Financial Markets and Investor Behavior Evidence File

May19: New Financial Markets and Investor Behavior: Indicators and Surprises
Why Is This Information Valuable?
“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
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
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.”

Apr19: New Financial Markets and Investor Behavior: Indicators and Surprises
Why Is This Information Valuable?
“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
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).

Mar19: New Financial Markets and Investor Behavior: Indicators and Surprises
Why Is This Information Valuable?
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.
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

Feb19: Financial Markets and Investor Behavior: Indicators and Surprises
Why Is This Information Valuable?
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
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
“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.

Jan19: Financial Markets and Investor Behavior: Indicators and Surprises
Why Is This Information Valuable?
A number of new papers have shed more light on the genetic roots of risk tolerance and risky behaviors
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.

Dec18: Financial Markets and Investor Behavior: Indicators and Surprises
Why Is This Information Valuable?
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).

Investing success comes from identifying a coherent and grounded investment philosophy, building a repeatable investment process and adhering to both of them. We seek to be long-term, long-horizon investors. Most capital that is invested institutionally is for long-term needs, yet unfortunately often acts with a short-term viewpoint. GMO’s objective is to buy assets that trade below their intrinsic value and let the force of mean reversion work on our behalf.

We believe investors should move their assets commensurate with the return opportunities that are presented to them, as well as the risks that are being underwritten. Valuation is a wonderful guide to do so. At times, we will look different as we believe that is the only way to outperform the crowd and avoid overpriced assets. The challenge is valuation offers limited insight on timing and requires patience as the investor may experience periods of protracted underperformance.

We believe that investors should focus on the risks that really matter – risks that can permanently impair their capital in the long run – such as buying overpriced assets. Many investors prefer the short-term comfort within the crowd and lose focus on what really matters. Valuation sensitive investing is hard because it takes time to work, it requires patience, and it often results in an unpopular portfolio.”

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.
“20 for Twenty: Selected Papers from AQR Capital Management on Its 20th Anniversary”
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.

Nov18: Financial Markets and Investor Behavior: Indicators and Surprises
Why Is This Information Valuable?
Bogle Sounds a Warning on Index Funds”, Wall Street Journal, 29Nov18

“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
“According to the Congressional Budget Office, the total annual cost of net interest payments on American debt in 2018 will be around $318bn. Right now, that sum seems manageable, relative to the overall American budget. But the CBO calculates that servicing costs will triple in size to nearly $1tn by 2028, on current policy trajectories and assuming that interest rates rise towards their long-term average of 3.7 per cent and 2.8 per cent for 10-year bonds and three-month bills respectively (or slightly above the current levels of 3.2 per cent and 2.34 per cent). If so, interest payments will soon become the third biggest item on the budget, eclipsing even military spending.

However, if interest rates rise faster than the CBO expects, the picture would be worse. For another striking feature of American debt is that its average maturity is only six years, shorter than most European countries. And during the Trump administration this maturity has —lamentably — shortened.”
Complacent investors face prospect of a Minsky moment”, by John Plender in the Financial Times, 13Nov18
Plender begins by highlighting “the debt-dependent nature of economic growth in the developed world.”

“Since 2008 debt has grown notably faster than nominal gross domestic product. This is most obviously the case in the US where public sector debt was on an unsustainable path even before Donald Trump introduced the first pro-cyclical fiscal expansion since Lyndon Johnson’s in the 1960s. Federal government debt is thus on a trajectory where the debt-to-GDP ratio could, according to the IMF, exceed 90 per cent by 2024. With a presidential election looming there is little likelihood Mr Trump will suddenly embrace fiscal orthodoxy…”

“Another important area of potential complacency relates to liquidity or the ability to deal without prompting adverse price movements. Regulatory curbs on proprietary trading in banks are clearly having an impact. So, too, are many structural changes in the markets including collective investment vehicles [e.g., government debt mutual funds and ETFs] that are assumed to be able to liquidate investments if investors seek to pull out in a troubled market.”

“[A further] difficulty is that the search for yield has pushed people into areas such as the corporate bond market that has never been particularly liquid … Liquidity is an elusive quality at the best of times. In a bear market it can disappear in a moment. Rest assured that not all of today’s trading strategies are predicated on that reality.”
Loss attitudes in the US Population” by Chapman et al

“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

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.”
Oct18: New Financial Markets and Investor Behavior Information: Indicators and Surprises
Why Is This Information Valuable?
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 successFinancial 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 assetsFinancial 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.

Sep18: New Financial Markets and Investor Behavior Information: Indicators and Surprises
Why Is This Information Valuable?
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

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.