RFS Executive Editor Blog

Trust, But Verify…in Venture Capital

trust-1418901_1280-pixabay[This is another of a series of editorials by Executive Editor Andrew Karolyi at the Review of Financial Studies featuring recently published papers at the journal. This editorial features “The Importance of Trust for Investment: Evidence from Venture Capital” by University of Bologna’s Laura Bottazzi, Tilburg’s Marco Da Rin, and Oxford’s Thomas Hellmann, an article in Issue 29(9) for September 2016. It was selected as an Editor’s Choice article on on the Oxford University Press web site for RFS.]

Former President Ronald Reagan’s quote about trust in diplomacy always resonates, but my own personal favorite is that of Ernest Hemingway: “The best way to find out if you can trust somebody is to trust them.” The salience of these quotes comes on the heels of the 2016 report of the Edelman Trust Barometer which, via a survey of 33,000+ respondents in 28 countries, tracks respondents’ trust in institutions, such as business, media, non-governmental organizations and government. Their report features many interesting facts and trends but each year the financial services sector reveals itself to be the least trusted at 51%. Edelman’s report states that “trust is too fragile and today’s financial services climate is too unpredictable for companies to rest on their laurels…the industry needs to continue to be dynamic and double-down on trust-building solutions.” Even more intriguing is that financial services features the largest and fast-accelerating trust “inequality” score, which is the gap in perceptions between what Edelman calls the informed public, who are college-educated and relatively wealthy, with the population at large.

The paper by Bottazzi, Da Rin, and Hellman is very timely in its examination of trust in venture capital, a segment of the financial services that is acutely dependent on it to make deals happen. What the paper does is connect a hand-collected dataset of European venture capital deals with the Eurobarometer measure of bilateral trust among those nations. The trust measure comes from several survey waves in the 1990s and it is based on a question that asks what percentage of citizens in one country trust a lot of people from another country. The paper’s theory predicts that earlier stage investment require more trust, that syndication is more valuable in low trust settings, and higher trust investors use more contingent contracts.

When I asked the authors what they believed were the takeaways they felt were at risk of being overlooked, they emphasized that generalized trust is based on stereotypical generalizations that are deep-rooted in historical patterns and what they really wanted to understand in this research initiative was whether trust among nations has any place in financial transactions by sophisticated professional investors, like venture capitalists (VCs). One might think that they would go beyond historical stereotypes and it turns out that they do not! A second thought they had concerned the relationship between trust and performance. Intuitively one might think that higher trust is associated with higher performance, but the papers finds the opposite. A positive correlation between trust and performance would make sense for ‘personalized trust,’ or the trust between specific individuals that are collaborating in some fashion. However, the analysis here is about what they call ‘generalized trust,’ which measures broader societal perceptions. The way to understand the negative correlation between trust and performance is then to look at the selection process, the willingness of investors in one country to place a bet on entrepreneurs in another country. The key insight is that venture capitalists apply a higher bar before investing in a relatively low trust country.

If generalized trust drives deal selection, as these authors teach us, and if it is true from Edelman’s Trust Barometer report that worldwide trust inequality in financial services is accelerating, I cannot help but wonder how much the future of the industry will be defined by the success of communication and engagement strategies for the general public, including for their employees as key advocates, to emphasize social purpose, and to contain strategies around data privacy and security, as well as financial education. This is relevant not just to the venture capital industry, but to the financial sector at large.

Ownership and Corporate Financial Policy: Separating Causation from Correlation

[This is another of a series of editorials by Executive Editor Andrew Karolyi at the Review of Financial Studies featuring recently published papers at the journal. This editorial features “The Effect of Institutional Ownership on Payout Policy: Evidence from Index Thresholds” by Rice University’s Alan Crane and James Weston and DePaul University’s Sébastien Michenaud, an article in Issue 29(6) for June 2016. It was selected as an Editor’s Choice article on on the Oxford University Press web site for RFS.]

school-916678_640-pixabay“Correlation is not causation” is a mantra drilled military-school style into every budding financial economist. I do not quite remember my graduate school days standing at a blackboard writing the four words repeatedly over and over, but it surely felt like it in retrospect. Now, I am not saying that every study in our midst is subject to potential criticism about endogeneity, reverse-causality, or omitted-variable problems, but sometimes, just sometimes, it’s worth pausing to recognize one that seems to have come upon a setting in which we have confidence in a robust causal finding.

