research on the way the interaction and evolution of narratives drive global macro, including economics, social change, politics and financial markets, including: “News And Narratives In Financial Systems: Exploiting Big Data For Systemic Risk Assessment”, by Nyman et al; “The Politics of Crisis: Deconstructing the Dominant Narratives of the Housing Crisis”, by Heslop and Ormerod; “Monetary Policy and the Management of Uncertainty: A Narrative Approach”, by Tuckett et al; and Robert Shiller’s work on narrative economics.

One of the uncertainties in this area is how long narratives, and the information on which they are based, actually persist in investors’ minds.

This paper makes an important contribution in this area. The authors report that the 33% most informed people “are 97% more likely than people in the bottom 33% to know the main story of the month.” The authors also find significant confirmation bias, with voters 10% to 30% less likely to know stories unfavorable to their political party and current views.

Finally, with respect to the rate at which narratives decay/weaken, “each month passing lowers the probability of knowing a story by 3-4 percentage points.”

See also, “Volatility Expectations and Returns” by Lochstoer and Muir, who find short-term underreaction and long-term overreaction to volatility shocks.

“Interdependent Diffusion: The Social Contagion Of Interacting Beliefs”, by Houghton and Shou

SURPRISE
“With good reason, the overwhelming majority of social contagion research over the last 50 years has assumed that diffusants spread independently of one another. Independence is an extremely useful and generative simplification. By assuming that diffusants do not interact, we can study the effects of social network structure, homophily, social reinforcement, or demographics on each contagion process in isolation…

““Interdependent diffusion” describes any social contagion process in which individuals’ likelihood of adopting diffusant A is a function of their current state of adoption of B (C, D, …) and in which their likelihood of adopting B (C, D, …) is a function of their state of adoption of A. In the social contagion literature, only a few studies explicitly allow for this type of interaction between diffusants…

“This paper asks how much does interdependence matter?” …

It “uncovers two new social processes that are unique to interdependent diffusion and which cannot be reduced to the familiar influences of network structure, homophily, social reinforcement, or demographics.

“First, when beliefs support one another’s adoption, they can “snowball” through a population to reach a broader audience than any could have reached on its own.

“Secondly, when individuals have similar belief sets, they are more likely to respond in the same way to new beliefs to which they are exposed, and so become yet more similar.

“Simulations in this paper predict that shared “worldviews” will emerge spontaneously from the process of interdependent diffusion. Specifically, subsets of a population will come to share a set of interconnected beliefs (and reject others that are equally available) without reference to any ground truth. Interdependent diffusion is also predicted to foment polarization by increasing similarity within ideological camps and difference between camps, and aligning the population along a “left-right” political axis.”

Logically, the same process can also lead to the development of market narratives that are both very different and, in many cases, surprisingly durable, even in the presence of information that casts doubt on their accuracy.

“The Persistence of Miscalibration”, by Boutros et al

“Using 14,800 forecasts of one-year S&P 500 returns made by Chief Financial Officers over a 12- year period, we track the individual executives who provide multiple forecasts to study how their beliefs evolve dynamically. While CFOs’ return forecasts are systematically unbiased, their confidence intervals are far too narrow, implying significant miscalibration.

“We find that when return realizations fall outside of ex-ante confidence intervals, CFOs’ subsequent confidence intervals widen considerably. These results are consistent with a model of Bayesian learning, which suggests that the evolution of beliefs should be impacted by return realizations. However, the magnitude of the updating is dampened by the strong conviction in beliefs inherent in the initial miscalibration and, as a result, miscalibration persists.”

“The Fallacy of ESG Investing” by Robert Armstrong in the Financial Times

As we have in past issues, Armstrong points out some of the contradictions of the increasingly popular ESG investing. Unfortunately, not enough investors will likely read his wide words before parting with their money.

“A single phrase sums up the appeal of environmental, social and governance investing: Doing well by doing good”. ESG strategies, we are told, promote the greater good and provide superior long-term financial performance…

“There are good reasons for investors to own portfolios that align with their values. This supposed win-win proposition is not one of them however. Not only is the evidence that ESG outperforms over long periods inconclusive; the win-win argument doesn’t even make sense…

“It is true that at some point in the indefinite future, the social good and financial interests must converge. There are no investment returns at all on a planet left uninhabitable by climate change. But that is not the time horizon individual investors operate over (they might have just 20 years between acquiring significant assets to invest and retiring). And it is far beyond any corporation’s planning horizon…

“There are two ways investments outperform: either they generate greater than expected cash flows over time (growth), or they are bought at a cheap price (value). Putting aside the question of growth, to argue that (say) a carbon, tobacco, and gun heavy portfolio cannot outperform over the long term is to argue that it will never be bought cheap…But of course it is the goal of the ESG movement to push investors away from “wicked” portfolios — making their prices cheap, and setting them up to outperform “virtuous” portfolios over time! The win-win pitch is a fallacy...

“Illogic is not the only problem with the win-win story. Another is performance attribution.

“ESG funds have had a nice run lately. Since its inception in late 2018, for example,Vanguard’s US ESG exchange traded fund return of 28 per cent has whipped its broad market ETF’s 17 per cent. Look, however, at the holdings of the ESG fund. The top seven holdings, accounting for a quarter of the funds’ value, are Apple, Microsoft, Amazon, Facebook, Google and Tesla. Tech has led the market this year. But has ESG, really? And if tech stocks become overpriced and their prices crash, does that mean ESG is suddenly a bad strategy? …

“None of this suggests that investors should not put their savings behind the things that they care about. It means only that they should not think of this as a wealth maximising strategy.”

“Some Perspectives on Gold in the New Paradigm”, by Jensen et al from Bridgewater

“Gold is one of the few effective diversifiers against the depreciation of paper currencies (and assets denominated in paper currency), as they all compete with gold as a storehold of wealth. And with interest rates at zero and the money supply increasing at warp speed, paper currencies are offering the worst deal ever, providing little incentive to hold them relative to gold.

“So far, this printing hasn’t produced too much in the way of inflation that erodes the real value of the currency, and it has succeeded in supporting financial assets. But given how much ongoing printing and spending will be needed, and given that replacing lost incomes is inherently more inflationary than replacing credit (as it doesn’t replace the supply those incomes were paying for), we could very well see inflationary pressures mount while the economy remains weak.

“A stagflationary outcome would put policy makers in a tough spot and leave paper currency assets vulnerable, while gold would likely be a valuable source of diversification. Stay too easy and risk further inflation (like after WWII, or most famously, the ’70s). Tighten too soon and risk plunging the world back into a deflationary downturn, like in 1937…

“In the case of a deflationary downturn without enough stimulus, defaults and bankruptcies lead to terrible returns on equities and corporate credit. Gold fares relatively better as an asset that is no one else’s liability that could be defaulted on. And while nominal bonds maintain their value under such circumstances, today there is much less potential upside going forward than there was in past cases when nominal bonds had more room to rally.

“In a successful reflation, financial assets do well as the central bank stays easy and supports a recovery, but gold is generally buoyed as well.

“Stagflation eats away at real returns of paper currency assets, while gold tends to shine as a real storehold of value.”

“How Much Information Is Incorporated In Financial Asset Prices?” by Page and Siemroth

“We investigate the informational content of prices in financial asset markets… We find that public information is almost completely reflected in prices, but that surprisingly little private information -- less than 50% -- is incorporated in prices.”

In “How Market Ecology Explains Market Malfunction”, Scholl et al add both uncertainty and time varying weights of different investment strategies that are active in markets at any point in time as further reasons why financial markets can operate far from equilibrium for long periods of time.

Both of these new papers reinforce critical points that we have made time and again:

Market prices don’t fully reflect the implications of investors’ private information, nor do they fully take into account the effects of uncertainty (which unlike risk cannot be quantified, priced, and transferred), perhaps the most important of which is investors’ tendency to copy the behavior of others when it is high.

These factors can cause the emergence of large losses, which usually happen much more suddenly than large gains.

In its September Quarterly Review, the Bank for International Settlements was surprisingly blunt:

“Financial markets recorded further gains during the review period, despite the challenging macroeconomic outlook. A divergence emerged between, on the one hand, elevated stock valuations and tightening credit spreads and, on the other, the reality of an economic recovery that looked incomplete and fragile…

“The evolution of aggregate equity valuations appeared to be somewhat at odds with the general economic outlook…

“Similarly to aggregate stock market patterns, credit spreads looked remarkably tight when contrasted with subdued expectations for the real economy…

“These spreads indicate that credit markets seem to expect that corporate bankruptcy rates will continue to be low, even though this would be at odds with historical experience. Concretely, if historical relationships continued to hold, the 2020 GDP growth forecasts – ranging between –(4.5%) and (11.0%) – would be consistent with bankruptcies increasing by 20–40% in 2020.”

Today’s markets seem to be an excellent example of the points made above – as the BIS notes, valuations seem to be at substantial variance with economic reality.

In his typical fashion, the Financial Times’ John Dizard bluntly noted the implications of this divergence in his column, “Why Be a Hero? Sell ‘Em All.”

As he warned, “This rally, by the way, is pretty close to the recovery in US shares between November 1929 and May 1930.”

“Roboadvisers Make Slow Progress Gaining Ground With Investors”, by Rheaa Rao in the Financial Times.

“Many large asset managers have shelled out time and money to develop roboadvisers. But, while assets invested in them are growing, only a small proportion of investors actually use such digital services, according to a report by data and analytics firm Hearts & Wallets.

“Just 8 percent (10 million) of US households report having money in such services… Use of robos is highest among millennials and so-called Generation X households (those born between the mid 1960s and early 1980s), with 13 per cent and 10 per cent respectively enrolled in robo…More than half of investors who use robo-advisers appear to be nudged into them by companies whose funds they already use.”

Will COVID sink roboadvisers? Here’s why it seems likely:

(1) Roboadvisers generally stay fully invested; they may switch between asset classes, and securities within them, but generally not into cash.

(2) Consider the nature of the algorithms that are making these allocation decisions. The fundamental problem is that the system that generated the data on which they were trained is constantly evolving (i.e., non-stationary). In such systems, particularly if they are quickly evolving, the past is a poor basis for predicting the future.

To be sure, some algorithms are continuously updated on the basis of the latest market results. Yet as we have seen, there are many reasons that markets can operate far from equilibrium for long periods of time. Hence constantly updating an algorithm using recent data is arguably a form of herding that does not eliminate, and in fact may increase exposure to the rapid emergence of large losses.

Other algorithms seek to avoid such losses by incorporating various signals that in the past have provided early warning of trouble ahead. But this works only under two conditions: the signals have to remain valid, and relatively few algorithms have to incorporate them – otherwise when they appear they will trigger a sudden rush to sell that may actually worsen realized losses.

As we have repeatedly noted, current machine learning algorithms are based on associational, not causal or counterfactual reasoning.

That is why human beings are able to reason about complex adaptive systems like global macro and financial markets beyond the “detection horizon” of algorithms, and spot substantial downside risks before they generate signals that will trigger algorithmic portfolio allocation decisions.

On the other hand, that is also why in in our Technology Evidence File you will see a substantial amount of evidence related to the rate at which algorithmic implementation of more complex causal and counterfactual reasoning, and related technologies (e.g., natural language processing, knowledge networks, and agent based modeling) are developing.
In so far as the global political economy and financial markets are now, post-COVID, operating in unchartered territory, we suspect that at some point roboadvisers’ algorithms may be blindsided by the tail events that we know complex adaptive systems produce, with the effects amplified by their tendency to always remain fully invested, regardless of metrics indicating worsening overvaluation across multiple asset classes.

“BDs Selling Far Riskier Investments Than RIAs, NASAA Finds”, by Tracey Longo

“Broker-dealers were two to eight times more likely than registered investment advisors to recommend risky investments in 2018,according to the findings of nationwide regulatory examinations designed to benchmark the practices of more than 2,000 firms and 360,000 practitioners working with 68 million retail investor accounts…

“One finding that stands out from the examinations, performed in 34 states: When complex products were sold, broker-dealers were twice as likely as investment advisors to recommend the purchase of leveraged and inverse ETFs, seven times as likely to recommend private placements, eight times as likely to recommend variable annuities, and nine times as likely to recommend non-traded REITs, the North American Securities Administrators Association (NASAA) said in its exam report.”

We’re just shocked, shocked.

RIAs have a fiduciary duty to put the client’s interest ahead of their own. Broker Dealers do not have a fiduciary duty to clients, and must only be able to justify that an investment was suitable, given a client’s needs.

However, much the BD community may argue that in practice there is no difference between fiduciary duty and suitability, surveys like this are a strong argument that very significant differences still exist.

August saw multiple commentaries questioning current equity market valuations.

For example, in “Reasons Not to Be Cheerful”, GMO’s James Montier (whose thinking I have long admired) wrote that, “Never before have I seen a market so highly valued in the face of overwhelming uncertainty.”

He noted that he had “written countless times over the years that overoptimism and overconfidence are a particularly heady and potently dangerous combination because they lead to the overestimation of return and the underestimation of risk. This combination strikes me as the best description of our current juncture…Voltaire observed, “Doubt is not a pleasant condition, but certainty is absurd.” The U.S. stock market appears to be absurd.”

Similarly, in “What Explains The Covid-19 Stock Market?”, Cox et al conclude that, “market movements during COVID-19 have been more reflective of sentiment than substance.

The combination of substantially overvalued stock markets, and poor appreciation of the extensive damage being done to the real economy as a result of the COVID pandemic are setting the stage for another sharp uncertainty shock.

If this occurs before the November election, all else being equal it will increase the probability that Biden will win.

In “Choosing Investment Managers”, Goyal et al reach damning conclusions about the effectiveness of pension consultants who advise plan sponsors on investment manager selection.

Specifically, they “study how plan sponsors choose investment management firms from their opportunity set when delegating $1.6 trillion in assets between 2002 and 2017. Two factors play an influential role in choice: pre-hiring returns, and pre-existing personal connections between personnel at the plan (or consultant advising the plan), and the investment management firm.

“Post-hiring returns for chosen firms are significantly lower than those for unchosen firms. The post-hiring returns of firms with relationships are, at best, indistinguishable from those without relationships, and often significantly worse.

“While relationships are conducive to asset gathering by investment managers, they do not appear to generate commensurate benefits for plan sponsors via higher gross returns or lower fees.”

This paper provides yet more evidence that the probability of sustaining true investment skill declines exponentially with time as the complex system generating returns on multiple assets and asset classes continues to evolve.

Balanced against this evidence, however, is a powerful mix of overoptimism, overconfidence, and/or incentives that cause some people – including those with a fiduciary duty to others – to reject it, and/or to believe that it does not apply to them.

