Eric N. Hughson

Intraday trade in dealership markets

We develop and test a structural asymmetric information transaction model to characterize the price impact of information when markets are thin. Since orders are accepted individually, the model allows for transaction costs and brokerage fees. Equilibrium demands mixed entry strategies on the part of potentially informed traders. Estimation of the structural parameters is performed using a maximum likelihood procedure on NYSE data. The structural model is rejected primarily because the nonlinear restrictions do not allow for sufficient correlation between price movements and pricing errors. This leads to unreasonably low estimates of the probability of informed trade relative to an unrestricted alternative. The price impact of information is found to be positive and significant, but economically small. This is because although the amount of private information is substantial, the quality of the information signals is poor, particularly in the middle of the trading day. Informed agents do not trade small quantities, which suggests that their ability to divide orders is limited by transaction costs.

The Misuse of Expected Returns

Much textbook emphasis is placed on the mathematical notion of expected return and its historical estimate via an arithmetic average of past returns. But those wanting to forecast a typical future cumulative return should be more interested in estimating the median future cumulative return than in estimating the mathematical expected cumulative return. For that purpose, continuous compounding of the mathematical expected log gross return is more relevant than ordinary compounding of the mathematical expected gross return. Self-test Pensions, endowments, and other long-term investors often want to forecast the future cumulative returns associated with various asset-class indices or investment strategies. Because no one can foretell the future, the future cumulative return is always a random variable that has a probability distribution. As a point forecast of the future cumulative return, some analysts have chosen to estimate the mathematical expectation of the future cumulative return’s distribution. We argue that this choice is misguided because the distribution of the future cumulative return is often heavily (positively) skewed. As a result, the mathematical expectation of its distribution is not as good a measure of its central tendency (i.e., what is more likely to happen) as is the median future cumulative return. The median future cumulative return has a 50 percent chance of being met or exceeded, but we show that the probability of meeting or exceeding the mathematical expectation approaches zero as the forecast horizon grows to infinity. As a result, even an accurate forecast of the mathematical expected future cumulative return is a badly overoptimistic forecast of what is likely to occur over long horizons. For example, our simulations indicate that there is only about a 30 percent probability of meeting or exceeding the mathematical expected future cumulative return of a large-capitalization stock index at the 30-year horizon that typifies retirement planning forecasts. We use a relatively recent result in the theory of statistics to argue that analysts who want to estimate the median future cumulative return should focus their attention on the mathematical expected logarithm of a single period’s gross return distribution. Continuously compounding the expected log gross return through T periods approximates the median future cumulative return at the T-period horizon. A simple point forecast of the median future cumulative return is made by (1) computing the average of the historical log gross returns (e.g., historical daily or monthly return data) in all past measurement periods and then (2) continuously compounding Step 1’s result up to the T-period forecast horizon. Substituting the average historical ordinary net return for the average historical log gross return in Step 1 is not recommended. Unfortunately, use of any historical average return is somewhat problematic, even in ideal statistical circumstances that may not characterize the real world. Even if the distribution of period log gross returns has been (and will remain) stable over time, the volatility of these log gross returns can make historical averages significantly different from future long-term averages. We show that typical stock index return volatility (15 percent) is enough to cause substantial fluctuation in historical averages. For example, even with 54 years of historical log gross return data, the fluctuation in future historical log gross return averages will be ±400 bps.

Strategic Mutual Fund Tournaments

This paper characterizes the optimal strategies of mutual fund managers competing in a multi-period winner-take-all tournament. Taking account of both multiple periods and competition between more than two managers, the optimal strategies are contingent on the state at the interim date. In the final period all managers maximize the amount of risk that they add to their portfolios with the exception of the leading fund. This fund locks in its advantage by reducing risk only if it has a sufficiently large lead. Empirically, we find that consistent with the theory, funds with larger leads decrease risk; however trailing funds do not increase risks. These results are robust to using different ways of controlling for systematic risk exposures.

A Simple-But-Powerful Test for Long-Run Event Studies

Testing for long-run abnormal performance has become an increasingly important part of the finance literature. We propose a test for abnormal performance in long-run event studies using the buy and hold abnormal return (BHAR). We augment the control firm approach of Barber and Lyon (1997) by using multiple control firms to create multiple correlated BHARs for each sample firm. Using the control firm structure allows us to avoid the new listing, rebalancing, and skewness biases. Further, despite the correlation amongst the BHARs, using multiple control firms allows us to increase the power of the test beyond that of existing tests. Finally, we show that our test is well-specified in both random and nonrandom samples.