Such is the case, I think, with the study by Crane, Michenaud, and Weston, which tries to break the pernicious, correlative connection between ownership decisions and corporate policy choices. They show that higher institutional ownership causes firms to pay more dividends. They force them to do so. And it is not a small effect. A one-percentage point increase in institutional ownership causes an 8% increase in dividends, worth about $7 million for the typical firm. The impact is even larger for firms that would be perceived to have higher expected agency costs. The authors follow up the headline findings with an analysis of shareholder proposals and voting patterns that suggest even non-activist, passive institutions play an important role in reducing wasteful spending that managers might otherwise allow.

mark-804936_640-pixabayWhat attracted me and likely others to the paper is the empirical strategy they invoke to break the reverse-correlation problem. They are the first to exploit a pseudo-experimental setting around mechanical definitions of the popular Russell 1000 and 2000 indexes to test their key hypothesis about institutional monitoring driving payout decisions. In a nutshell, the Russell indexes are formed each year (on May 31) on the basis of market capitalization rankings; the largest thousand form the Russell 1000 and the next two thousand, the Russell 2000. At the cutoff between the two, the differences in capitalization are a tiny fraction of the return variation that firms cannot possibly control, so their index assignment on one side or the other of the threshold is as good as random. The weights these stocks enjoy at the top of the Russell 2000 ledger are substantially higher than those at the bottom of the Russell 1000 given the money tracking them, which opens up the possibility for the discontinuity in weights to serve as an instrument for institutional ownership. Voilà! A quasi-natural experiment is had and causal-not-just-correlative findings ensue.

When I contacted the authors to ask them what they think readers may not fully appreciate about their paper’s findings or approach, they mentioned to me that their paper worked hard to unify a number of different econometric approaches to the use of the Russell index thresholds, one of which includes a paper by Ye-Cheng Chang, Harrison Hong, and Inessa Liskovich that the Review published last year (“Regression Discontinuity and the Price Effects of Stock Market Indexing,” January 2015, Volume 28(1)). In a carefully crafted internet appendix available on the Oxford University Press site for Review publications, Crane, Michenaud, and Weston replicate the results of other recent studies and test the sensitivity to methodological differences. They mentioned to me that “subtle biases and small variations in methodology can drive very different results for some outcome variables,” though their dividend result is robust across all approaches.

Diving into this kind of detailed supplementary note may be just the ticket for scholars who want to pursue this particular event involving exogenous variation in institutional ownership. It could be a template for exploring how other pseudo-experimental settings can be exploited to get traction with causal inference. One thing I am 100% certain of is that it is a more useful pursuit than repeating aimlessly a “correlation is not causation” mantra.

A New Look at the Financial Consequences of Economic Policy Uncertainty

[This is another of a series of editorials by Executive Editor Andrew Karolyi at the Review of Financial Studies featuring recently published papers at the journal. This editorial features “Policy Uncertainty and Corporate Investment” by Purdue University’s Huseyin Gulen and University of Arizona’s Mihai Ion, an article in Issue 29(3) for March 2016. It was selected as an Editor’s Choice article on the Oxford University Press web site for RFS.]