“Past performance is not indicative of future results” is a warning found in every investment product’s prospectus. In “Return Expectations of Institutional Pension Investors”, Andonov and Rauh provide further evidence that many the world’s largest investors don’t believe it.

Specifically they study the publicly disclosed asset class return assumptions and portfolio allocations used by public pension funds in the United States, which manage USD 4 trillion in assets.

They ask, “what do institutional investors believe about the expected returns of the asset classes in which they invest, and how do they set these expectations?” They find that, “institutional investors rely on past performance in setting future return expectations, and these extrapolative expectations affect their target allocations [to different asset classes].”

The authors acknowledge that, “extrapolating past returns to future expectations could be justified if there is long-term persistence in the cross-section of pension fund performance. In particular, it would be rational to extrapolate past performance if the extrapolation were based on asset classes where past performance robustly predicts future performance, due to better skill or access to higher-quality external managers.” However, they find that this is not the case.

Because of its unique methodology, this paper provides exceptionally strong evidence that, despite explicit warnings against it, past investment performance still exerts a powerful influence even on the decisions of sophisticated managers with a fiduciary duty of care to pension plan beneficiaries.

“How to Lose a Billion Dollars Without Really Trying”, by Leanna Orr in Institutional Investor

Whether in the form of insurance products, equity put options, or more complicated derivatives, selling protection against losses due to adverse events is always a seductive but dangerous game.

It’s seductive because it looks like easy money – collecting premium for bearing exposure to risks that have a very low level of occurring.

But far more dangerous than people want to accept, because if those risk exposures are generated by a complex adaptive system (like a financial market), they have a power law distribution, with a lot of irreducible uncertainty about how large your losses could be in the far reaches of its tail.
Strategically, sellers of protection should be aware that such exposures exist.

Operationally, they may even have thought about how different scenarios could generate them – but knowing that in complex adaptive systems forecast accuracy degrades exponentially as the time horizon lengthens.

Essentially, when you’re selling protection, there’s an underlying assumption that you will spot an exponentially growing exposure and be able to close out your positions before others do.

As this analysis shows, when COVID-19 hit, a lot of big hedge funds that were long volatility (i.e., that had sold protection) lost impressively large amounts of money for their investors.

If you spend enough years in financial markets, you realize that this is a plot line that repeats with depressing regularity.
See also, “Fed Regulator is Fed Up with Hedge Funds’ Behavior”, about the Fed’s forced bailout of hedge funds on the wrong end of Treasury trades, by John Dizard in the Financial Times

“The ESG Concept Has Been Overhyped And Oversold”, by Bradford Cornell from UCLA

SURPRISE
Another valuable and frequently repeated piece of investing wisdom, ignored at ones peril, is that if something seems to good to be true, it probably isn’t. In this column, professor Cornell makes that case for ESG investing.

“The environmental, social and governance bandwagon is rolling. Companies are becoming ESG advocates, tempted by promises that they will become more profitable and valuable if they follow the ESG script, say the right things and spend money improving their ESG ratings.

“Meantime, institutional investors, drawn by the allure of earning higher returns while keeping their consciences clean, are directing tens of billions of dollars to “good” companies with high ESG ratings.

“Much as we would like to accept this virtuous story, we believe that the whole concept has been overhyped and oversold. Furthermore, it is backed by weak to non-existent evidence of promised pay-offs for either companies or investors, and fraught with internal inconsistencies that undercut its credibility” …

“To assess the current dogma, we start with the premise that for a company’s social consciousness to affect its value, it has to change either the cash flows that it generates or alter the risk of those cash flows.

“From that perspective, the best-case scenario for ESG is that consumers will buy more of the products and services offered by good companies, allowing these companies to increase future cash flows. That argument works for niche companies such as Patagonia, which serve a small, upscale market of socially conscious consumers. It may not for bigger companies that have to cater to larger, more price-conscious markets…

“A second way that ESG and value may be positively linked is if bad companies are punished in capital markets because investors require higher expected returns to hold them, leading to lower stock prices…

“The strongest evidence in favour of ESG is on the discount rate front. There are signs that “sin” stocks such as tobacco or weapons companies face higher costs of funding than good companies. But that is a double-edged sword. If, as ESG advocates argue, fund managers prefer to invest in “good” companies and reward them with higher values, investors who buy at those higher values will earn lower returns over time.

“One hopeful note for investors is that there seems to be a pay-off to investing in good companies before the market recognises and prices in that goodness. But with the attention paid to ESG growing rapidly, such opportunities are likely to disappear quickly…

[However], “it is impossible to have an honest discussion about ESG when its advocates believe that they occupy the moral high ground and view disagreement as immoral or unethical.”

“What is Certain About Uncertainty?” by Cascaldi-Garcia et al from the Board of Governors of the Federal Reserve

SURPRISE
The authors present an excellent, thorough overview of what are, essentially, measures of risk. Only at the end do they focus on the distinction between risk and Knightian uncertainty. Unsurprisingly, they find few measures of the latter; those they highlight are based on individual levels of ambiguity aversion.

This paper highlights a point we have been making in our writing for almost 25 years: The dominant form of doubt about the future in complex adaptive systems like political economies and financial markets is Knightian Uncertainty, not risk.

Even with today’s machine learning technologies, it resists quantitative analysis (though when AI technologies acquire causal and counterfactual reasoning capabilities, that will change). Instead, it must be approached using qualitative tools, particularly over longer time horizons. To the extent that customers for such analysis desire probabilities, they must settle for ones that are unavoidably Bayesian, and not grounded in frequentist statistics.

“Sentiment and Uncertainty”, by Birru and Young

This paper’s findings is consistent with previous research that has found individuals’ willingness to conform to the views and behavior of a group, and hence their use of social learning and copying, all increase with uncertainty.

“Sentiment should exhibit its strongest effects on asset prices at times when valuations are most subjective. Consistent with this hypothesis, we show that a one-standard- deviation increase in aggregate uncertainty amplifies the predictive ability of sentiment for market returns by two to four times relative to when uncertainty is at its mean.”

“Re-evaluating cryptocurrencies’ contribution to portfolio diversification”, by Schmitz and Hoffman

“By including cryptocurrencies in their portfolios, investors predominantly cannot reach a significantly higher efficient frontier. These results also hold, if the non-normality of cryptocurrency returns is considered.”

“Bitcoin is Not a New Type of Money” by Lee and Martin of the Federal Reserve Bank of New York

This new analysis from the NY Fed very clearly punctures many of the myths that surround cruptocurriences.

“Bitcoin, and more generally, cryptocurrencies, are often described as a new type of money. In this post, we argue that this is a misconception. Bitcoin may be money, but it is not a new type of money. To see what is truly new about Bitcoin, it is useful to make a distinction between “money,” the asset that is being exchanged, and the “exchange mechanism,” that is, the method or process through which the asset is transferred.

“Doing so reveals that monies with properties similar to Bitcoin have existed for centuries.

“However, the ability to make electronic exchanges without a trusted party—a defining characteristic of Bitcoin—is radically new. Bitcoin is not a new class of money, it is a new type of exchange mechanism, and this type of exchange mechanism can support a variety of forms of money as well as other types of assets.”

“Active Management Is in Trouble (Again)” by Jeffrey Corrado

“The COVID-19 crisis thrusts the asset management industry to the brink—this moment could define active management for the next generation. As investors pile back into stocks after a ~36% decline in the S&P 500 and ~44% drop in the Russell 2000, research by Portformer and Passiv.AI indicates that active managers struggled to mitigate downside risk and failed to outperform their passive counterparts through recent market turmoil…

“Rather than generating outsized returns during rallies, outperforming funds generate the bulk of their alpha through lower losses than their benchmarks during market corrections…

“Many active managers promote mitigating downside risk as a primary value-add over lower fee passive strategies—a claim compelling enough to convince allocators to continue paying higher fees even as upside performance lagged in recent years.

“That active managers failed to fulfill this mandate in the COVID crisis mirrors their underperformance during the financial crisis, when only 30% outperformed their benchmark…

“Active managers must rethink their investment strategies, differentiate these strategies from passive offerings, and explore modern asset management technology to improve performance while lowering fees…

“Modern asset management technology could streamline the investment research process and improve performance, but active managers have been reluctant to invest in such technology due to high upfront costs and the need to update legacy technology systems.”

“A Bloodbath Awaits Commercial Property Investors” by John Dizard in the FT

SURPRISE

Dizard once again triggers our PTSD from credit crises past…

“It is sad to see all those fresh-faced and eager “distressed asset managers” marching forth with cash-stuffed backpacks through the cheering throngs of pension sponsors, sovereign wealth funds and family office staff. Those who have seen more than one cycle know many of them will not survive.

“Or at least the money they’re armed with will be lost on the bloody fields of bankruptcy courts, unfixable operating companies and deserted properties. That is usually what happens to inexperienced “deep value” managers when they are given can’t-miss opportunities at the beginning of the down cycle. The cheap assets and arbitrages they initially discover turn into life-sucking disasters…

“The optimistic outcome being peddled by property promoters is that they can see through the temporary problems such as plague, depression and rage-fuelled politics. They are, they say, patient investors, with your money, that is.
“And that might work, but there is too much debt to service here. Property managers can defer some rents, finance some extensions, and dress up balance sheets for one last orgy of equity raises. That might get them through the end of this year, but not longer.”

For a slightly less pessimistic assessment, see: “Is Investors’ Love Affair with Commercial Property Ending?” in The Economist, 25Jun20 edition.

Criticism of private equity as an asset class continued this month

In “Financial Wizardry Breathes Magic Into Private Equity Returns”, the FT’s Chris Flood describes how by borrowing debt secured against committed but undrawn investments by Limited Partners, funds’ have boosted their internal rate of return. Flood notes that the Institutional Limited Partners Association has complained that this “subscription line financing” has made it a “near impossible task” to understand the meaning of PE Funds’ stated IRRs.
While the cash multiple (cash returned to LP/ cash invested by LP) is a much more difficult metric to game, it is still not as popular as IRRs.

See also: “The Real Money Heist is Taking Place in Private Equity”, and “Pension Funds Are Playing a Loser’s Game in Alternative Assets” by Jonathan Ford, and “SEC Censures Private Equity and Hedge Fund Managers Over Fees”, by Chris Flood, all in the FT, and “Endowment Performance” by the legendary Richard Ennis.

The latter concludes that, “alternative investments have failed to provide putative diversification benefits post-GFC and have been a drag on endowment performance.”

“Zeroing in on the Expected Returns of Anomalies”, by Chen and Velikov of the Federal Reserve Board

SURPRISE

“The early 2000s saw a revolution in information and trading technologies, implying that data from earlier decades may not be informative about the future.”

In this paper, the authors “zero in on the expected returns of anomalies by accounting for both trading costs and the staleness of historical data.”

Their key result is that, “net of these effects, expected returns are effectively zero.”

More specifically, they focus “on the expected returns of long-short portfolios based on 120 stock market anomalies by accounting for (1) effective bid-ask spreads, (2) post-publication effects, and (3) the modern era of trading technology that began in the early 2000s. Net of these effects, the average anomaly’s expected return is a measly 8 bps per month. The strongest anomalies return only 10-20 bps after accounting for data-mining with either out-of-sample tests or empirical Bayesian methods.”

This month, the US Department of Labor, which regulates ERISA plan fiduciaries, proposed a new rule clarifying that private employer-sponsored pension plans cannot invest in ESG funds that sacrifice returns or take on additional risk.

It is important to note that the proposed rule does not cover investments by individuals or public sector pension funds, both of which are still allowed to sacrifice return and/or take on additional uncompensated risk in pursuit of their investment policies’ and portfolios’ environmental, social, and governance goals.

See also, “US Asset Managers Set to Fight Proposals on ESG Investments”, by Billy Nauman in the FT

“The Performance of Hedge Fund Performance Fees”, by Ben-David et al

Along with recent analyses of private equity returns, this paper will in the future be referenced as part of the answer to the classic investment management question: “And where are the customers’ yachts?”

The authors “study the long-run outcomes associated with hedge funds' compensation structure. Over a 22-year period, the aggregate effective incentive fee rate is 2.5 times the average contractual rate (i.e., around 50% instead of 20%). Overall, investors collected 36 cents for every dollar earned on their invested capital (over a risk-free hurdle rate and before adjusting for any risk).

“In the cross-section of funds, there is a substantial disconnect between lifetime performance and incentive fees earned. These poor outcomes stem from the asymmetry of the performance contract, investors' return-chasing behavior, and underwater fund closures.”

“An Inconvenient Fact: Private Equity Returns and the Billionaire Factors” by Ludovic Phalippou

SURPRISE

In this explosive new paper, Phalippou painfully reminds readers about the critical difference between value creation and value capture. In doing so he also raises fundamental questions about whether the decades long growth in private equity investment is sustainable for much longer.

“As of the end of 2019 (i.e., right before Covid-19), since at least 2006, net of fees performance of PE funds matched that of public equity markets.6 Despite this lack of clear outperformance, the fee structures are such that a few individuals shared a large performance-related bonus payment, known as Carry, which added up to $230bn for funds raised over the decade 2006-2015 (these are the most recently raised funds that terminated (or were close to terminating) their investment period as of 2019 year-end).

“It is widely believed that the providers of capital should gladly pay Carry to fund managers because it means that returns have been good. A first caveat is that Carry works only in one direction. Hence, an investor may end up paying Carry to some managers even if its overall PE portfolio performed poorly.

“Second, Carry is paid as a fraction of absolute performance, rather than relative performance. Hence, although the latest decade of funds that terminated their investment period (2006-2015 vintages) returned about the same as public equity benchmarks (about 11% p.a.), their managers still received $230bn of Carry, alongside a lot of other fees.

“Most of this money went to a relatively few individuals, mostly founders of large PE firms. I find that the number of PE multibillionaires rose from 3 in 2005 to 22 in 2020, and are mostly affiliated to large PE firms…

“Large pension funds have earned about $1.5 (net of fees) per $1 invested in PE funds (both since 2006, and since inception). At least since 2006, this return has been the same as what public equity has returned.”

Phalippou notes that, “this wealth transfer might be one of the largest in the history of modern finance: from a few hundred million pension scheme members (plus Endowments, Sovereign Wealth Funds, Family offices, etc.) to a few thousand people working in private equity.”

Pointedly, he asks, “How could this be an economic equilibrium?”

“Why are trustees, investment teams, external managers, consultants, and others not seeing through this? Maybe because their livelihood depends on them not seeing it. Net-of-fee performance of PE funds being superior to that of public equity is the sine qua non condition for continued employment of at least 100,000 people.