A first look at Brexit and global equity markets

ABSTRACT Global equity markets fell by nearly 5% overall on 24 June 2016 following news of the Brexit referendum result. Although nearly all EU stock market indices experienced additional significantly negative abnormal returns, especially poor performance was registered by the debt-ridden PIIGS group (Portugal, Ireland, Italy, Greece and Spain). In this article, we identify a systematic tendency for more severe stock market responses to be concentrated amongst countries with higher debt to GDP ratios. This effect endures even after controlling for the degree of openness, EU membership and for being part of the PIIGS group.

Generational Asset Pricing, Equity Puzzles, and Cyclicality

To examine the potential role cohort preferences play in asset pricing cycles and puzzles, we consider a model with stochastic generational variation in preferences. In our structure, the pricing kernel reflects an investing generations consumption growth from mid-life to retirement rather than aggregate consumptions growth over the same time period. Generational domination of the pricing kernel provides insight into rationalizing three widely-recognized asset pricing puzzles and suggests one potential contributor to boom-bust patterns in stock market returns. (Copyright: Elsevier)

Can Boundedly Rational Agents Make Optimal Decisions? A Natural Experiment

The television game show The Price Is Right is used as a laboratory to test consistency of suboptimal behavior in an environment with substantial economic incentives. On the show, contestants compete sequentially in two closely related games. We document that contestants who use transparently suboptimal strategies in the objectively easier game use the optimal strategy almost all of the time in the game that is much more difficult to solve. Further, there is no consistency in the mistakes that are made in the two games. One cannot predict, conditional on play in one game, whether play in the other game will be optimal. The results have implications for the consistency of behaviorally based economic theory that relies on evidence derived in a laboratory setting.

A First Look at BREXIT and Global Equity Markets

Global equity markets experienced a significantly negative return of over 4.7 percent on June 24, 2016 in response to news of Britain’s decision (BREXIT) to leave the European Union. Stock market indices for members of the European Union were hard hit with nearly all experiencing significantly negative additional abnormal returns. Large economies such as Germany, Britain and France experienced abnormal returns of -3 to -4.2 percent while the so-called PIIGS: Portugal, Ireland, Italy, Greece, and Spain fared even worse, experiencing abnormal returns of up to negative 10 percent (Greece). We find positive abnormal returns in some BRICS nations.

Why Expertise Is Important for the Detection of Abnormal Performance The Hot Hand Strikes Back

The Sarbanes–Oxley Act emphasizes auditor and analyst independence as defining characteristics of quality financial reporting. We argue that this focus on independence may have adverse effects on auditor expertise. In particular, an independent auditor will lack firsthand knowledge regarding the construction of a client’s financial statements, knowledge that is obtained through the provision of a variety of non-audit services. This lack of firm-specific expertise introduces the potential for a bias analogous to that alleged in sports fans’ belief in the “hot hand” in athletic performance. In studies of professional sports, little evidence of serially abnormal performance has been documented by empirical researchers. As a result, many assert the absence of a hot hand in sports and conclude that athletic spectators’ beliefs and assertions thereof are not rational. We demonstrate that commonly used tests lack power specifically in the detection of abnormally elevated streaks in performance, even for large sample sizes. Importantly, in contrast to the usual non-structural (and non-parametric) approach, incorporating a structural-data-generating process dramatically enhances an observer’s ability to make the correct inference concerning the existence of a hot hand even if only a few observations are available. Of relevance to the debate regarding the (dis)advantages of independent auditors, we argue that auditor expertise regarding the underlying structure of the data-generating processes is paramount in increasing statistical power in (a) an auditor’s detection of anomalous corporate behavior and (b) inquiries by the Public Company Accounting Oversight Board (PCAOB) regarding auditor inference and potential corruption.

Counterparty Risk and the Establishment of the New York Stock Exchange Clearinghouse

Heightened counterparty risk during the recent financial crisis has raised questions about the role clearinghouses play in global financial stability. Empirical identification of the effect of centralized clearing on counterparty risk is challenging because of the co-incidence of macro-economic turbulence and the introduction of clearinghouses. We overcome these concerns by examining a novel historical experiment, the establishment of a clearinghouse on the New York Stock Exchange (NYSE) in 1892. During this period the largest NYSE stocks were also listed on the Consolidated Stock Exchange (CSE), which already had a clearinghouse. Using identical securities on the CSE as a control, we find that the introduction of clearing reduced annualized volatility of NYSE returns by 90-173bps and increased asset values. Prior to clearing, shocks to overnight lending rates reduced the value of stocks on the NYSE, relative to identical stocks on the CSE, but this was no longer true after the establishment of clearing. We also show that at least ½ of the average reduction in counterparty risk on the NYSE is driven by a reduction in contagion risk - the risk of a cascade of broker defaults. Our results indicate that clearing can cause a significant improvement in market stability and value through a reduction in network contagion and counterparty risk.