It is hard to travel in the academic circles of Finance without engaging in some impromptu discussion about the currently volatile political environment in the U.S. Whether the conversation revolves around the candidates, the polls, the calendar of primaries, or the “substance” of a recent debate, it seems to capture our collective fancy. Intriguingly, those among our colleagues who hail from other countries around the world seem to be as caught up in the coffee-break chat as any others. I cannot help but wonder about the opportunity cost of this attention and count in my head how many important research papers could be hatched and fostered in the absence of these distractions.

question-marks-2215_640-pixabaySlowdowns in research paper production are one thing, but delays in corporate investment activity are another. And this is precisely the question that a new article by Huseyin Gulen and Mihai Ion pursues. Their paper focuses on heightened policy uncertainty and how it is associated with significant delays in investments. The economic magnitudes are striking: on average two-year delays and as much as a one-third decline in corporate investments were observed during the recent financial crisis and aftermath. The authors emphasize that the effect is most acute among firms for which investments are likely to be more irreversible, sensibly anchored in real option theory, and which are more reliant on government spending.

One of the more intriguing aspects of their paper—equal in my mind with how they quantify the link between general policy uncertainty and investments—is how they operationalize various measures of economic policy uncertainty. This measurement business has been a fruitful area of research in macro-finance, applied econometrics, and even political science. The authors agree with me that this part of their paper, in fact, may be one of their most important contributions that readers risk overlooking. The bulk of their paper employs a measure of economic policy uncertainty developed by Scott Baker, Nick Bloom, and Steven Davis (BBD). BBD construct a policy-related economic uncertainty index from three components: one based on newspaper coverage of economic policies, a second based on federal tax code provisions set to expire in coming years, and a third based on disagreement among economic forecasters. Details are at www.policyuncertainty.com, where interested readers can download the data and read the latest 2015 working paper on which it is based. Sure, there are others to consider, such as the VXO index or an intriguing index built using factor-augmented Vector Autoregression techniques by Kyle Jurado, Sydney Ludvigson, and Serena Ng in the American Economic Review in 2015. But what Gulen and Ion show is how resilient BBD’s index is to the inclusion of many different controls for general economic uncertainty. It makes one wonder which component of the BBD index is the live one for investment activity.

A special part of the Gulen-Ion paper (again, that might be overlooked by some) is that the BBD index’s link to delayed corporate investment activity seems to belie political election cycle forces, another research stream in Finance and Economics that has received a healthy dose of attention. Sufficient variation seems to happen outside election years, be it when debt-ceiling debates arise or when other government budget crises ensue.

So, the next time you and your colleagues gather around the water cooler to deconstruct the content—or name-calling—of the primary debate of the prior evening, feel comfortable that the delays in corporate investments (and perhaps financial economic research activity) are likely no worse than usual.

Understanding the Mechanics of Contagion in Financial Markets

[This is another of a series of editorials by Executive Editor Andrew Karolyi at the Review of Financial Studies featuring recently published papers at the journal. This editorial features “Financing Constraints and the Amplification of Aggregate Downturns” by Daniel Carvalho of the University of Southern California, an article in Issue 28(9) for September 2015. It was selected as an Editor’s Choice article on the Oxford University Press web site for RFS.]

About a year ago, I posted a blog entitled “Wherefore art thou (corporate) peer?” in which I featured the work of Cecilia Bustamante, now of the University of Maryland, and her study on “Strategic Investment and Industry Risk Dynamics” in Issue 28(2), February 2015. I reflected on the large and growing literature on peer effects in Economics and the challenges of pinning down the positive/negative externalities among peers given correlated effects, Manski’s (1993) “reflection problem,” and such like. Cecilia’s study featured what I called a “twist” in its focus on firms’ strategic interactions with product-market peers.

below-257882_640-pixabayFast forward half a year and we find another fascinating study of industry peer effects, but this time focusing on financing, especially in market downturns. Daniel Carvalho’s paper identifies financial contagion effects during industry declines in which financially-constrained firms impose negative externalities on each other and in which the severity of the declines are significantly amplified. We have known about amplification effects from lots of theories in both Macroeconomics and Finance and they often feature the importance of financing frictions. The work of Ben Bernanke, Mark Gertler, and Simon Gilchrist in the 1990s is the reference point for many of us. Taking this kind of analysis to the industry level is what is novel toward pinning down the channels of influence with greater precision. Daniel’s work uncovers positive evidence on the mechanism through which these industry-specific amplification effects arise: the adverse impact of financially-constrained firms on the balance-sheets of their industry peers. When all firms in an industry face greater financing constraints, asset prices are lower, and firms have a harder time selling their assets to cover short-falls or borrowing against the collateral value of those assets.