“The importance of this condition might explain why the mantra of ‘PE outperforms’ has for many people, who work in and around PE, become a quasi-religious article of faith. Merely to question it is considered heresy: either you believe and you are one of us, or you question the existence of outperformance and you are an enemy. The level of emotion generated by the mere questioning of PE outperformance is, in my experience, second to none in the financial industry.

“In addition, many individuals want to avoid embarrassment; think of a pension fund board admitting paying billions of Carry in order to achieve the same returns as public equity markets.”

“Advisors Rank Almost As Low As Mechanics on Trustworthiness” by Asia Martin

“Investors trust financial advisors about as much as they trust mechanics, according to the CFA Institute’s annual report “Earning Investors’ Trust: How the Desire for Information, Innovation, and Influence Is Shaping Client Relationships.”

“Advisors ranked fourth out of six types of professionals when investors were asked which they consider to be more trustworthy.

“Nearly a third (32%) of investors ranked advisors last or second to last for trustworthiness; 46% placed advisors in the third or fourth spot; and just 23% put them at the top of their list.

“Mechanics were ranked lower than advisors, but not by much. Politicians had the worst ranking, with 83% of investors giving them low trust scores.”

“The study also found that nearly three out of four (73%) participants preferred human advisors over robos, which hasn’t changed since 2018.”

“The Murder-Suicide of the Rentier: Population Aging and the Risk Premium”, by Kopecky and Taylor

SURPRISE

“Population aging has been linked to global declines in interest rates. A similar trend shows that equity risk premia are on the rise. An existing literature can explain part of the decline in the trend in safe rates using demographics, but has no mechanism to speak to trends in relative asset prices.

“We calibrate a heterogeneous agent life-cycle model with equity markets, showing that this demographic channel can simultaneously account for both the majority of a downward trend in the risk free rate, while also increasing premium attached to risky assets. This is because the life cycle savings dynamics that have been well documented exert less pressure on risky assets as older households shift away from risk.

“Under reasonable calibrations we find declines in the safe rate that are considerably larger than most existing estimates between the years 1990 and 2017. We are also able to account for most of the rise in the equity risk premium. Projecting forward to 2050 we show that persistent demographic forces will continue push the risk free rate further into negative territory, while the equity risk premium remains elevated”

“The Death of Trust Across the Finance Industry”, by Limbach et al

SURPRISE

Having arrived on Wall Street in the late 1970s, this paper struck a powerful chord, as it supports what I have anecdotally observed over the years with more systematic evidence.

The authors show how trust has evolved in the finance industry over the long run, using data from a representative U.S. survey between 1978 and 2016. They find that “the level of trust of finance professionals has not only declined in absolute terms, but also relative to the general U.S. population. Simply put, while generalized trust has declined in U.S. society as whole, it has declined significantly more across finance professionals. This relative decline in trust is unique to finance.”

They also find that “the relative decline in trust is particularly strong in the investment sector and among professionals with higher seniority, i.e., those who set the tone [in their organizations and in the industry as a whole]”.

“AntiNoise”, by Cheng and Struck

SUPRRISE

The authors “employ machine learning techniques to quantify the extent to which noise traders' behavior creates systematic patterns in returns.”

Studying U.S. equity markets, they “find that noise traders account for less than 1.21% of stock returns in recent years.” That is far less than most people would likely estimate.

“Who Benefits from Robo-advising? Evidence from Machine Learning”, by Rossi and Utkus

SURPRISE

The authors “study the effects of a large U.S. hybrid robo-adviser on the portfolios of previously self-directed investors. Across all investors, robo-advising reduces investors' holdings in money market mutual funds and increases bond holdings. It also reduces idiosyncratic risk by lowering the holdings of individual stocks and US and international active mutual funds and raising exposure to low-cost indexed mutual funds. It further eliminates home bias by significantly increasing international equity and fixed income diversification.

“Investors who benefit from robo-advice are those with little self-directed investment experience on the platform, those with prior high cash holdings, and those with high trading volume before adopting advice. Individuals invested in high-fee active mutual funds also display significant performance gains.”

This analysis is fine as far as it goes. But sometimes it is the dog that doesn’t bark that provides the most important information.
Because this analysis was completed before the COVID19 shock hit financial markets, it is silent on whether robo clients were fully invested when it hit. At the end of 2019, our model portfolio was fully defensive (with significant allocations to cash and gold), due to very high valuations across multiple asset classes and rapidly strengthening economic headwinds.

When it comes to achieving long-term investing goals, over the years, here at The Index Investor we have repeatedly emphasized that avoiding large losses is mathematically more important than achieving big gains.
Consider this simple example: If you start with 100, and earn 50%, you have 150. If you then lose 50%, you are left with 75. Another 50% gain only brings you back to 112.50.

Whenever it is written, we look forward to reading how robo-advisors (and their clients) performed during the COVID19 shock.

“Coronavirus mayhem reflects phenomenon of ‘shock-led’ markets”, by Robin Wigglesworth in the Financial Times

“The latest burst of market mayhem has been notable both for its severity and the tranquility that preceded it… Markets have always been prone to booms and busts. But the intensity of last week’s rout also reflects a recent phenomenon, where market turbulence evaporates for long
stretches, but the market is then rattled by more violent shocks.”

Wigglesworth notes a number of factors that have driven this change, including (1) the increased use of options; (2) continuing use of the same value-at-risk metrics that were implicated in the 2008 crash; (3) the rise of funds that target a given level of volatility; and (4) the deterioration in market liquidity over time.

“Shaking Things Up: On the Stability of Time and Risk Preferences” by Beine et al

SURPRISE
Using the experience of two very serious earthquakes, this very timely paper explores the impact of natural disasters on individuals’ time and risk preferences.

They find that following such a disaster (like COVID19), risk aversion increases and time horizons shrink. Moreover, these effects become stronger with repeated disasters (e.g., a possible second wave of coronavirus infections).

“Glitchy coronavirus markets cause quant funds to misfire”, by Wigglesworth and Aliaj in the FT

“So-called quantitative funds rely on high-powered computers, vast data sets and algorithms to systematically exploit patterns in securities prices. Their success has spawned a multitude of copycats and led many traditional investment groups to try to emulate their techniques”…

“The ferocity of the recent market turmoil has inflicted some painful losses, forcing many quants to ratchet back their positions. One big investor in hedge funds described the numbers from his quant portfolio as a “disaster”, while some say the setbacks resemble a “quant quake” — a reference to a brief but traumatic period for the industry in August 2007.”

The COVID19 market shock has once again reminded investors that maturity transformation – funding short term to make long term investments in relatively illiquid assets – is always a potentially dangerous game. This is especially the case when you leverage up your short term funding, and don’t have access to a central bank’s discount window or another source of emergency government funding.

In contrast to the Great Financial Crisis in 2008, by the time of the COVID19 pandemic arrived corporate bond ETFs and mutual funds had become much larger buyers of corporate debt.
Similarly, private credit funds have also become much more important in the last decade.

Given this, it didn’t come as a surprise that ETFs that promised daily liquidity to investors but invested in relatively illiquid corporate debt ran into problems, with their shares trading at deep discounts to the apparent net value of their underlying assets (reflecting uncertainty about both future defaults and market liquidity). Nor was it a surprise that some REITs suspended investor redemptions.

What was surprising was what happened next: The Fed decided to buy investment grade and high yield corporate bond ETFs to prevent further ETF price dislocations and worsening conditions in the underlying corporate bond market. This was a first – legally, the Fed is only supposed to buy government issued or guaranteed bonds. Technically, the Fed is executing the EFT purchases through a Special Purpose Vehicle that provides a fig leaf of legal compliance. But the Fed has crossed another previous red line.

And thus far, it has yet to take action towards private credit funds.

What remains to be seen is how the Fed and the regulators will react when and if the problem shifts from short-term market liquidity challenges to dealing with widespread defaults.

“The Millennial Boom, the Baby Bust, and the Housing Market”, by Bolhuis and Cramer

SUPRRISE
“As baby boomers have begun to downsize and retire, their preferences now overlap with millennials’ predilection for urban amenities and smaller living spaces. This confluence in tastes between the two largest age segments of the U.S. population has meaningfully changed the evolution of home prices in the United States…

“From 2000 to 2018 (i) the price growth of four- and five-bedroom houses has lagged the prices of one- and two-bedroom homes, (ii) within local labor markets, the relative home prices in baby boomer-rich zip codes have declined compared with millennial-rich neighborhoods, and (iii) the zip codes with the largest relative share of smaller homes have grown fastest.

“These patterns have become more pronounced during the latest economic cycle, [and] are concentrated in areas where housing supply is most inelastic. If this pattern in the housing market persists or expands, the approximately $16.5 trillion in real estate wealth held by households headed by those aged 55 or older will be significantly affected.

“We find little evidence that these upcoming changes have been incorporated into current prices.”

Global Private Equity Report, 2020, by Bain and Company

SURPRISE
“Most PE firms are not achieving their projected margin expansion. Average margins are 3.3% below deal model forecasts, with 71% of investments falling short, including 14 or 18 that identified margin improvement as critical to value creation (based on 65 fully realized buyout deals completed between 2009 and 2015).”

“Can Investors Time Their Exposure to Private Equity?”, by Brown et al

“Private equity performance, both for buyouts and venture capital, has been highly cyclical: periods of high fundraising have been followed by periods of low performance. Despite this seemingly predictable variation, we find modest gains, at best, to pursuing realistic, investable strategies that time capital commitments to private equity. This occurs, in part, because investors can only time their commitments to funds; they cannot time when commitments are called or when investments are exited.”

“Diverse Risk Preferences and Heterogeneous Expectations in an Asset Pricing Model”, by Gomez and Piccillo

SURPRISE
While this is a technical paper, it shows yet again how, even in the absence of any external shocks, internal (endogenous) factors can cause markets to operate and asset prices to exist far from equilibrium.

“We propose a heuristic switching model of an asset market where the agents’ choice of heuristic [decision rule] is consistent with their individual risk aversion. They choose between a fundamentalist and a trend-following [i.e., momentum] rule to form expectations about the price of a risky asset. Given their risk aversion, agents make a deterministic trade-off between mean and variance both in choosing a forecasting heuristic and determining the number of risky assets to buy.

“Heterogeneous risk preferences can lead to diverse choices of heuristic. Using empirical estimates for the distribution of risk aversion, simulations show that the resulting time-varying heterogeneity of expectations can give rise to chaotic dynamics: irregular booms and busts in the asset price without exogenous shocks. Small, stochastic price shocks lead to larger asset price bubbles, and can make stable solutions explosive.”

“Frenzy in Private Debt Pushes Assets Beyond $800 billion”, by Robin Wigglesworth, FT 1Mar20

“Private debt has become one of the hot topics for fund managers desperate to build a fee-rich asset class at a time when their traditional businesses are struggling. But even industry insiders are warning that the boom in private debt is turning into a frenzy.

“Once a niche area of the global asset management industry, assets invested in private debt — largely made up of non-bank loans to unlisted companies — reached a record $812bn in 2019, putting the market on track to break through the $1tn barrier within the next year…

“Private debt is moving into the mainstream as investors hunt for higher yield. Its growth has been spurred by banks quitting the market when they rationalised their loan books to meet tougher capital rules…

“Prequin data show that over the past four years 327 direct lending funds, the most common type of private credit strategy, have been raised, with about $207bn flowing into the strategies… Dry powder for all private credit, including strategies such as distressed and mezzanine debt in addition to direct lending, stands at $261 billion…

“Industry executives warn that the cracks now on show will become much worse when the market cycle turns.”

“Crowded Trades, Market Clustering, and Price Instability”, by van Kranlingen et al

“Crowded trades by similarly trading peers influence the dynamics of asset prices, possibly creating systemic risk…

“Market clustering, however, cannot be observed by individual investors and its effect on price dynamics can thus unfold unexpectedly…

“Market clustering is expected to cause price shocks, because it amplifies the effect of existing sources of price fluctuations.”

“Squaring Venture Capital Valuations with Reality” by Strebulaev and Gornall

The authors “develop a valuation model for venture capital–backed companies and apply it to 135 US unicorns, that is, private companies with reported valuations above $1 billion.” They find that reported unicorn post–money valuations average 48% above fair value, with 14 being more than 100% above.

“Wall Street banks ramp up research into quantum finance”, by Richard Waters, FT 5Jan20

“Some of Wall Street’s biggest banks have stepped up their research into quantum computing, signaling growing confidence that recent breakthroughs in the field have laid the foundation for the first practical applications of the revolutionary new computing technology…

“The banks’ research efforts centre on trying to design new types of algorithms capable of being run on quantum machines. The first of these involve a class of optimisation problems…

"Banks hope quantum machines will greatly reduce the time it takes to analyse complex risk positions, making it possible to adjust on the fly rather than relying on an overnight calculation…

“Further in the future, the banks also hope to use quantum computing to speed up the machine learning systems that lie at the heart of their push into artificial intelligence. That could make it possible to spot anomalies in markets more quickly, or identify opportunities that were not apparent at all before. However, that research has yet to begin and will require techniques that go well beyond optimization”.

“The Economic Effects of Private Equity Buyouts”, by Davis et al

The authors, “examine thousands of U.S. private equity (PE) buyouts from 1980 to 2013, a period that saw huge swings in credit market tightness and GDP growth. [Their] results show striking, systematic differences in the real-side effects of PE buyouts, depending on buyout type and external conditions...

“Employment at target firms shrinks 13% over two years in buyouts of publicly listed firms but expands 13% in buyouts of privately held firms, both relative to contemporaneous outcomes at control firms. Labor productivity rises 8% at targets over two years post buyout (again, relative to controls), with large gains for both public-to-private and private-to-private buyouts. Target productivity gains are larger yet for deals executed amidst tight credit conditions.”

“Zooming In on Equity Factor Crowding”, by Volpati et al

“Investors in a purportedly crowded strategy may face three related predicaments. One is that of increased competition for the same excess returns, leading to an erosion of the performance of the strategy…

“Second is increased transaction costs: maintaining similar portfolios leads to similar trade flows. This amplifies the effective market impact suffered by all investors following the same strategy – an effect called ‘co-impact’...

"This in turn leads to a deterioration of performance even under normal conditions...

“Finally, if the portfolios of different competitors largely overlap, systemic risk may arise as the liquidation of one of these portfolios can trigger further liquidations and even severe cascading losses for all investors who shared similar positions”…

“Crowding is most likely an important factor in the deterioration of strategy performance, the increase of trading costs and the development of systemic risk”…

The authors “identify significant signs of crowding in well-known equity signals, such as Fama-French factors and especially Momentum. We show that the rebalancing of a Momentum portfolio can explain between 1% to 2% of order flow, and that this percentage has been significantly increasing in recent years.”

