Robert Stambaugh is the Miller Anderson & Sherrerd Professor of Finance at the Wharton School of the University of Pennsylvania. He is a Fellow and former President of the American Finance Association, a Fellow of the Financial Management Association, and a Research Associate of the National Bureau of Economic Research. Professor Stambaugh has been the editor of the Journal of Finance, an editor of the Review of Financial Studies, an associate editor of those journals as well as the Journal of Financial Economics, and a member of the first editorial committee of the Annual Review of Financial Economics. He has published articles on topics including return predictability, asset pricing tests, portfolio choice, parameter uncertainty, liquidity risk, volatility, performance evaluation, investor sentiment, and active-versus-passive investing. His research awards include a Smith-Breeden first prize for an article in the Journal of Finance as well as a Fama-DFA first prize and three second prizes for articles in the Journal of Financial Economics. Before joining Wharton in 1988, he was Professor of Finance at the University of Chicago, where he received his PhD in 1981. Professor Stambaugh visited Harvard University as a Marvin Bower Fellow in 1997-98.
Abstract: A pre-specified set of nine prominent U.S. equity return anomalies produce significant alphas in Canada, France, Germany, Japan, and the U.K. All of the anomalies are consistently significant across these five countries, whose developed stock markets afford the most extensive data. The anomalies remain significant even in a test that assumes their true alphas equal zero in the U.S. Consistent with the view that anomalies reflect mispricing, idiosyncratic volatility exhibits a strong negative relation to return among stocks that the anomalies collectively identify as overpriced, similar to results in the U.S.
Abstract: A four-factor model with two “mispricing” factors, in addition to market and size factors, accommodates a large set of anomalies better than notable four- and five-factor alternative models. Moreover, our size factor reveals a small-firm premium nearly twice usual estimates. The mispricing factors aggregate information across 11 prominent anomalies by averaging rankings within two clusters exhibiting the greatest co-movement in long-short returns. Investor sentiment predicts the mispricing factors, especially their short legs, consistent with a mispricing interpretation and the asymmetry in ease of buying versus shorting. Replacing book-to-market with a single composite mispricing factor produces a better-performing three-factor model.
Abstract: We derive equilibrium relations among active mutual funds' key characteristics: fund size, expense ratio, turnover, and portfolio liquidity. As our model predicts, funds with smaller size, higher expense ratios, and lower turnover hold less liquid portfolios. A portfolio's liquidity, a concept introduced here, depends not only on the liquidity of the portfolio's holdings but also on the portfolio's diversification. We derive simple, theoretically motivated measures of portfolio liquidity and diversification. Both measures have trended up over time. We also find larger funds are cheaper, funds trading less are larger and cheaper, and excessively large funds underperform, as our model predicts.
Abstract: Lower skill of the active management industry can imply greater fee revenue, value added, and investor performance. Such outcomes arise in a competitive equilibrium in which portfolio choices of active managers partially echo those of noise traders and also contain manager-specific noise. Both sources of noise reduce managers' skill to identify mispriced securities and thereby produce alpha. However, lower skill also means a given amount of active management corrects prices less and thus competes away less alpha. The latter effect can outweigh managers' poorer portfolio choices, so that investors rationally allocate more to active management when its skill is lower.
Abstract: We find that active mutual funds perform better after trading more. This time-series relation between a fund's turnover and its subsequent benchmark-adjusted return is especially strong for small, high-fee funds. These results are consistent with high-fee funds having greater skill to identify time-varying profit opportunities and with small funds being more able to exploit those opportunities. In addition to this novel evidence of managerial skill and fund-level decreasing returns to scale, we find evidence of industry-level decreasing returns: The positive turnover-performance relation weakens when funds act more in concert. We also identify a common component of fund trading that is correlated with mispricing proxies and helps predict fund returns.
Robert F. Stambaugh, Jianfeng Yu, Yu Yuan (2015), Arbitrage Asymmetry and the Idiosyncratic Volatility Puzzle, Journal of Finance, 70, pp. 1903-1948.