When I asked Daniel to define the contribution of his work that many readers would miss, he emphasized the effects of financing frictions at the industry level. He pointed out that it is natural to think about general equilibrium effects as mitigating the importance of financing frictions at the aggregate level and not in exacerbating them. For him, it is all about how they can be downright destabilizing during industry downturns and across a range of industries. These externalities can lead to what Daniel calls “socially inefficient” corporate financial policies.

So, the most important takeaway here is the contrast between individual (firm) and aggregate (in this case, industry) effects of financial frictions and the need to incorporate these indirect contagion effects to get a clearer picture of what we observe in corporate finance research. Ironically, Cecilia Bustamante’s study of industry peer effects called for a revisionist perspective on portfolio sorting techniques for asset pricing tests defined by a firm’s relative position within its own industry.

Is there an echo in here?

Can We Always Invest Like Mr. Spock, and Not Homer Simpson?

[This is another of a series of editorials by Executive Editor Andrew Karolyi at the Review of Financial Studies featuring recently published papers at the journal. This editorial features “Confusion of Confusions: A Test of the Disposition Effect and Momentum” by The Ohio State University’s Justin Birru, an article in Issue 28(7) for July 2015. It was selected as an Editor’s Choice article on the Oxford University Press web site for RFS.]

Readers may recognize from my title the symbolism of investing like Mr. Spock versus Homer Simpson from the popular book by Cass Sunstein and Dick Thaler, Nudge (Penguin Books, 2009). In it, they describe how investors’ frequent lapses in judgment combined with herd mentality can reveal their inner “Homers” as opposed to choices of efficient-market-stleonard-nimoy-393861_640-pixabayyle “Econs” in the image of Mr. Spock from Star Trek. An April article in Barron’s featuring Thaler (designed to draw attention to his newest book, Misbehaving, May 2015, Norton) got my attention, particularly the discussion around one of the most common Homerian “D’ohs,” the disposition phenomenon. Per prospect theory, recall how people are more risk averse in the domain of stock gains than in the domain of stock losses where they are more risk-seeking, so they tend to hold to losers too long. [Yes, yes, I know that there is hardly a consensus in our literature on whether prospect theory actually predicts the disposition effect, thanks to Barberis and Xiong, Ben-David and Hirshleifer, and others.] The Barron’s article goes on to describe investment strategies at Thaler’s investment firm, Fuller & Thaler, and at JPMorgan’s $2 billion Undiscovered Managers Behavioral Value fund, all designed to capitalize on the disposition effect and other investor cognitive biases.

For such investors, the new article by Justin Birru of The Ohio State University in the July 2015 issue may offer a sobering reality check. The study uses investor-level data to re-examine the disposition effect specifically around a stock split. It’s an intriguing stock event that can serve as an optical illusion. Consider an example. For a stock undergoing a 3-for-2 stock split, an investor with an original purchase price of $20 should now realize that a stock price of $14 actually represents a gain rather than a loss, as the new reference price should be $13.33. What Birru finds is that the disposition effect breaks down following a stock split. In the period after a stock split, investors no longer realize gains at a rate any different than their losses – the winner/loser status of the stock is no longer significant for the selling decision. He suggests a bunch of candidate explanations. Maybe investors are inattentive to the split? Maybe they are unable or unwilling to properly update their reference price? Birru admitted to me in private communication that his paper never offers up a definitive answer here.

But that is just fine. Because this temporary breakdown provides a unique laboratory in which to investigate how and whether the disposition effect induces return predictability. That is the secret code that the Mr. Spock managers are trying to crack! Birru has the findings of prior studies looking at how the disposition effect can explain the momentum anomaly in his sights. Theory predicts that prices will revert to fundamental values in the absence of the disposition effect, so this should lead to a dissipation of momentum returns. It turns out that the momentum anomaly is alive and well in the post-split sample of stocks. He operationalizes the tests using a stock-specific capital gains variable built prior to the split event, which he uses to gauge whether the stock is above or below its fundamental value and with which he predicts their returns post-split.