“Structured Finance and Correlation Risk”, by Chesney et al

SURPRISE

The authors, “study the relation between the inherent complexity of structured products and their endogenous issuer margins. First, using a sample of 4,460 yield enhancement products (YEP), [they] document a shift towards more complex payoff structures. Margins for more complex products are twice as high relative to their less complex counterparts, while the former's realized investor returns are lower and negative on average…

[They] “identify uncompensated correlation risk as the main mechanism behind this discrepancy…[and find that investors] systematically underestimate the embedded correlation risk of more complex products. The resulting relative overpricing is increasing in the underlying volatility and in subjects' overconfidence.”

“ESG rating disagreement and stock returns”, by Gibson et al

SURPRISE

The authors find that rating providers in civil law countries “are more apt at identifying material social information” while rating providers in common law countries are better at identifying governance issues. They also find that “disagreement by such rating providers results in overvaluation and thus lower subsequent stock returns”.

“Associative Memory and Belief Formation”, by Enke et al

SURPRISE

One of the great mysteries of financial markets (and group dynamics more broadly) is why certain narratives persist, and even become more widely held, despite accumulating evidence that they are inaccurate. As we have noted in the past, part of the reason lies in human beings’ increased tendency towards conformity and copying the beliefs and behavior of others (“social learning”) when uncertainty increases. This new research highlights another reason that has been hardwired into us by evolution.

The authors experimentally study the role of associative memory (Kahneman’s System 1) for belief formation. “Real world information signals are often embedded in memorable contexts. Thus, today’s news, and the contexts they are embedded in, may cue the selective retrieval of similar past news and hence contribute to the widely documented pattern of expectation overreaction”.

The authors experimentally find support for this hypothesis. “Once today’s news is associated with the stories and images of previous opposite news, expectations systematically underreact. By exogenously manipulating the scope for imperfect and associative recall in our setup, we further provide direct causal evidence for the role of memory in belief formation and overreaction.”

“Investors grit their teeth for a ‘low return decade” by Robin Wiggleworth in the FT, 11Dec19

“Wall Street’s forecasts for the coming year make for fairly glum reading. But the real horror-show lies in the smattering of long-term forecasts, which indicate that the coming decade could be as terrible for investors as this one has been terrific…

“The price-to-earnings ratio for US stocks constructed by Nobel prize-winning economist Robert Shiller, which adjusts for economic cycles, has climbed back to 30 times — roughly twice its long-term average. Meanwhile, $11.6tn of bonds are trading at negative yields.
“I think this will be an abnormally low-return decade,” warned Andrew Sheets, chief cross-asset strategist at Morgan Stanley. “For bonds it’s just arithmetic, but for equities there are also valuation challenges…

“While such dour forecasts are sometimes dismissed as Cassandra-like — a reference to the soothsayer of Greek mythology — Mr Sheets noted that Cassandra’s prophesies were actually accurate. Her curse was never to be believed…

“The long-term estimates of GMO, the asset manager founded by Jeremy Grantham, are even more pessimistic. GMO forecasts US equities will on average lose 3.9 per cent a year in real, inflation adjusted terms over the next seven years, US bonds will shed 2.2 per cent, international equities will move sideways, and international fixed income — when the currency exposure is hedged — will drop 3.9 per cent, because the elevated cost of insuring against FX movements will erode the scant returns available…

"Most investors are more hopeful than their Wall St banks and fund managers. In a UBS survey of its clients, 69 per cent said they are optimistic about investment returns over the next decade.”

“Estimating the Anomaly Base Rate”, by Chinco et al

SURPRISE
“The academic literature literally contains hundreds of variables that seem to predict the cross-section of expected returns. This so-called ‘anomaly zoo’ has caused many to question whether researchers are using the right tests of statistical significance. But, here’s the thing: even if researchers use the right tests, they will still draw the wrong conclusions from their econometric analyses if they start out with the wrong priors—i.e., if they start out with incorrect beliefs about the ex ante probability of encountering a tradable anomaly. So, what are the right priors? What is the correct anomaly base rate?”

The authors conclude that it is ver low, noting that ”the anomaly zoo contains a few tradable anomalies and many more spurious predictors.”

“Fund Managers Push Against the Flow in Global Macro”, FT 9Dec19

“US asset manager Neuberger Berman on Monday announced the launch of a “macro opportunities” fund, focused purely on major currencies. Specialist currency manager Adrian Lee & Partners, which manages $14bn of assets, announced the launch of a global macro fund late last week. Both target high single-digit annual returns…

“The managers behind the new fund launches reported strong demand from institutional investors, especially given nerves that the long bull run in equities might be coming to an end.”

“Active Fixed Income Illusions”, by Brooks et al

SURPRISE
“Over the past 20 years, active fixed income (FI) managers have tended to deliver returns in excess of their benchmarks. This has generated a popular notion that active investing in fixed income markets is ‘easy’. Our aim is to assess the veracity of that notion. Across a broad set of popular active FI categories, we find that passive exposures to traditional risk premia (especially exposure to credit risk) explain the majority of FI manager active returns. The resulting implication is that, contrary to popular belief, traditional discretionary active FI strategies offer little in the way of true alpha, and that traditional active FI strategies may significantly reduce the strategic diversification benefit of FI as an asset class.”

“Chasing Your Own Tail Risk, Revisited”, by Thapar et al from AQR

“Credit Cycles and Asset Returns”, by Davis and Taylor

In the history of investing, some lessons get learned over and over, often as the result of painful experience.

”Investor experience and academic research since the Global Crisis reflects a growing realisation that credit conditions can affect future macroeconomic outcomes. This column investigates whether credit booms throughout history have had any explanatory power to account for future asset class returns. It finds that credit booms tend to systematically predict poor returns in the near future for equities in absolute terms, and relative to bonds. An investor who had tilted their portfolio allocations based on a credit boom signal would have been able to improve portfolio performance. The contribution of the credit boom signal is meaningful when compared to other well-established signals such as momentum and value.”

Russell Investments was put up for sale by its private equity owner.

A furtner indicator of the consolidation in the asset management industry that is being driven by the intensifying competition active managers face from indexed and algorithm based strategies.
See also, “How Quants and QE Shook the Cult of the Stockpicker”, FT 21Nov19 “Assets held in exchange traded funds surge to record $6tn, doubling in size in less than four years”, FT 30Nov19

“Beyond the Rubicon North American asset management in an era of unrelenting change”, by McKinsey & Company

“Any question as to whether North American asset management has undergone a fundamental phase shift should have been put to rest in 2018 (and 2019 thus far). The period served up a heady mix of macroeconomic shocks to the financial markets as well as changes in the industry, spurring revenue and profit pressure for firms across the sector.

“While average assets under management (AUM) in North America edged up nearly 7 percent for 2018 to $43 trillion, the industry’s aggregate revenue pool gained just 1 percent and, facing a rising cost bill, industry profits fell nearly 4 percent…

A set of now familiar industry forces continued to redraw the asset management landscape, and their impact was accelerated and intensified by the stresses of the macroeconomic environment.

“Six major themes played out in North America over the course of 2018: (1) An intensifying search for yield and diversification; (2) A continued challenge to active management in public markets; (3) A power shift in factor of distributors and intermediaries; (4) Emergence of new paradigms for pricing; (5) An untethering of costs from revenues; (6) Continued importance of scale and scope.”

Finally, at The Index Investor, we couldn’t help but note this observation by McKinsey: “Market reactions to macroeconomic shocks have elevated the importance of portfolio construction as a source of returns and resilience. Accordingly, investors have turned to [asset allocation] specialists.”

“Don’t Take Their Word For It: The Misclassification of Bond Mutual Funds”, by Chen et al

SUPRRISE
The authors “provide evidence that mutual fund managers misclassify their holdings, and that these misclassifications have a real and significant impact on investor capital flows. In particular, we provide the first systematic study of bond funds’ reported asset profiles to Morningstar against their actual portfolios. Many funds report more investment grade assets than are actually held in their portfolios, making these funds appear significantly less risky.

“This results in pervasive misclassifications across the universe of US fixed income mutual funds by Morningstar, who relies on these reported holdings.

"The problem is widespread- resulting in about 30% of funds being misclassified with safer profiles, when compared against their actual, publicly reported holdings.
“Misclassified funds” – i.e., those that hold risky bonds, but claim to hold safer bonds– outperform the actual low-risk funds in their peer groups.

“Misclassified funds” therefore receive higher Morningstar Ratings (significantly more Morningstar Stars) and higher investor flows due to this perceived outperformance. However, when we correctly classify them based on their actual risk, these funds are mediocre performers. Misreporting is stronger following several quarters of large negative returns.”

“Investment Funds Under Stress”, by Gourdel et all of the European Central Bank

SURPRISE
“This paper presents a model for stress testing investment funds, based on a broad worldwide sample of primary open-end equity and bond funds…

“Our results indicate that the impact of a global adverse macro-financial scenario leads to a median depletion in assets under management (AUM) of 24% and 5%, for euro area-domiciled equity and bond funds respectively, largely driven by valuation effects…Based on this, we estimate that 5.8% and 0.5% of euro area-domiciled equity and bond funds respectively could go into liquidation.”

“Bond Funds and Credit Risk”, by Choi et al

SUPRRISE
With so much below investment grade bond and loan debt held by ETFs and mutual funds that promise daily liquidity to investors, the authors describe what could, in the next downturn, become a key driver of debt deflation dynamics.

The authors “show that supply side effects arising from the bond holdings of open-end mutual funds affect corporate credit risk through a refinancing channel. In our framework, bond funds exposed to flow-performance relationships become excessively reluctant to refinance bonds of companies with poor cash flow prospects. This lowers refinancing prices, enhancing incentives for strategic default, thus engendering a positive association between bond funds’ presence and credit risk.”

“Will These 20’s Roar?” BCG’s 2019 Report on the Global Asset Management Industry

“The global asset management industry has reached a tipping point. In 2018, after roughly a decade of positive momentum, the industry hit a wall—as was sure to happen sooner or later—amid concerns over rising interest rates and a turn in the economic cycle…

“As we look toward the 2020s, we expect to encounter more market volatility, competition, and economic uncertainty…

“Among the key trends that we expect to see dominating the next few years are…The winner-takes-all phenomenon will accelerate as brand recognition, distribution dominance, and scale become ever more critical…Managers will have a hard time prospering if they don’t make step changes in their use of technology. Businesses across the industry view data and analytics as a route to sharper decision making, lower costs, and turbocharged performance…The problem is the high degree of uncertainty regarding the way forward”…

“Looking ahead, we expect to see an increasingly binary formula for success. The first option comprises boutique alpha shops—small, focused, nimble businesses that can achieve alpha by using capacity-constrained strategies.

“The second option lies at the opposite end of the scale: distribution powerhouses with more than $1 trillion in AUM that offer a full spectrum of products. We have doubts about the ability of firms in the middle to reinvent themselves to the degree necessary to create sustainable business models. All of them will have to evolve significantly to be successful, and some will not make it to 2030. There is also a wild card: the role of tech giants. If the likes of Amazon and Google step up, as their peers in China are starting to do, the disruption will be rapid and powerful.”

“Machine Learning in Financial Services” by The Bank of England

“Machine learning (ML) is the development of models for prediction and pattern recognition from data, with limited human intervention” …The promise of ML is to make financial services and markets more efficient, accessible and tailored to consumer needs. At the same time, existing risks may be amplified if governance and controls do not keep pace with technological developments …

"Firms validate ML applications before and after deployment. The most common validation methods are outcome-focused monitoring and testing against benchmarks. However, many firms note that ML validation frameworks still need to evolve in line with the nature, scale and complexity of ML applications…

"More broadly, ML also raises profound questions around the use of data, complexity of techniques and the automation of processes, systems and decision-making.”

“Wework, Neil Woodford and the Modern ‘Bezzle’”, by Merryn Somerset Webb, FT, 25Oct19

“In his 1955 book on the 1929 Wall Street crash, John Kenneth Galbraith suggested that “embezzlement is the most interesting of crimes”. It is, he said, the only one of the various forms of theft that comes with a time lag. So “weeks, months, or years may elapse between the commission of the crime and its discovery”.

“During that period things are good. The person who will eventually end up with the money knows he will have and keep it. Those who will lose it don’t yet know they will. They think it is permanent wealth — even if it is really what Galbraith referred to as “psychic wealth”. This happy period is the bezzle. All booms have it — the more relaxed and trusting everyone is and the more freely money is available, the more bezzle you get”…

“Our distorting easy-money environment can work to transfer large amounts of cash from ordinary savers to ridiculously overconfident individuals — a modern type of bezzle, the defining feature of which seems to be not so much theft from but lack of concern for the financial futures of those who support you.

“Set up a company, do amazing PR at a time when more money than sense is looking for a home (thank you loose monetary policy), expand in such a way that no one is much bothered by the impossibility of profitability, make the game last at least until you are rich, and you have a modern bezzle.”

“How Best To Annuitize Defined Contribution Assets?” by Munnell et al

Surprise
“Unlike defined benefit pensions that provide participants with steady benefits for as longas they live, 401(k) plans and Individual Retirement Accounts (IRAs) provide little guidance onhow to turn accumulated assets into income. As a result, retirees have to decide how much to withdraw each year and face the risk of either spending too quickly and outliving their resources or spending too conservatively and consuming too little. Surveys of individuals’ plans and several recent studies suggest that people will not draw down their accumulations for fear that they will exhaust their money and be unable to cover end-of-life health care costs. They also must consider how to invest their savings after retirement. These are difficult decisions.

“Better strategies are possible that will ensure a higher level of lifetime income, reduce the likelihood that people will outlive their resources, and alleviate some of the anxiety associated with post-retirement investing.

“Workers could use a portion of their 401(k) and IRA assets to purchase an immediate annuity that pays a fixed amount throughout their lives, typically starting at age 65. Or they could purchase an advanced life deferred annuity (ALDA) that requires a smaller share of accumulated assets and begins payments at a later age like 85.

“Alternatively, they could use their assets to delay claiming Social Security – essentially purchasing an inflation indexed annuity. Right now, none of these three options is commonly used. Very few workers choose to purchase immediate or deferred annuities (the first two options). And few retirees appear to be deferring claiming in order to receive the maximum annuity income from Social Security – most people simply retire earlier and claim immediately…

“Increasing annuitization in a meaningful way would require embedding annuities in 401(k) plans, with annuitization as the default…Moving forward would require some consensus about the appropriate share of 401(k) assets to be annuitized and the best method for annuitizing them.”