Abstract: We empirically analyze the nature of returns to scale in active mutual fund management. We find strong evidence of decreasing returns at the industry level. As the size of the active mutual fund industry increases, a fund?s ability to outperform passive benchmarks declines. At the fund level, all methods considered indicate decreasing returns, though estimates that avoid econometric biases are insignificant. We also find that the active management industry has become more skilled over time. This upward trend in skill coincides with industry growth, which precludes the skill improvement from boosting fund performance. Finally, we find that performance deteriorates over a typical fund?s lifetime. This result can also be explained by industry-level decreasing returns to scale.
Robert F. Stambaugh, Jianfeng Yu, Yu Yuan (2014), The Long of It: Odds That Investor Sentiment Spuriously Predicts Anomaly Returns, Journal of Financial Economics, 114, pp. 613-619.
Abstract: Extremely long odds accompany the chance that spurious-regression bias accounts for investor sentiment's observed role in stock-return anomalies. We replace investor sentiment with a simulated persistent series in regressions reported by Stambaugh, Yu, and Yuan (2012), who find higher long-short anomaly profits following high sentiment, due entirely to the short leg. Among 200 million simulated regressors, we find none that support those conclusions as strongly as investor sentiment. The key is consistency across anomalies. Obtaining just the predicted signs for the regression coefficients across the 11 anomalies examined in the above study occurs only once for every 43 simulated regressors.
Abstract: During the past few decades, the fraction of the equity market owned directly by individuals declined significantly. The same period witnessed investment trends that include the growth of indexing as well as shifts by active managers toward lower fees and more index-like investing. I develop an equilibrium model linking these investment trends to the decline in individual ownership, interpreting the latter as a reduction in noise trading. Active management corrects most noise-trader induced mispricing, and the fraction left uncorrected shrinks as noise traders' stake in the market declines. Less mispricing then dictates a smaller footprint for active management.
This course studies the concepts and evidence relevant to the management of investment portfolios. Topics include diversification, asset allocation, portfolio optimization, factor models, the relation between risk and return, trading, passive (e.g., index-fund) and active (e.g., hedge-fund, long-short) strategies, mutual funds, performance evaluation, long-horizon investing and simulation. The course deals very little with individual security valuation and discretionary investing (i.e., "equity research" or "stock picking").
This course studies the concepts and evidence relevant to the management of investment portfolios. Topics include diversification, asset allocation, portfolio optimization, factor models, the relation between risk and return, trading, passive (e.g., index-fund) and active (e.g., hedge-fund, long-short) strategies, mutual funds, perfermance evaluation, long-horizon investing and simulation. The course deals very little with individual security valuation and discretionary investing (i.e., "equity research" or "stock picking").
This course is an introduction to empirical methods commonly employed in finance. It provides the background for FNCE 934, Empirical Research in Finance. The course is organized around empirical papers with an emphasis on econometric methods. A heavy reliance will be placed on analysis of financial data.
This course has three main objectives: The first object is to introduce students to the fundamental works and the frontier of research in dynamic asset pricing. We will cover recent models that have been proposed to shed light on intreguing and important empirical patterns in the cross section and in the time series. Topics include non-separable utilities, market incompleteness, learning, uncertainty, differences of opionions, ex-ante and ex-post asymmetric information, ambiguity and Knightian uncertainty. The second objective is to teach students how to think of asset pricing research under a bigger or richer framework. We shall focus on the interactions between asset pricing and other fields such as macroeconomics, corporate finance, financial institutions, and international finance. The goal of inventigating the joint dynamics is not only to better understand how asset prices are determined, but also (maybe more importantly) how would asset pricing dynamics affect other important economic vaiables such as investment, corporate payout and financing, unemployment, risk sharing, and international capital flows. Students will learn production-based asset pricing models, particularly the asset pricing models with investment-specific technology shocks, risk shocks, financial friction, searching frictions and information frictions. Of course, the advanced solution methods will focus too. The third objective is to introduce advanced empirical methods to analyze the data and the quantitative dynamic models. It includes how to estimate structural dynamic models, how evaluate structural models beyond goodness-of-fit tests, how confront the models predictions with empirical data by simulation and re-sampling techniques, and how to efficiently test models and explore new patterns using asset pricing and macro data.
For hedge funds, poor performance, closures and large investor withdrawals are raising questions about their future. But don’t expect hedge funds to disappear anytime soon.Knowledge @ Wharton - 2015/11/6