What are the big picture takeaways? Birru writes to me that investors’ no longer behaving according to the disposition effect following a split is interesting in and of itself. But what he views as more important is that the setting can be exploited to shed light on the relationship between momentum and the disposition effect. Good to know this cognitive bias cannot be the primary driver of momentum returns. Maybe his effort will prompt scholars to explore other corporate events or capital market settings salient for other behavioral biases and the large array of anomalies to be pursued. One thing for sure is that Birru’s work will be a useful caution for our Mr. Spocks out there who seek to capitalize on the vulnerable Homer Simpsons.

Non-Marketable Assets and Capital Market Equilibrium – Redux

[This is another of a series of editorials by Executive Editor Andrew Karolyi at the Review of Financial Studies featuring recently published papers at the journal. This editorial features “Human Capital as an Asset Class Implications from a General Equilibrium Model” by Vanderbilt University’s Miguel Palacios. This paper is from Issue 28 (4) for April 2015. It was selected as an Editor’s Choice article on Oxford University Press web site for RFS.]

As young graduate students at the University of Chicago in the 1980s, I and my colleagues were strongly encouraged (compelled!) in Professor Eugene Fama’s asset-pricing course to read an article by David Mayers published in 1972. It was Dave’s thesis paper and was one of the contributions to the celebrated edited volume by Michael Jensen (Studies in the Theory of Capital Markets) that inspired a lot of modern finance. This excellent paper extended the classic CAPM model of capital market equilibrium of Sharpe (1964), Lintner (1965), Black (1972), and others to include nonmarketable assets such as human capital. The model adapted the expected return-risk relationship to redefine the benchmark model to include all marketable assets as well as the total payoff (income) on all non-marketable assets. And, of course, covariance risks – yes, now two – were accordingly redefined. The concept seemed very intuitive, but yet never really seemed to gain much traction in the empirical testing that followed. I was fortunate to be hired as a young assistant professor at Ohio State where Dave served as a senior colleague and I asked him once why the idea never gained more attention. The turning point, he argued, may have been a seminal 1977 study by Fama and Bill Schwert that collected income data from the U.S. Department of Commerce and that tested for – and comfortably rejected – the need for this extension for the cross-section of U.S. returns.

A number of years later, it seems Dave Mayers’ early work inspired another young graduate student at UC Berkeley, Miguel Palacios, who was intrigued enough (unlike yours truly!) to pursue the question further. And thank goodness for that–the fruits of his labor is celebrated in the form of a very nice contribution to the Review in its April 2015 issue. In the paper, Miguel derives the value and risk of aggregate human capital in a stochastic equilibrium model with Duffie-Epstein preferences (the continuous-time analogue of the more familiar Epstein-Zin preferences). Besides human capital’s value and risk, out of this comes a three-factor model including the market portfolio, the share of capital (relative to labor) and investment in human capital. The amazing statistic is that, upon calibration, the model estimates human capital to constitute a whopping 93% of aggregate wealth, well above what most previous studies have estimated. A second major finding is that human capital is a relatively safe investment, with attributes more like a bond than a stock. There are nuances about this last finding with respect to the horizon over which one judges it.

entrepreneur-1419389_640-pixabayWhen I called on Miguel to point out the most salient facts that readers should take away from his paper, he offered generously that his is not the first paper to assert the importance of human capital as a fraction of total wealth and that the portfolio choice literature has traditionally assumed human capital is safe. Not even the technique of calibrating a production-based asset-pricing model with an implicit valuation for human capital is novel.  What he argues is new is its focus on human capital in a comprehensive theoretical framework where the main economic factors determining its size and risk are part of the analysis. He sees the potential implications going beyond the identification of a three-factor model or of serving up the potential use of investment in education as a conditional variable in its testing. Focusing attention on the characteristics of the asset is the story of the hour.