“Popularity: A Bridge Between Classical and Behavioral Finance”, by Ibbotson et al

Surprise
“Classical finance posits that all investors are rational and fully informed. This starting point seems to lead to a recommendation to index all assets, but that advice is not necessarily where it leads.

“Although most of classical finance focuses only on risk and expected return, investors differ in their tastes and preferences and assets differ in their characteristics other than risk and expected return…

“Active investment strategies could also work for behavioral reasons, in the sense of allowing for the possibility that not all investor preferences are rational or well-informed…

“The idea that the popularity of an asset affects its pricing, and ultimately its return, is not new but is often overlooked in the mathematics of asset pricing models...An asset could be liked (or disliked) for rational or irrational reasons. In this way, popularity spans ideas from both classical and behavioral finance, thus providing a bridge between the two camps…

“Assets are priced not only by their expected cash flows but also by the popularity of the other characteristics associated with the company or security. The less popular stocks have lower prices (relative to the expected discounted value of their cash flows), thus higher expected returns.”

“A simulation of the insurance industry: The problem of risk model homogeneity” by Heinrich et al

The authors “develop an agent-based simulation of the catastrophe insurance and reinsurance industry and use it to study the problem of risk model homogeneity.” Their “model simulates the balance sheets of insurance firms, who collect premiums from clients in return for ensuring them against intermittent, heavy-tailed risks. Firms manage their capital and pay dividends to their investors, and use either reinsurance contracts or cat bonds to hedge their tail risk…”

“Under Solvency II, insurance companies are required to use only certified risk models. This has led to a situation in which only a few firms provide risk models, creating a systemic fragility to the errors in these models… using too few models increases the risk of nonpayment and default while lowering profits for the industry as a whole. The presence of the reinsurance industry ameliorates the problem but does not remove it. These results suggest that it would be valuable for regulators to incentivize model diversity.”

“Private Equity Secondary Deals Soar”, FT 15Sep19

Highly leveraged private equity deals are often said to be “priced for perfection.” This is exponentially more so in the case of secondary sales, where one PE fund sells a portfolio company to another PE fund. As such, I have always regarded rising secondary PE sales as a leading indicator of an approaching market top.

As the FT notes, “the game of pass the parcel in the private equity industry is booming with secondary market activity — the buying and selling of assets before the end of a PE fund’s agreed term — running at record levels.

“Deals worth $42.1bn were completed in the first half of 2019 in the private equity secondary market, up by a third on the same period last year...This is a conservative estimate because the activities of sovereign wealth funds are excluded and it is difficult to capture all the deals done by the many opportunistic buyers that dip into the market…Competition is expected to intensify because there are about 43 secondary vehicles focused on private equity fundraising with a combined target of $72bn, according to Prequin, the data provider.”

“Leveraged Bank Loan versus High Yield Bond Mutual Funds”, by Ayelen Banegas and Jessica Goldenring from the Board of Governors of the Federal Reserve

Surprise
The authors describe a liquidity train wreck waiting to happen, with 60 percent of illiquid high yield bonds and loans held by retail funds that promise daily liquidity to their investors.

“Since the financial crisis, the markets for Bank Loan (BL) and High Yield Bond (HYB) mutual funds (MFs) have grown significantly, with assets under management increasing from $19 billion and $75 billion to close to $117 billion and $225 billion, respectively, as of December 2018…in terms of portfolio allocations, HYB and BL MFs hold around 60 percent of B, BB and BBB-rated assets…Net flows as a share of assets were larger and more volatile for BL MFs than for their HYB counterparts…”

Finally, "HYB MFs significantly outperformed BL MFs since early 2000.”

“Family Offices Prepare for Market Downturn”, FT 23Sep19

Historically, family offices, particularly in Europe, have been conservative investors whose portfolio shifts are often early indicators of future market turning points.

“UBS, the Swiss bank, and Campden Wealth, a data provider, surveyed 360 family offices and found that 55 per cent expected the global economy to sink into a recession before the end of 2020…”

“The 360 family offices generated an average return of 5.4 per cent over the 12 months ended May, weighed down by disappointing performances from their holdings of publicly traded equities in developed and emerging markets…Alternative investments, such as private equity, hedge funds and real estate, already account for about 40 per cent of the average family office portfolio, a significantly higher share than among public pension funds.”

“Allocations to alternatives are set to increase further. A net 39 per cent of respondents said they anticipated a rise in direct private equity investments in 2020 and a net 28 per cent expected to increase their exposure via private equity funds. Real estate also remains an attractive proposition for family offices with a net 16 per cent aiming to raise direct property holdings in 2020…

“The appeal of gold has also risen with a net 12 per cent expecting to increase their allocation to the precious metal next year.”

“Hedge funds, however, have continued to struggle to win new admirers among family offices. Allocations to hedge funds have been reduced for the past five years, [as some see them as] “relatively high [cost] when compared to their performance.”

“Thomas Cook’s collapse shows perils of debt derivatives”, FT 26Sep19

Surprise
The recent Thomas Cook bankruptcy, where credit derivative holders forced the company into bankruptcy instead of a restructuring, are the most recent example of why Warren Buffet memorably called credit them financial weapons of mass destruction. The more important lesson to be learned from the Cook saga, however, is that they are very likely to make debt problems and workouts triggered by the next economic downturn even more difficult and painful – and in the process, reinforce hostile popular and political animus towards financial elites.

“Equity Premium Puzzle or Faulty Economic Modelling?” by Shirvania et al

Surprise
This excellent paper is for everyone who at some point in the past 40 years has thought that their cost of equity estimate didn’t seem quite right.

The authors “revisit the equity premium puzzle reported in 1985 by Mehra and Prescott.” They “ show that the large equity premium that they report can be explained by choosing a more appropriate distribution for the return data, [and] demonstrate that the high-risk aversion value observed by Mehra and Prescott may be attributable to the problem of fitting a proper distribution to the historical returns and partly caused by poorly fitting the tail of the return distribution.”


They “describe a new distribution that better fits the return distribution and when used to describe historical returns can explain the large equity risk premium and thereby explains the puzzle.”

“The Performance of Exchange-Traded Funds” by Blitz and Vidojevic

At The Index Investor, we have long distinguished between funds that track narrowly defined indices, and ones that track broad asset class indices. The former are something akin to wolves in sheeps’ clothing, as they give one the illusion of being a passive investor while the product itself is actually a less expensive version of an active strategy. This new paper provides further evidence this is indeed the case.

The authors observe that, “exchange-traded funds (ETFs) are commonly regarded as an efficient, low-cost alternative to actively managed mutual funds, yet their perceived superiority is largely anecdotal. [They] evaluate the performance of a comprehensive, survivorship bias-free sample of US equity ETFs following the same approach that has been commonly used to evaluate the performance of actively managed mutual funds.”

They ”find that ETFs have collectively lagged the market by an amount that appears similar to the widely documented underperformance of active mutual funds” and conclude that “from a pure performance perspective, the allure of ETFs finds little support in the data.”

“Looking Under the Hood of Active Credit Managers”, by Palhares and Richardson from AQR

SURPRISE
The authors note that, “While systematic approaches to investing are commonplace in equity markets, until relatively recently, research exploring cross-sectional drivers of returns in fixed income markets had been limited, particularly so for corporate credit... We find that credit long/short managers tend to have high passive exposure to the credit risk premium. In contrast, we find that high-yield-focused long-only managers provide less exposure to the credit risk premium than their respective benchmarks. For both credit hedge funds and long-only credit mutual funds, we find that neither have economically meaningful exposures to well-compensated systematic factors [such as valuation, momentum, and carry].”

“Does Investing in Emerging Markets Still Make Sense?” by Jonathan Wheatley in the Financial Times

SURPRISE
“The prospect of fast economic growth has always gone hand in hand with political risk. But the basic calculations are changing for emerging markets as that growth potential dims — and with it, part of the core rationale for investing in the asset class.”

“Today, high commodity prices are a fading memory. Trade is stuttering and global supply chains are being disrupted [not the least by fast improving automation technologies, which reduce the attraction of labor cost arbitrage through offshoring production]. Far from catching up with the developed world, many supposedly emerging markets are growing more slowly. As globalisation risks going into reverse, many investors are asking what, if anything, will drive the asset class in future, raising questions over the role of emerging markets in a diversified portfolio.”

The obvious answer is increasing economic growth, driven by favorable demographic trends, and, above all, rising productivity. Yet more than ever before, increasing human capital quality (i.e., through better education) is the key, rather than the traditional route of providing workers with more capital. Alternatively, the latter could still be effective provided that trade patterns shifted towards a greater emphasis of exports and imports among emerging markets, rather than between them and developed countries. However, the prospects for these developments remain uncertain, and to a rising number of observers seem unlikely at this point.

“Dodging the Beta Bullet”, by Brad Zigler, on weathmanagement.com

In the model portfolios Index Investor created at the turn of the 21st century, we included two types of actively managed investment: equity market neutral and global macro funds. Our goal for both was alpha returns that had a very low correlation with the returns on the broad asset classes that comprised the rest of the portfolio. When it came to finding retail funds to implement these model portfolios, we found that true equity market neutral funds were rate. Zigler’s very useful article finds that they still are.

“Accelerating Learning in Active Management: The Alpha-Brier Process”, by Cerniglia and Tetlock

SURPRISE
This thought-provoking paper argues that the same process that outperformed national security intelligence analysts can be deployed to improve investment returns. As a veteran of Tetlock’s Good Judgment Project, I admit to being prejudiced; however, I still find the argument persuasive – indeed we use a variant of the recommended process here at the Index Investor.

“Who Benefits From Robo Investing?” by D’Hondt et al

SURPRISE
This is a fascinating paper for two number of reasons. The first is its micro-level analysis of the circumstances under which robo-investing benefits investors.

Closer to home, we found the discussion of the Robo’s performance around the 2008 crisis fascinating, as in 2007 The Index Investor also recommended a move into cash. Unfortunately, the paper does not make clear when the Robo made the same move.

“To assess the benefits of robo-investing we use a unique data set covering brokerage accounts for a large cross-section of 22,972 individual investors covering a sample from January 2003 to March 2012, and therefore includes the 2008 financial crisis. We have records of all trades, and in addition have detailed information about each individual investor's characteristics such as age, gender, education, annual net income, and most importantly, risk aversion assessed on the basis of responses to survey questions...

“We introduce the notion of AI AlterEgos, which are shadow robo-investors, to assess the benefits of robo-investing…The novelty of our approach is that we know what the investors have done in reality versus what a robo-investor would have done instead. In that sense our analysis is a real-time experiment with real data.

“We explore robo-investing strategies commonly used in the industry, including some involving advanced machine learning methods…“We consider three investment strategies. Two are based on a Markowitz (1952) mean-variance (MV) scheme and a third is based on the DeMiguel, Garlappi, and Uppal [equal weighting approach].

“The two MV strategies differ in terms of the sophistication regarding the conditional mean and variance estimates. The first involves two-year rolling sample estimates for both the mean and variance. For the second we rev up the robot engines and replace the rolling sample estimators by respectively expected return predictions using machine learning algorithms and sophisticated conditional covariance estimators…

“Finally, it is important to note that robo-investors have the option to hold cash, i.e. decide to avoid market risk exposure. No short selling is allowed…”

Robo-Portfolios are rebalanced monthly.

“The AI Alter Ego robo-investors using either equal weighting or rolling sample mean and variance estimates perform poorly and are of little value to any of our investors. In contrast the machine learning MV AI Alter Egos result in significant investment portfolio performance improvements for certain types of investors. In particular, those featuring high risk aversion benet greatly from following the robo-investor strategies. Low income (low education) investors typically also gain from the AI advice.

“These results confirm the claims made by practitioners in the industry regarding the promises the use of AI hold for the future of the FinTech industry. “More intriguing, and somewhat unexpected are our results pertaining to the performance during the financial crisis. Robo-investors outperform a large swath of investors. In fact, the median robo-investor moves into cash (because of negative expected returns using AI) whereas individuals exhibit behavioral biases, such as the disposition effect with unfortunate consequences during the onset of the financial crisis.

"As a by-product of our analysis, we also identify which machine learning methods perform well. While deep learning is often the best across a large cross-section of stocks, a close second-best is a much simpler linear prediction model with elastic net penalty based on the same set of predictors, namely those suggested by Welch and Goyal (2007), which consist of a mixture of firm-specific and macrocovariates. Put differently, the gains from using non-linear models are marginal at best.”

“What Drives Risk Perception? A Global Survey with Financial Professionals and Lay People”, by Holzmeister et al

SURPRISE
“Despite extensive research on decision-making under risk, little is known about how risks are actually perceived by nancial professionals, the key players in global financial markets. In a large-scale survey experiment with 2,213 nance professionals and 4,559 lay people in nine countries representing 50% of the world's population and more than 60% of the world's gross domestic product, we expose participants to return distributions with equal expected return and we systematically vary the distributions' next three higher moments.

“Of these, skewness is the only moment that systematically affects financial professionals' perception of financial risk. Strikingly, variance does not influence risk perception, even though return volatility is the most common risk measure in finance in both academia and the industry.

“When testing other, compound risk measures, the probability to experience losses is the strongest predictor of what is perceived as being risky.”

“Tomorrow's Fish and Chip Paper? Slowly incorporated News and the Cross-section of Stock Returns” by Tao et al

“A large literature debates the link between news and investor decision making. Relying on unique U.S. firm-level news data between 1979 and 2016, we document the cross-sectional difference in the speed of diffusion of the information contained in news.

“We distinguish news articles as being either slowly or quickly incorporated into stock prices. The return spread between these two types of news yields a statistically significant profitability (94 basis points per month) and this effect cannot be explained by other well-known risk factors. By employing novel attention data (Google Search Volume Index and Bloomberg News Readerships Index), we find that this news-induced anomaly can be attributed to limited-attention theory where firm-specific news is not read by investors.”

“Does Wealth Matter for Responsible Investment? Experimental Evidence on the Weighing of Financial and Moral Arguments”, by Doskeland and Pedersen

SURPRISE
“Responsible investment (RI) [i.e., ESG] is on the rise. RI refers to investments aiming to maximize risk-adjusted return while taking social, environmental, and moral concerns into account… both financial and moral objectives can be drivers of RI among individual investors…
"However, RI does not require moral concerns—investors can purchase such funds purely based on the belief that they will perform well, for conspicuous consumption reasons, and so on… There is scarce knowledge about the influence of investors’ wealth on their responsiveness to financial and moral arguments when investing responsibly…

“We conduct a large-scale natural field experiment on responsible investment, wherein we treat investors with financial, moral, and no arguments… Our study reveals a significant difference in the responsiveness to financial and moral arguments between investors of different wealth. The results are consistent throughout the decision-making process—for clicking (information search) and buying green funds (investment behavior). The difference is statistically and economically significant for both outcomes.

