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RFS Executive Editor Blog

Non-Marketable Assets and Capital Market Equilibrium – Redux

by Andrew Karolyi • May 7, 2015

Human Capital as an Asset Class Implications from a General Equilibrium Model” by Miguel Palacios
Rev. Financ. Stud. (2015) 28 (4):978-1023. doi: 10.1093/rfs/hhu073

[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.

When 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.

The article is available free online here.


Wherefore Art Thou (Corporate) Peer?
by Andrew Karolyi • February 1, 2015

Strategic Investment and Industry Risk Dynamics” by M. Cecilia Bustamante
Rev. Financ. Stud. (2015) 28 (2):297-341. doi: 10.1093/rfs/hhu067

[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 article is available free online here.


The Curious Incident of the Absence of News…and How it Matters for Prices
by Andrew Karolyi • December 1, 2014

No News is News: Do Markets Underreact to Nothing?” by Stefano Giglio and Kelly Shue
Rev. Financ. Stud. (2014) 27 (12):389-3440. doi: 10.1093/rfs/hhu052

[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.]

The 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.”

The article is available free online here.


Tail Risk is Not Crash Risk…or Is It?
by Andrew Karolyi • September 26, 2014

Tail Risk and Asset Prices” by Bryan Kelly and Hao Jiang
Rev. Financ. Stud. (2014) 27 (10):2841-2871. doi: 10.1093/rfs/hhu039

[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.]

The 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?

The article is available free online here.


Finance’s Other Bosses? 
by Andrew Karolyi • August 27, 2014

The Labor Market for Bankers and Regulators” by Philip Bond and Vincent Glode
Rev. Financ. Stud. (2014) 27 (9):2539-2579. doi: 10.1093/rfs/hht132

[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.

The article is available free online here.