I say we should welcome the return of human capital, if it ever left. It fits well with our discipline’s penchant for evaluating alternative multi-factor specifications for pricing, so welcome to the competition, human capital, and good luck to you. To the extent that it reflects back the cumulative investment in education we make (20 years before joining the workforce, 50 years of work after that, a substantial fraction of the population devoted to teaching, etc.), its magnitude may very well be as large as that in physical capital, so we should not ignore it. And, as big as human capital may be in countries like the U.S., I wonder about its potential importance in countries like China, India, Brazil, and other emerging markets around the world.

Wherefore Art Thou (Corporate) Peer?

business-894846_640-pixabay[This is another of a series of editorials by Executive Editor Andrew Karolyi at the Review of Financial Studies featuring forthcoming or recent papers at the journal. This editorial features “Strategic Investment and Industry Risk Dynamics” by Cecilia Bustamante of the University of Maryland and London School of Economics. It is the lead article in Issue 28 (2) for February 2015. It was selected as an Editor’s Choice article on the Oxford University Press web site for RFS.]

There is a large and growing literature on peer effects in Economics. Many scholars in Finance may have first encountered this intriguing research, like I did, in work on peer effects in the classroom. Are students “good” peers if they produce positive learning spillovers so that students exposed to them gain more for each investment in their education, or “bad” peers if they have the reverse effect? The challenge in this research is the problem of correlated effects. Are the externalities we observe true peer effects or endogenous social effects?, asks Charles Manski in his classic (1993) “reflection problem” study. Ever increasingly, Finance scholars are evaluating the potential existence and consequences of peer effects in corporate financial decision making.

Cecilia Bustamante’s paper falls into this line of inquiry, but with a twist. The paper focuses on firms’ strategic interactions with their product market peers. We are in an imperfectly competitive industry setting and the decision making is about the firm’s own investment strategy and that of its industry peers, an oligopolistic model of strategic capacity choice. Where is the twist? This joint dynamic of strategic interaction among firms studied in the industrial organization world becomes a tool to explain potential regularities in the cross-section of expected returns. Theoretical asset pricing studies regularly overlook the impact of firms’ strategic behavior on asset prices by focusing on monopolies or perfectly competitive industries.

So that becomes the key insight of the model Bustamante builds. A firm’s marginal product of capital or marginal q reflects a firm’s relative position with respect to its industry peers or its relative ability to increase its market share in the future. Its exposure to systematic risk is jointly affected by its own investment strategy and the investment strategy of its industry peers and the intensity of that interaction depends on the dispersion in the marginal q across firms within the industry and how that evolves over time. The testable prediction is that firms’ betas and returns correlate more in industries with low value spreads (book-to-market ratios) within the industry. And this is exactly what her empirical findings uncover.

What inspired Bustamante to pursue the question in the first place? She writes to me that she was inspired by a number of empirical studies that “cross-sectional regularities in returns are predominantly intra-industry, hinting at significant peer effects.” When I asked her about where these findings may take research in the future, she saw the potential to stir up new projects examining portfolio sorting techniques for asset pricing tests defined by a firm’s relative position within its own industry, whether size, market-to-book, gross profitability, investment-to-assets, or even other dimensions. Bustamante proposes future work to extend the analysis to more complex industry structures or to explore alternative intra-industry interactions along the supply chain.

The Curious Incident of the Absence of News…and How it Matters for Prices

[This is another of a series of editorials by Executive Editor Andrew Karolyi at the Review of Financial Studies featuring forthcoming or recent papers at the journal. This editorial features “No News is News: Do Markets Underreact to Nothing?” by the University of Chicago Booth’s Stefano Giglio and Kelly Shue, lead article in Issue 27 (12) for December 2014. It was selected as an Editor’s Choice article on the Oxford University Press web site for RFS.]

finance-462986_640-sherlock-pixabayThe 1892 book, The Memoirs of Sherlock Holmes by Sir Arthur Conan Doyle, is a collection of short stories, one of which entitled “Silver Blaze” is a mystery about the disappearance of a famous racehorse the night before a race and the murder of the horse’s trainer. Sherlock Holmes solves the mystery by recognizing that no one that he interviewed in his investigation remarked that they had heard barking from the watchdog during the night in question. The fact that the dog did not bark when it was expected to while the horse was stolen leads Holmes to conclude the perpetrator was not a stranger to the dog and thus would cause it not to bark. This “negative fact” cracked the case.