“That is, the financial treatment leads to more information search and more green buys, and the economic magnitude of those effects is substantial. Wealthy investors who are subject to financial rather than moral arguments click 29% more frequently for more information, while the difference is not significant for less wealthy investors. Furthermore, wealthy investors who are subject to financial rather than moral arguments invest in green funds 18% more frequently. Again, the difference is not significant for less wealthy investors.

“In more fine-grained analyses, we find that the difference with regard to wealth is particularly high among the wealthiest groups.”

“The Best of Strategies for the Worst of Times: Can Portfolios be Crisis Proofed?”, by Harvey, et al

This is an excellent overview of the different approaches to hedging an investor’s exposure to overvalued equities

“Do Measures of Risk Attitude in the Laboratory Predict Behavior under Risk in and outside of the Laboratory?” by Charness et al

In a finding that will come as no surprise to many practitioners, the authors conclude that, “measures of risk attitude are not related to risk-taking in the field, calling into question the methods currently used for the purpose of measuring actual risk preferences.”

“Evidence accumulation is biased by motivation: A computational account”, by Gesiarz et al

We cease collecting more information sooner when they indicate an outcome that we prefer, compared to situations when they don’t. Not only do we engage in motivated reasoning, we also engage in “motivated information collection.”

This is likely when people scoring high on tests of Active Open Minded Reasoning – i.e., people who actively seek information that contradicts their beliefs – are more accurate forecasters.

“The Expected Unexpected & Unexpected Unexpected”, by Quinn et al

This paper makes a critical point about a key forecasting challenge: Conceiving of scenarios that are substantially different from both the present what linearly extrapolated trends would be likely to produce in the future.

“The answers people give when asked to ‘think of the unexpected’ for everyday event scenarios appear to be more expected than unexpected. There are expected unexpected outcomes that closely adhere to the given information in a scenario, based on familiar disruptions and common plan failures.

“There are also unexpected unexpected outcomes that are more inventive, that depart from given information, adding new concepts/actions. However, people seem to tend to conceive of the unexpected as the former more than the latter.”

“Stop Worrying About Your Portfolio” by Ben Inker from GMO

Inker echoes a point that we have been making for 20 years at the Index Investor.

“Investors have a tendency to focus on the characteristics of their portfolios almost to the exclusion of other factors that will lead to success or failure for the larger objective that the portfolio is intended to serve. By taking into account the characteristics of the assets and liabilities that exist outside of their investment portfolios, they could build portfolios that are a better match for the true problem they should be solving.

“Because the liabilities and assets outside of the portfolios do not generally have quantitatively well-estimated characteristics the way that traditional investment assets do, this type of analysis necessarily involves a certain amount of judgment rather than simpler historical return analysis. But this effort seems well worth the attempt, because most apparently rigorous attempts to build “optimal” investment portfolios are solving the wrong problem for most investors.”

“The Sound of Many Funds Rebalancing” by Chinco and Fos

“Noise makes financial markets possible. But where exactly does noise come from? Research has pointed to several mechanisms. Early papers suggested that noise comes from random supply shocks, or from liquidity traders with random cash demands. There's also a lot of research into noise traders whose random demand stems from irrational beliefs. Other papers have modeled noise as the result of agents' need to hedge random endowment shocks…

“An overlooked source of noise is that in modern markets it is computationally infeasible to predict how even simple, rational trading rules interact to create net demand for a stock. For example, empirical data suggest that we can predict whether a stock will be affected by an exchange traded fund portfolio rebalancing cascade, but not how.”

“Alice’s Adventures in Factorland: Three Blunders That Plague Factor Investing” by Arnott et all

“Factor investing has failed to live up to its many promises. Its success is compromised by three problems that are often underappreciated by investors. First, many investors develop exaggerated expectations about factor performance as a result of data mining, crowding, unrealistic trading cost expectations, and other concerns. Second, for investors using naive risk management tools, factor returns can experience downside shocks far larger than would be expected. Finally, investors are often led to believe their factor portfolio is diversified. Diversification can vanish, however, in certain economic conditions, when factor returns become much more correlated…

“Factor investing is a powerful tool, but understanding the risks involved is essential before adopting this investment framework.”

“The Dynamics of Households’ Stock Market Beliefs” by von Gaudecker and Wogrolly

SURPRISE
This paper provides yet more evidence of how complexity and uncertainty (and lack of predictability) naturally arise n financial markets – in this case due to the interactions between investors who employ very different belief updating processes.

“We analyse a long panel of households’ stock market beliefs to gain insights into the nature of their expectations formation processes. We classify respondents into one of five groups based on their data and estimate group-wise models of expectations formation.

“Two of the groups are at opposite extremes in terms of optimism: Pessimists who expect substantially negative returns and financially sophisticated individuals whose expectations are close to the historical average.

“Two groups expect returns around zero and differ only in how they respond to information: Extrapolators who become more optimistic following positive information and mean-reverters for whom the opposite is the case.

“The final group is characterised by poor probability numeracy; its individuals are not willing or able to quantify their beliefs about future returns.

“None of the estimated belief formation processes passes a rational expectations test.”

“New Thinking is Needed as the Gloss Drips Off the Art Market”, by John Dizard

Dizard’s description of this market is too priceless not to share:

“The headline art market is more a high-end retail business that takes the form of a global series of cocktail parties. It is mostly run by three western auction houses, two Chinese auction houses, a dozen art fair promoters and a couple of hundred major dealers and consultants.

“These have a supporting cast of sycophants, publicists, “specialists”, security guards, party planners, removals companies, hired academics and journalists.

“The whole point is to tickle the enthusiasm and maintain the turnover of a floating crowd of a few hundred active collectors who require constant affirmation of their good taste and relative standing. Many of the collectors want to be dealers themselves or even raise their status to ‘museum founder’”.

“Long Term Economic Consequences of Hedge Fund Activist Interventions” by deHann et al

SURPRISE
Nearly 40 years ago, I was involved (as a banker) in my first LBO. Back then, a lot of companies and divisions thereof were inefficiently run, and many buyouts created substantial value. But that game didn’t last long; companies began to evaluate their cost structures through buyout funds’ eyes, and deal leverage ratios kept rising, with too many “priced for perfection” as we used to (and still do) say. Still some funds still created value by astutely timing market cycles, taking companies private at low points, sometimes completing strategic (usually cost driven) transactions before going public again at a higher price.

As this approach to value creation became more challenging (in no small part because of multiple bidders driving up buyout prices), there has arisen a new thesis: That buyout funds could not just cut cost and add leverage, but also materially improve revenue generation at their portfolio companies. In my personal experience, this has often proved far more difficult than expected, as fund analysts discovered that it is far easier to change a number on a spreadsheet than to navigate the messy process of actually making it happen in the real world.

With that in mind, I was reassured to discover this paper, and confirm that my personal experience were consistent with a much larger pattern.

The authors “examine the long-term effects of interventions by activist hedge funds.

“Research documents positive equal-weighted long-term returns and operating performance improvements following activist interventions, and typically conclude that activism is beneficial. We extend the literature in two ways.

"First, we find that equal-weighted long-term returns are driven by the smallest 20% of firms, with an average market value of $22 million. The larger 80% of firms experience insignificant negative long-term returns. On a value-weighted basis, which likely best gauges the effects on shareholder wealth and the economy, we find that pre- to post-activism long-term returns insignificantly differ from zero.

“For operating performance, we find that prior results are a manifestation of abnormal trends in pre-activism performance. Using an appropriately matched sample, we find no evidence of abnormal post-activism performance improvements. Overall, our results do not strongly support the hypothesis that activist interventions drive long-term benefits for the typical shareholder, nor do we find evidence of shareholder harm.”

“Stress Testing Networks: The Case of Central Counterparties” by Berner, Cecchetti, and Schoeholtz

As the Financial Times’ John Dizard has often warned, the authors of this paper conclude that, “the network created by central clearing can act as an important transmission mechanism for shocks emanating from Europe.

According to the Schroeders 2018 Investor Survey, on average, investors expect their portfolios to deliver annual returns of 9.9% over the next five years. Investors who consider their level of investment knowledge to be advanced/expert expect returns of 10.9% per year over the next five years

SURPRISE
The Schroeder’s estimate seems high, particularly given another piece of new data, the most recent estimate of equity market risk premiums used by global investors, based on a survey by Pablo Fernandez of IESE Business School. In the US, he found an average ERP of 5.6%, and median of 5.5%, roughly unchanged from 2015, despite rising valuation levels and an increasingly uncertainty economic outlook.

“Fact vs. Affect in the Telephone Game” by Brithaupt et al

As we have repeatedly mentioned, researchers have found that when uncertainty is high, human beings tend to conform to the views of their group, and rely more heavily on social learning/social copying and less on their own private and information when making decisions. Hence understanding the way that stories and narratives are socially transmitted is of great interest.

The authors find that, “When people retell stories, what guides their retelling? Most previous research on story retelling and story comprehension has focused on information accuracy as the key measure of stability in transmission. This paper suggests that there is a second, affective, dimension that provides stability for retellings, namely the audience affect of surprise. In a large-sample study with multiple iterations of retellings, we found evidence that people are quite accurate in preserving all degrees of surprise in serial reproduction –even when the event that produced the surprise in the original story is dropped or changed [in the process of retelling].”

This finding is consistent with the conclusion of an earlier paper, which found that, “when messages are propagated through diffusion chains, they tend to become shorter, gradually inaccurate, and increasingly dissimilar between chains. In contrast, however, the perception of risk is propagated with higher fidelity due to participants manipulating messages to fit their preconceptions, thereby influencing the judgments of subsequent participants” (“The Amplification of Risk in Experimental Diffusion Chains” by Moussaid et al)

“Estimating the Anomaly Base Rate” by Chinco et al

Today we are frequently confronted with critiques of the exploding number of factors that produce anomalies in asset returns compared to the traditional efficient market hypothesis, and further claims that these factors can be used to generate superior risk-adjusted returns.

From a Bayesian point of view, the true test of the claim that a new factor/anomaly has been discovered should go beyond the simple p-value (i.e., the likelihood that it is not just a random result), and also rest on the prior base rate for the discovery of anomalies in general.

Unfortunately, the latter is not an subject that has been much researched in financial economics. This paper finally does that, calculating the base rate for anomalies since 1973. While technical, it is well worth a read both by active managers seeking to discover and exploit factors, and by index managers who seek to replicate them.

“Liquidity Risk after 20 Years” by Pastor and Stambaugh

The authors note the successful replications of their original findings about the existence of a liquidity risk premium. They also note how liquidity risk premiums have increased in recent years. This aligns with multiple articles over the years that have claimed that different market developments have negatively affected market liquidity, including higher bank capital requirements, the rise of algorithmic trading across multiple locations, and the growth in value of ETF investments.

“Fundamental Trends in Dislocated Markets”, by Bakrania et al from AQR

The authors begin by noting two principles of AQR’s investment philosophy which align with our own (which in turn underlies the value provided by our global macro forecasts): that some types of information are only slowly incorporated into asset prices, and that those prices have a tendency to overshoot.

They then describe “two approaches to global macro investing: a systematic strategy focused on identifying fundamental trends and an opportunistic strategy capitalizing on extreme dislocations between prices and fundamentals. [They also] explore the potential benefits of combining these approaches into a single integrated macro strategy.”

Private equity once again in the news. First, some firms have launched so-called “super carry” funds in which managers obtain 30%, rather than 20% of profits above a threshold return (in addition to fund management fees). Second, as Robin Wigglesworth reported in the 15Apr19 Financial times, “Quant Funds Train Their Sights on Private Equity.”

For the better part of 20 years, The Index Investor has wrestled with and commented on the basic question of whether private equity offers a superior risk/return tradeoff to public market equities with similar characteristics.

Time has not changed our view that in most cases it does not, and should be avoided by most investors.

Having been “present at the creation” as it were, we are the first to admit that transactions by the original 1980s Leveraged Buyout funds created value, as the exploited a rich set of targets with too high costs and too little leverage. Research also found that value creation by these funds also benefited from skill in choosing when to return companies the public market.

Over time, however, private equity investing has become a much, much more difficult game to win. Potential targets were run much more efficiently and leveraged up their balance sheets. That forced PE funds to attempt new games, such as sector rollups (which often created value via cost cuts and increased pricing power), and attempts to increase revenues by improving value propositions (which has been a much greater challenge). While today some PE funds are well known, the much larger number that failed to raise “Fund 2” are not.
According to Prequin, at the end of 2018, PE funds had $1.2 trillion in dry powder (uninvested capital), and these days too many PE deals are, as they say, priced to perfection, with highly leveraged balance sheets required to hit target returns on equity. However, servicing this large amount of debt depends on successfully hitting very aggressive operating targets in an economy that will likely face and extended downturn during the life of the fund. Moreover, too many PE “exit” transactions are now taking the form of “pass the potato” sales to other PE funds.

In our view, this chapter in PE history is very unlikely to end on a happy note for funds’ limited partners (and too many portfolio companies).

March saw more articles highlighting market structure and conduct issues that could rapidly generate non-linear negative effects in the case of a market downturn.

On 16Mar19, the FT’s Robin Wigglesworth titled his column, “Liquidity is the Scary Absentee in Stocks’ Rebound.”

He notes that, “Wall Street has long complained that liquidity has deteriorated across markets in recent years.” Perhaps his most worrisome observation referred back to a previous column he wrote on 28Feb19 (“Markets Must Adjust to a New Type of Sudden Shock”), in which he quoted Robert Hilman of Neuron Advisors, who has “calculated that between 1960 and 2015 there were 14 significant shocks, which he defines as one-day returns being five standard deviations away from the daily average return of the preceding 33 days…

“However, between 2016 and today there have been four such five-sigma “sudden shocks” in the S&P 500: the market turbulence triggered by the Brexit referendum in 2016, fears over rising US interest rates in the autumn of 2016, the “Volmageddon” blow-up of VIX funds in February 2018, and renewed concerns over US monetary policy last October… We have to go back to the 1940s to find a three-year period where there have been four shocks or more, according to Neuron.”