The opening of Giglio and Shue’s paper makes clear that the tale of “the dog that did not bark” indeed inspired them in part to investigate a novel question: does the absence of news reports and the passage of time contain important information for markets? Their paper offers up many possible settings in which one could explore the instance of no news and how economic agents might react to its non-existence, but they offer up the context of mergers. As Shue writes to me, “the merger context is great for this analysis because we can easily quantify the information content of the passage of time: the time elapsed after a merger announcement without completion or withdrawal offers information about the probability a merger will be completed.” The authors build a simple hazard rate function of the likelihood of completion in the next week conditional on its not having been completed or withdrawn to date. The implied hazard rate function forms a hump shape in a consistent matter across a large sample of U.S. merger deals over a 35-year horizon. The most intriguing part of the paper comes when the authors show that the implied hazard rates can predict weekly returns: the higher the probability of completion, the higher is also the average return.

The study takes yet one more turn in the mystery, making Sir Arthur very proud. A kind of mispricing is revealed. With the passage of time after merger announcement with hazard rates rising, the market seems to underestimate the probability of merger completion and positive surprises arise on average. As even more time passes and hazard rates start to fall, the market overestimates the probability of merger completion and negative surprises ensue. The intensity of this under- and over-reaction is greatest for illiquid stocks which may imply some kind of limits to arbitrage.

When I asked the authors to rationalize the big takeaway from their study, they offered that this evidence of mispricing to the passage of time is “suggestive of a more general phenomenon…it is less exciting than the new stories typically covered by the media.” They added that “underreaction to new news can potentially exacerbate asymmetric information problems in other contexts such as between voters and politicians, between managers and employees, or investors and insiders, where arbitrage is even more difficult.”

Tail Risk is Not Crash Risk…or Is It?

[This is the second of a new series of editorials by Executive Editor Andrew Karolyi at the Review of Financial Studies featuring forthcoming or recent papers at the journal. This editorial features “Tail Risk and Asset Prices,” by Chicago-Booth’s Bryan Kelly and Michigan State University’s Hao Jiang, lead article in Issue 27(10). It was selected as an Editor’s Choice article on the Oxford University Press web site for RFS.]

stock-exchange-1376107_640-pixabayThe global financial crisis called into question the ability of markets to deal with extreme events. As The Economist magazine put it a few years ago (“Fat-tail attraction,” March 24, 2011), “…tail-risk hedging was the talk of Wall Street in 2008 after global markets nosedived.” The key word here is after. The article points out how anxious investors tried to figure out how they could protect themselves from extreme or “black swan” events that arise outside the mid-range of the distribution of outcomes. The current (2014) unstable global market environment has surely revved up investor interest in uncovering such protections anew.

Excess tail risk is technically defined as a higher-than-expected likelihood of an investment position moving more than two or three standard deviations away from the mean. For many, tail risk simply means any large decline in a portfolio’s value. But a critical first step to hedging tail risk is to measure it. What Kelly and Jiang’s (2014) study proposes is an innovative measure of time-varying tail risk that is implied by the cross-section of stock returns. What they do is assess the magnitude of firm-level price declines every month to come up with a market-wide measure of common fluctuations in tail risk among individual stocks. They demonstrate how their measure is linked to traditional ones of tail risk extracted from equity index options, and how it moves inversely with real economic conditions. But what Kelly and Jiang ultimately seek out–and affirm–is an ability to forecast aggregate stock market returns while also capturing the cross-section of returns. Their back-tested portfolio that spreads U.S. equities on past tail-risk exposures delivers a juicy annual alpha of more than 5%.