Why is this worrying? Because due to human beings tendency toward social learning/copying, particularly when uncertainty is high, the distribution of outcomes produced by complex adaptive systems is not normal/Gaussian; rather it follows a power law. Moreover, the distribution of returns also tends to be fractal (i.e., self-similar) over different time horizons. To use an analogy, back in 2006-2007, we observed a similar series of “small earthquakes” in different indices (e.g., credit default swaps) which indicated to us that dangerous pressures were building up within the global financial system, that at some point it would no longer be able to contain on a small scale. That led to our May 2007 warning and recommendation to move a substantial amount of assets into cash.

The FT’s John Dizard and Gillian Tett have repeatedly warned about another way that small shocks can rapidly generate substantial negative effects across global financial markets, via the exposure of centralized derivatives clearninghouses to failed margin calls, combined with the unclear division of responsibility between national and multinational regulators should such a crisis occur. See, for example, Dizard’s “A Clearinghouse Crisis will Pose a Particular Threat to Europe” (FT 28Feb19) and Tett’s “A Transatlantic Front Opens in the Brexit Battle Over Derivatives” (FT 20Mar19).

A final potential amplifier of small shocks is the dependence of global bank and non-bank financial institutions on dollar funding, which is higher now than it was before Lehman Brothers failed in 2008. In that case, the Federal Reserve functioned as the global dollar lender of last resort, by creating and then expanding currency swap lines with other central banks, so that they could provide dollar funding to their national banks. Whether those swap lines will be adequate, or whether an increasingly politicized Fed will be able to act quickly enough the next time in a much more contentious international environment is a critical uncertainty.

We also have a keen interest in new research on the impact of changes in perceived uncertainty, and how these translate into asset pricing effects.

Surprise
In “Ambiguity Aversion and the Variance Premium”, Miao et al from the Federal Reserve Bank of Atlanta find that about 96 percent of the average variance premium can be attributed to ambiguity aversion (to be clear, the authors are using ambiguity to refer to Knightian Uncertainty – situations where some combination of the range of possible future outcomes, their impact, and/or their likelihoods cannot be estimated). In general, people are more averse to ambiguity than to risk, and thus require a higher premium to bear exposure to the former than to the latter.

In “The Time Variation in Risk Appetite and Uncertainty”, Bekaert et al find that credit spreads and corporate bond price volatility are highly correlated with measures of time varying economic uncertainty (which is inversely correlated with subsequent demand growth), while the variance premium on equity is informative about the time varying price that investors require for bearing exposure to it.

In “ Deep Learning in Asset Pricing”, Chen et al use a combination of machine learning tools to estimate a Stochastic Discount Factor (i.e., pricing kernel) that can explain expected returns on all assets. For financial economics, the quest for the SDF has been akin to the search for the Holy Grail, and the authors have made very impressive progress.

However, for our purposes, what we found most interesting was that the authors’ solution not only required the inclusion of macroeconomic factors to accurately estimate the SDF and its complex dynamics over time, but also that the multiple macro time series first had to be transformed based on a deep low dimensional factor structure that described four distinct macro state space processes.

Or, in English: the authors found that there were four deep drivers of multiple macroeconomic time series data. The four drivers (which are statistical artifacts and don’t correspond to specific macro variables) visually vary over time (like business cycles), with two peaking during times of recessions. Intuitively, these seem likely to correspond to aggregate demand/supply conditions, the state of interest rates and the financial system, the extent of uncertainty (or its converse, confidence), and perhaps the rare disaster/catastrophe risk identified by Robert Barro’s research.

Two other recent papers confirmed what investment professionals already know (but too few investors have yet to realize).

In “Passive in Name Only: Delegated Management and ‘Index’ Investing” Adriana Robertson documents a long held and frequently repeated complaint from The Index Investor: Many “index” funds are low cost active wolves in sheeps’ clothing. Robertson notes the very narrow base of many indexes, and finds that “the overwhelming majority of the indices in [her] sample are used as a primary benchmark by only a single fund.” She concludes that the vast majority of “index” products are just another form of active management, delegated to the designer of the index rather than a traditional investment manager.

In “A Census of the Factor Zoo”, Harvey and Liu bluntly conclude that “the rate of factor production in the academic research is out of control.” They decry what they term “factor mining” and note that many of those discovered “are simply lucky findings.” As Robertson also does, Harvey and Liu note the investor protection issues raised by their findings, because too many “investors develop exaggerated expectations based on inflated backtested results and are then disappointed by

New publications from AQR (“Capital Market Assumptions for Major Asset Classes”) and GMO (“7 Year Asset Class Real Return Forecasts”) provide a quantitative picture of how much even some of the world’s smartest asset allocators can disagree about future asset class returns in today’s High Uncertainty Regime.

For example, AQR’s medium term compound real return forecast for US equities is 4.3%; GMO’s is (3.7%) for large cap equities and (0.5%) for small cap. For emerging markets, AQR’s estimate is 5.4%, while GMO’s is 3.8%.

For ten-year US Government bonds, AQR forecasts 0.8%, while GMO’s estimate is (0.8%).

AQR also has an interesting discussion on estimating medium returns on private equity investment, which they forecast to be 4.7%, net of fees. Whether a 0.3% premium over the forecast 4.3% return on public market equity is sufficient compensation for the additional risks of investing in private equity (e.g., illiquidity) is an interesting question to ponder, particularly since at the end of February, the spread between AAA rated corporate bonds and 10-year US Treasuries (another proxy for the liquidity premium) stood at 1.15%.

“Victim Characteristics of Investment Fraud” by Lee et al

SURPRISE
This is a fascinating paper for investors and their advisors.

“Investment fraud constitutes a major problem in the United States. While several studies have investigated various aspects of fraud, none have analyzed victim characteristics of investment fraud. This study posits five fraud languages that, when used by fraudsters, shutdown the perceived need to conduct due diligence in their victims…

Many fraudsters perpetuate an aura of perceived success on the part of their victims. This is often done through false account statements as well as a fabricated prospectus or other documents…

Perhaps one of the most powerful ways fraudsters bypass the need for due diligence is to project an air of familiarity in order to appear as a member of the prospect’s ‘in-group’…

When their legitimacy is challenged, fraudsters will appeal to authority, usually a government agency that has supposedly already ‘cleared’ or ‘checked out’ the fraudster or the investment scheme.

Sometimes, this is done indirectly by associating the fraud with an already established entity like an investment advisory or stock brokerage firm…

For many of these schemes, fraudsters convey a common, often charitable, goal to promote the interests and prosperity of a non-profit (usually a church) itself or the group’s members…

The fifth and final fraud language is framed authenticity. It is similar to the claim to authority language in the alignment with apparently legitimate institutions that are often regulated by appropriate authorities. The difference here is framed authenticity emphasizes the legitimate business with which the investment program (and the fraudster) are aligned.”

“Alice’s Adventures in Factorland: Three Blunders That Plague Factor Investing” by Arnott et al

Bob Arnott finds that, “factor investing has failed to live up to its many promises. Its success is compromised by three problems that are often underappreciated by investors. First, many investors develop exaggerated expectations about factor performance as a result of data mining, crowding, unrealistic trading cost expectations, and other concerns.

Second, for investors using naive risk management tools, factor returns can experience downside shocks far larger than would be expected.

Finally, investors are often led to believe their factor portfolio is diversified. Diversification can vanish, however, in certain economic conditions, when factor returns become much more correlated.

Factor investing is a powerful tool, but understanding the risks involved is essential before adopting this investment framework

“Sovereign Bonds Since Waterloo”, by Meyer et al

This paper is an excellent study of external sovereign bonds as an asset class.

The authors “compile a new database of 220,000 monthly prices of foreign-currency government bonds traded in London and New York between 1815 (the Battle of Waterloo) and 2016, covering 91 countries. Our main insight is that, as in equity markets, the returns on external sovereign bonds have been sufficiently high to compensate for risk. Real ex-post returns averaged 7% annually across two centuries, including default episodes, major wars, and global crises…

“The observed returns are hard to reconcile with canonical theoretical models and with the degree of credit risk in this market, as measured by historical default and recovery rates. Based on our archive of more than 300 sovereign debt restructurings since 1815, we show that full repudiation is rare; the median haircut is below 50%.”

“Measuring Risk Preferences and Asset-Allocation Decisions: A Global Survey Analysis”, by Lo et al

Advisors, please note this quote:

“Overall, our findings suggest that financial advisors are of direct benefit to most individual investors…

“We use a global survey of over 22,400 individual investors, 4,892 financial advisors, and 2,060 institutional investors between 2015 and 2017 to elicit their asset allocation behavior and risk preferences. We find substantially different behavior among these three groups of market participants…

“Most institutional investors exhibit highly contrarian reactions to past returns in their equity allocations. Financial advisors are also mostly contrarian; a few of them demonstrate passive behavior. However, individual investors tend to extrapolate past performance…

Our results have another important implication, one that arises from the differences in responses between financial advisors and individual investors. We find that advisors generally advise their clients to change their allocation in the opposite direction of the typical preference of the individual investor. It may be that advisors recognize the excessive tendency of investors toward extrapolation and try to mitigate this effect by giving contrarian advice…

“Overall, our findings suggest that financial advisors are of direct benefit to most individual investors…

“We compare risk aversion across the three groups...Individual investors are significantly more risk averse than financial advisors, who are in turn more risk averse than institutional investors.”

“Selling Fast and Buying Slow: Heuristics and Trading Performance of Institutional Investors”,by Akepanidatawarn et al

SURPRISE
“Most research on heuristics and biases in financial decision-making has focused on non-experts, such as retail investors who hold modest portfolios. We use a unique data set to show that financial market experts ( institutional investors with portfolios averaging $573 million) exhibit costly, systematic biases.

A striking finding emerges: while investors display clear skill in buying, their selling decisions underperform substantially (even relative to strategies involving no skill such as randomly selling existing positions) in terms of both benchmark-adjusted and risk-adjusted returns…

“We present evidence consistent with limited attention as a key driver of this discrepancy, with investors devoting more attentional resources to buy decisions than sell decisions.”

“Who is on the Other Side?” by Mike Mauboussin

This is a very thorough and interesting overview of many inefficiencies in financial markets that theoretically create the opportunity for successful active management (i.e., positive alpha after fees and execution costs).

Unfortunately, in reality they have repeatedly been proven to be extremely difficult to consistently seize.

A number of new papers have shed more light on the genetic roots of risk tolerance and risky behaviors

SURPRISE
To the extent that they are genetically based (as opposed to learned or situationally based), differences in risk tolerance are likely to be impervious to attempts to modify them (e.g., through education). This suggests that financial advisors (and regulators) should instead focus on ways to compensate for them.

In “Genome-Wide Association Analyses of Risk Tolerance and Risky Behaviors in Over 1 Million Individuals Identify Hundreds of Loci and Shared Genetic Influences”, by Auton et al, the authors “find evidence of substantial shared genetic influences across general risk tolerance and risky behaviors in the driving, drinking, smoking, and sexual domains.”

Other research has found that impulsivity and risk tolerance are actually separate concept with different genetic and neurochemical roots (e.g., see, “Risk Taking and Impulsive Behaviour: Fundamental Discoveries, Theoretical Perspectives and Clinical Implications” by Isles et al).

In “Relationships Among Impulsive, Addictive and Sexual Tendencies and Behaviours: A Systematic Review of Experimental and Prospective Studies in Humans”, Leeman et al find that “generalized, self-reported impulsivity is a predictor of addictive and sexual behaviours at a wide range of severity.” Separate research has found that impulsive behavior also has a strong genetic component (e.g., see “Genetics of Impulsive Behavior” by Bevilacqua and Goldman).

In “Three Gaps and What They Mean for Risk Preference”, Hertwig et al note that risk tolerance measures based on self-reported preferences are more stable over time than those based on observed behaviors.” In our experience, this reflects the significant role that situational factors play in many decisions taken in the fact of risk, uncertainty, and ignorance.

The end of 2018 also saw the publication of a number of articles describing the increasing challenge to active management posed by algorithms.

The FT’s Robin Wigglesworth noted “a flurry of finger-pointing by humbled one-time masters of the universe, who argue that the swelling influence of computer-powered quantitative, or quant, investors and high-frequency traders is wreaking havoc on markets and rendering obsolete old-fashioned analysis and common sense” (“Volatility: how ‘algos’ changed the rhythm of the market”, FT 8Jan19).

A recent research paper highlighted the increasing efficiency that appears to have resulted from the deployment of algorithmic strategies in some markets. In their study of the forward exchange rate market between 1994 and 2016, Levich et al “find widespread evidence of excess-predictability, hence currency market inefficiency, in the early part of the sample period and then at specific times, such as the recent global financial crisis. In the more recent part of the sample period, the evidence of excess-predictability [i.e., inefficiency] is largely limited to emerging market currencies” (“Measuring Excess-Predictability of Asset Returns and Market Efficiency over Time”).

However, another paper found that systematic/algorithmic strategies do not yet deliver superior performance in more complex environments, such as global macro funds (see, “Systematic and Discretionary Hedge Funds: Classification and Performance Comparison” by Chuang and Kuan).

Another recent research paper highlighted the human biases that some algorithmic strategies seek to exploit: “Most research on heuristics and biases in financial decision-making has focused on non-experts, such as retail investors who hold modest portfolios. We use a unique data set to show that financial market experts (institutional investors with portfolios averaging $573 million) exhibit costly, systematic biases.

“A striking finding emerges: while investors display clear skill in buying, their selling decisions underperform substantially in terms of both benchmark-adjusted and risk-adjusted returns. We present evidence consistent with limited attention as a key driver of this discrepancy, with investors de-voting more attentional resources to buy decisions than sell decisions” (“Selling Fast and Buying Slow: Heuristics and Trading Performance of Institutional Investors” by Klakow Akepanidtaworn et al).

Along with many others, we mourn the passing of Jack Bogle, who encouraged us when we launched The Index Investor back in 1997.

In all the coverage of his accomplishments, a key issue, that we know bothered Bogle deeply, has received far too little attention: the difference between index investing and passive investment.

Bogle was suspicious of exchange traded funds from the moment they were launched, fearing that over time they would grow in number, be based on ever narrower indexes, and encourage investors to frequently trade. In short, he feared that ETFs would become the antithesis of long term passive investing in a diversified portfolio of mutual funds that are based on broad asset class indexes. His fears were sadly prescient.

For example, a recent article (“Index Funds Are King, But Some Indexers Are Passive-Aggressive”, by Peter Coy on Bloomberg, 24Jan19), and a recent academic paper (“The Active World of Passive Investing”, by Easley et al) both note that the majority of ETF products amount to lower cost active strategies based on indexes that track a wide range of sectors, regions, styles, factors, and investment themes. Picking narrowly defined index products instead of individual stocks or bonds or commodities does not make one a passive investor.