Those statistics may capture active investor attention, but allow me to offer a few sobering thoughts the authors shared about their study’s most important findings. “I think it’s important to emphasize that our findings suggest that investors hedge against risk of an aggregate tail event…(and they are) not about stocks with higher crash risk having higher average returns,” states Bryan Kelly. In other words, they interpret their measure as an aggregate crash risk “state variable” contributing to fluctuations in investors’ marginal utility. This is why market participants will sacrifice a big chunk of their return on wealth to insulate themselves against a surge in crash risk. They also caution that their finding is “…not solely about return tails,” and show in their paper a significant correlation between return tails and cash-flow tails, which I think is very interesting. What is the link between tails on asset price returns and tails on the real side of firm behavior? One wonders how these concepts are connected to the surge of recent research on uncertainty shocks and disaster-risk shocks. The authors propose another intriguing connection may be to derivative pricing models that incorporate not only some measure of volatility as an observable state variable, but also build in a dynamic jump intensity. Which could be related to those volatility dynamics…or something else. Indeed, could Kelly-Jiang’s new measure of tail risk represent a useful observational process for jump-risk modeling in derivatives?

Finance’s Other Bosses?

man-209417_640-pixabay[This is the first of a new series of editorials by Executive Editor Andrew Karolyi at the Review of Financial Studies featuring forthcoming or recent papers at the journal. This editorial features “The Labor Market for Bankers and Regulators,” by The Wharton School’s Vincent Glode and the University of Washington’s Philip Bond, Issue 27(9). It was selected as an Editor’s Choice article on the Oxford University Press web site for RFS.]

A provocative article published in The Economist during 2010 with the same title as above asks, “Does it really matter who is in charge of the regulators? The grunt work of supervision depends on more junior staff, who will always struggle to keep tabs on smarter, better-paid types in the firms they regulate.” An implicit assumption here is that the people who staff financial regulatory agencies are less skilled than those they oversee. Worries abound as this assumption calls into question the effectiveness of financial regulation. Maybe the key to improving the functioning of financial markets is to mandate hiring better bosses at regulatory agencies, and giving better pay to those better bosses.

The goal of the Bond and Glode study is to explore the economic forces that might yield such an outcome. Their paper offers an elegant model of the interplay among bankers and regulators. The starting point is an assumption that regulatory jobs are preferable to banking jobs; individuals derive greater satisfaction from public service, or further, these jobs help individuals accumulate human capital more efficiently. The model shows us that bankers will be more skilled than regulators and that, because of compensating differentials, regulatory jobs will pay less. A banker’s compensation is also naturally more sensitive to job performance.  The “better” job gets the worse worker, and the regulatory jobs offer “safer” pay. The authors then build out the model dynamics to show intriguingly that, during financial booms, banks attract the best workers away from the regulatory sector. And this is the mechanism through which misbehavior in the marketplace might increase.

When I asked the authors to emphasize one salient point they felt readers might miss in the article, they pointed me to a nice example. “It is important to understand that the regulatory agency may never explicitly express a preference for low-skill workers,” Philip Bond suggests. “The forces described lead to a labor market equilibrium in which regulatory agencies offer $150K to workers and banks offer $600K to high-skill employees and $300K to low-skill employees…Confronted with these job terms, all high-skill workers view the financial cost of becoming a regulator as too large to bear, even though they would prefer to be regulators if pay were the same. Low-skill workers find the financial penalty acceptable and some of them become regulators.”

The most concerning social implications come through loud and clear in the conclusion of the study, which is well worth a close reading: increasing budgets of regulatory agencies will not eliminate the problem, Bond and Glode caution. Allocating more resources to regulatory agencies allows them to increase the quantity of supervision, but it is the less-skilled among the banker ranks who will be hired away from the private sector. This way, the skill inequality between bankers and regulators naturally persists. Worse yet, closing the revolving door that restricts regulators from switching to banking (a view that some in policy circles advocate) may inadvertently reduce the positive benefits of starting careers in regulation, making it less productive all around.