To be sure, there will always be an active part of investing, whether at the level of investment policy formulation (e.g., how much to save, when to retire, target bequests, etc.), asset class definition, portfolio construction, product selection and timing to implement portfolio strategy, and/or policy and portfolio risk management decisions.

However, for more than 20 years we have strongly supported Jack Bogle’s view that most investors can maximize the probability of achieving their goals by passively investing in a broadly diversified portfolio of broadly defined index funds. Our key additions to that philosophy is the recognition that avoiding large losses is critical to achieving long-term goals, particularly when markets can operate far from equilibrium. Wise investors therefore pay careful attention to both current asset class valuation metrics and the complex mix of macro forces that cause them to change over time.

“Perspectives on Today’s ‘Unconventional’ Portfolio Positions”. In this note to investors, GMO offered such a clear and succinct statement of its investment philosophy that it bears sharing here (it is also a view with which we strongly agree).

“Finance’s Lengthening Shadow”, by Nicole Gelinas in City Journal.

Gelinas offers a clear warning that nonbank (or “market-based”) lending is likely to play a significant role in our next financial crisis. This warning has recently become more acute, as investors have begun to flee riskier corporate debt markets.

“Banks remain hugely important, of course, but the potential for a sudden, 2008-like seizure in global credit markets increasingly lies beyond traditional banking…the financial system isn’t just banks. Over the last ten years, a plethora of “nonbank” lenders, or “shadow banks”—ranging from publicly traded investment funds that purchase debt to private equity firms loaning to companies for mergers or expansions—have expanded their presence in the financial system, and thus in the U.S. and global economies. Banks may have tighter lending standards today, but many of these other entities loosened them up. One consequence: despite a supposed crackdown on risky finance, American and global debt has climbed to an all time high…

“The ultimate cause of the [2008] crisis, however, wasn’t complex at all: a massive increase in debt, with too little capital behind it…”

As examples of market based lending vehicles that could present future systemic risks, Gelinas points to ETFs that invest in relatively illiquid bonds and bank loans, as well as private credit funds.

“Passive Attack: The Story of a Wall Street Revolution”, by Robin Wigglesworth in the 19Dec18 Financial Times

This is one of the best short histories of index investing — from how it began to what it has become — that I’ve read in 20+ years of being involved with this industry. Well worth a read.

At 632 pages, this is much longer than Wigglesworth’s short history of indexing, but equally rewarding, with 20 thought provoking (but often technical) articles for investors.

“Intelligence and You: A Guide for Policymakers”, by Brian Katz

As we have frequently noted over the years, there are far more similarities between investment management and intelligence analysis than both sides realize. With that in mind, Brian Katz’ article should be a very thought-provoking read for investment managers, who we are sure will come away more effective for having read it.

“Bogle Sounds a Warning on Index Funds”, Wall Street Journal, 29Nov18

SURPRISE

“The father of the index fund says it’s probably only a matter of time before they own half of all U.S. stocks; ‘I do not believe that such concentration would serve the national interest…

There no longer can be any doubt that the creation of the first index mutual fund was the most successful innovation—especially for investors—in modern financial history. The question we need to ask ourselves now is: What happens if it becomes too successful for its own good…

“If historical trends continue, a handful of giant institutional investors will one day hold voting control of virtually every large U.S. corporation. Public policy cannot ignore this growing dominance, and consider its impact on the financial markets, corporate governance, and regulation. These will be major issues in the coming era.”

“Beware of Gradual, then Sudden Fissures in Credit”, by Michael Mackenzie in the 30Nov18 Financial Times

“We are heading into a typical late-cycle period where the excesses of corporate borrowing come home to roost, an outcome that usually surprises many investors accustomed to the good times…there is a lot more credit exposure in the form of baskets such as exchange traded funds. As we have seen, this can exacerbate selling pressure across the broad credit market.”

“Against that dynamic what really worries many in the market is the expansion of triple B-rated debt, now running at $2.5tn, up from $670bn in 2008…What remains to be seen is whether private equity funds, which have become much bigger players in credit, stick to the long view and don’t join the rush to the exit.”

“Investors Start to Fret About Ballooning US Public Debt”, by Gillian Tett, in the Financial Times 8Nov18

“Complacent investors face prospect of a Minsky moment”, by John Plender in the Financial Times, 13Nov18

“Loss attitudes in the US Population” by Chapman et al

SURPRISE

“Base on a representative sample of the U.S. population (N = 2;000)…we find that around 50% of the U.S. population is loss tolerant. This is counter to earlier findings, which mostly come from lab/student samples, that a strong majority of participants are loss averse. Loss attitudes are correlated with cognitive ability: loss aversion is more prevalent in people with high cognitive ability, and loss tolerance is more common in those with low cognitive ability.

“The Current State of Quantitative Equity Investing” by Becker and Reinganum

An excellent overview that makes a critical point.

“The current approaches and products of quantitative equity investing stand on the shoulders of major theoretical and empirical contributions in financial economics. At the root of disciplined, modern investment processes are two intuitive concepts: risk and return. The notion of total return is obvious—price appreciation plus any dividend payments.”

“Risk is not so straightforward. Indeed, in Risk, Uncertainty, and Profit, Knight (1921) distinguished between risk and uncertainty. In essence, uncertainty involves environments in which investors cannot articulate potential outcomes or the likelihood of those outcomes. In contrast, risk is much more precise, like a roulette wheel. The possible outcomes are well specified and the likelihood of each outcome is known, but in advance, an investor does not know which outcome will be realized. Quantitative methods rely on this latter view of risk.”

At The Index Investor, our focus is instead on Knightian uncertainty that does not lend itself to easy quantification based on the frequency of historical events.

“Replicating Anomalies”, by Hou et al

As Stanford’s John Ioannidis has repeatedly shown in his research, replicating previous academic findings is a serious problem across the social sciences. This paper extends this analysis to previous investment research findings of different anomalies and claims that they can be exploited to generate alpha.

The authors find that, “most anomalies fail to hold up to currently acceptable standards for empirical finance.” They conclude that “capital markets are more efficient than previously recognized.”

“The Many Faces of Human Sociality: Uncovering the Distribution and Stability of Social Preferences”, by Adrian Bruhin

SURPRISE

This study presents interesting findings that divide people into three different categories of social preference, which remain stable over time.

“There is vast heterogeneity in the human willingness to weigh others’ interests in decision making. This heterogeneity concerns the motivational intricacies as well as the strength of other-regarding behaviors, and raises the question how one can parsimoniously model and characterize heterogeneity across several dimensions of social preferences while still being able to predict behavior over time and across situations…”

“We find that non-selfish preferences are the rule rather than the exception. Neither at the level of the representative agent nor when we allow for several preference types do purely selfish types emerge in our sample. Instead, three temporally stable and qualitatively different other-regarding types emerge without pre-specifying assumptions about the characteristics of types.”

“When ahead in a contest, all three types value others’ payoffs significantly more than when behind. The first type, which we denote as strongly altruistic type, is characterized by a relatively large weight on others’ payoffs – even when behind – and moderate levels of reciprocity.”

“The second type, denoted as moderately altruistic type, also puts positive weight on others’ payoff, yet at a considerable lower level, and displays no positive reciprocity.”

“The third type is averse to being behind, puts a large negative weight on others’ payoffs when behind, and behaves selfishly otherwise.”

“We also find that there is an unambiguous and temporally stable assignment of individuals to types.”

“Empirical Asset Pricing via Machine Learning” by Gu et al.

Excellent overview of the asset pricing accuracy of different ML techniques. Key insight: the best performing methods to a better job than traditional approaches of capturing non-linear interactions between key variables

“Index Proliferation Adds Choice But Fuels Confusion” by Pauline Skypla in the Financial Times

“A recent survey by the Index Industry Association revealed its 14 member companies publish 3.29m indices, of which 3.14m cover stock markets. Only 5.6 per cent of these 3.14m are factor or smart beta indices, which are based on factors other than companies’ market capitalisation. However, that modest percentage still works out at more than 175,000.”

“These measures may not all be investable indices — benchmarking, where investors use an index to assess their own performance, is also a driver of proliferation. Even so, the choice facing investors can be confusing. “The proliferation of indices, and the way providers calculate indices that sound the same differently, makes the job of investors more difficult,” says Deborah Fuhr, managing partner at ETFGI, a London-based consultancy. Problematically, there is no standard set. “Smart beta is a space that isn’t well defined or owned by a couple of index providers,” says Ms Fuhr.”

“A check of five well-known providers in the field (ERI Scientific Beta, Vanguard, State Street Global Advisors, FTSE Russell Global Factor Index Series, MSCI Factor Indexes) shows they all include the criteria of value, momentum and volatility that are among the top half dozen filters commonly applied to smart beta products.”

The above column highlighted the growing number of indexes that underlie so-called “smart beta” products. This brought to mind a number of previous papers on the smart-beta approach, which concluded that many investors were likely to be disappointed. These include Rob Arnott’s “How Can Smart Beta Go Horribly Wrong”, by Rob Arnott, “Quantifying Backtest Overfitting in Alternative Beta Strategies”, by Suhonen et al, and “Smart Beta Herding and Its Economic Risks: Riding the Dragon” by Krkoska and Schenk-Hoppé.

Since the smart beta products first appeared, The Index Investor, we have emphasized that they are active management products (see, The Confusing World of Factor (“Smart Beta”) Models and Indexes, from our August 2003 issue). While it is possible that they will return lower returns with less risk, or higher returns with more risk than a broad market index fund, a belief that they will produce higher returns with lower risk rests on three hiqhly questionable assumptions:

(1) The mispricing of factor risks that smart beta products claim to exploit is a durable phenomenon – e.g., one caused by investors’ systematic cognitive or emotional biases;

(2) There are durable barriers that prevent other investors (including algorithmically driven funds) from arbitraging away the mispricing of one or more factor risks that smart beta products exploit; and

(3) Investors are able to identify in advance smart-beta funds that are based on those factors to which assumptions (1) and (2) apply.
Unfortunately, once it becomes widely recognized, the failure of smart-beta funds to deliver superior returns is likely to further shake investor confidence in active management.

“It Was the Worst of Times: Diversification During a Century of Drawdowns” by AQR Capital Management

AQR highlights a critical distinction between diversification and hedging. The former involves investing in assets whose returns have a low correlation to equities. There is no guarantee that the returns on diversifying investments will be positive when those on equities are negative. And as we saw in 2008, correlations across asset classes can substantially increase during periods of extreme uncertainty and system stress.

In contrast, hedges are deliberately designed to increase in value when returns on equities decline –put options being the classic example.

However, for this very reason, hedging investments will tend to be more expensive than diversifying investments.

“Challenging the Conventional Wisdom on Active Management: A Review of the Past 20 Years of Academic Literature on Actively Managed Mutual Funds”, by Cremers et al

Cremers presents a good summary of arguments in favor of active management. At Index Investor, we have never denied that over some periods of time many active managers will outperform an appropriate passive benchmark index.

What we have always questioned, however, is (1) their ability to sustain that superior forecasting performance (or luck) over time; (2) their ability to identify and implement profitable investment opportunities as their funds grow in size; and (3) investors’ ability to identify these superior active managers in advance, rather than in hindsight. If you consider this a joint probability and assume that each of these probabilities is slightly better than luck – say, 55% -- then the joint probability – which essentially equals the probability of an active management strategy outperforming a passive strategy over the long term – is only equal to about 17% -- or a one in six chance.

“Private equity deals fail to keep up pre-crisis success” Financial Times 17Oct18

This column is a good example of the argument against active management outlined above.

“The proportion of winning private equity deals — those that deliver more than three times the original investment — has seen a sharp decline in the years since the financial crisis as buyout groups struggle with record-high valuations and fierce competition, an analysis has shown.”

“On average, 35 per cent of deals produced healthy returns between 2002 and 2005 compared to roughly 20 per cent of winning transactions between 2010 and 2013, an analysis by Cambridge Associates and Bain & Company showed.”

One week after the above story this one appeared: “Private equity set to surpass hedge funds in assets” Financial Times, 24Oct18

“Private equity will overtake hedge funds as the largest alternative asset class within the next five years as investors flock to private rather than public markets in search of returns, according to a new analysis.”

There are at least two possible explanations for this: (a) optimism, overconfidence, and conformity biases on the part of the institutional investors committing more funds to private equity in spite of declining recent returns; or (b) a rational decision to take on more risk in pursuit of higher returns, even though the probability of the latter being realized has significantly declined.

In the latter case, I have in mind the no-win situation faced by public sector pension funds in the United States, most of which are badly underfunded. Their managers must choose between hoping to reduce underfunding by earning high investment returns, or telling public sector employers that they must increase their annual pension fund contributions, which in turn will necessitate either cuts in spending in other areas, and/or an increase in taxes on the public.

Stories on 10th anniversary of the Lehman Bankruptcy

A recurring theme is how little has changed. While very aggressive monetary, and in some cases fiscal policy, staved off a severe and prolonged downturn, little was done to address the underlying causes of the 2008 crisis, which in some ways have arguably become worse over the past decade (e.g., debt levels, inequality, low productivity, corporate concentration, and declining labor share of national income).

“The Next Financial Crisis Won’t Come from a Known Unknown” by Robin Wigglesworth in the Financial Times

“No Deal Brexit has Big Implications for Europe’s Derivatives Market” by John Dizard in the Financial Times

“How Hedge Funds Keep Markets Trading in a Crunch” by Gillian Tett in the Financial Times

Surprise.

Wigglesworth highlights that in a complex adaptive system like the global financial markets, crises are most likely to emerge from unanticipated combinations of apparently benign factors. He also notes that since “high frequency trading, quants, passive funds, and options now account for about 90 percent of US equity trading volumes”, this structural change in the market is likely to rapidly accelerate, and potentially exponentially increase the damage caused by whatever combination of causes trigger the next global financial crisis.

Dizard suggests one potential cause that is easily overlooked – the post 2008 concentration of derivative trading in a small number of clearinghouses that lack sufficient capital to make good on a rapidly increasing number of failed trades, as might occur if the next crisis produced, as the last one did, an exponential increase in funding/liquidity problems – e.g., for leveraged hedge funds that, as Tett reports, have, since Dodd-Frank imposed higher limits on bank capital, become much more important sources of market liquidity than they were in 2008.

The FTSE All World ex US index results compared to US equity market returns. Through September, the rest of world is down (5.26%), while the US is up 8.96%. But gains in the US are narrowly concentrated: FTSE Health, up 16.1%; Consumer Services, 18.5%, and Technology, 19.1%

Narrow markets imply a high degree of social learning and imitation, which is a hallmark of situations characterized by high uncertainty and elevated potential for sudden and substantial changes and regime shifts.