Visiting Professor of Business Administration
Mark Seasholes conducts research in the field of financial economics, focusing on trading behavior and asset prices around the world. He has written on cross-border equity investments, herding behavior of individual investors, and loss aversion. Current work focuses on liquidity and asset pricing. One project looks at the systematic liquidity demands of individual investors. A second project studies NYSE specialist inventories (a measure of liquidity provided to the market).
His work experience includes a number of years on Wall Street and in the emerging markets of East/Central Europe. Mark was one of the first equity analysts in post-communist Poland. He has completed a valuation project in Honduras, helped with the Lloyds of London restructuring, and given a series of lectures in the People's Republic of China. Mark's experience also includes work with State Street Bank and Trust and their portfolio flow indices.
Professor Seasholes has teaching experience in a number of countries and cultures. He taught at U.C. Berkeley Haas School from 2000 to 2007 where he won teaching awards in three different programs. Other teaching experiences include London Business School, Hong Kong University of Science and Technology, Santa Clara University, and UT Austin. Visiting positions include INSEAD (France), Northwestern-Kellogg (USA), and University of Grenoble (France).
He received his BA from Wesleyan University where he graduated with high honors in physics, Phi Beta Kappa, and University Honors (the university's highest award.) University Honors was awarded to 5 of 650 graduates his year. He received his AM and PhD degrees from Harvard University.
Liquidity Provision and Stock Return Predictability
This paper examines the trading behavior of two groups of liquidity providers (specialists and competing market makers) using a six-year panel of NYSE data. Trades of each group are negatively correlated with contemporaneous price changes. To test for return predictability, we sort stocks into quintiles based on each group's past trades and then form long-short portfolios. Stocks most heavily bought have significantly
higher returns than stocks most heavily sold over the two weeks following a sort. Cross-sectional analysis shows smaller, more volatile, less actively traded, and less liquid stocks more often appear in the extreme quintiles. Time series analysis shows the long-short portfolio returns are positively correlated with a market-wide measure of liquidity. A double sort using past trades of specialists and competing
market makers produces a long-short portfolio that earns 88 basis points per week (act as complements). Finally, we identify a "chain" of liquidity provision. Designated market makers (NYSE specialists) initially trade against order flows and prices changes. Specialists later mean revert their inventories by trading with competing market makers who appear to spread trades over a number of days. Alternatively, specialists may trade with competing market makers who arrive to market with delay.
Investing in What You Know: The Case of Individual Investors and Local Stocks
This paper tests the performance of individuals' equity investments. We study over 40,000
accounts and 950,000 trades from a large discount broker. Individuals invest heavily in
local stocks and put 14% more into these stocks than a market-neutral portfolio would
suggest. Using holdings-based calendar-time portfolios, we find the local holdings do
not generate positive alphas. Using the transactions data, we find local stocks bought
actually underperform local stocks sold (though the underperformance is more severe
when considering remote stocks). We find no support for the folk wisdom that one should
"invest in what you know."
Risk and the Cross-Section of Stock Returns
This paper mathematically transforms unobservable rational expectation equilibrium model parameters (information precision and supply uncertainty) into a single variable that is correlated with expected returns and that can be estimated with recently observed data. Our variable can be used to explain the cross section of returns in theoretical, numerical, and empirical analyses. Using Center for Research in Security Prices data, we show that a −1σ to +1σ change in our variable is associated with a 0.31% difference in average returns the following month (equaling 3.78% per annum). The results are statistically significant at the 1% level. Our results remain economically and statistically significant after controlling for stocks' market capitalizations, book-to-market ratios, liquidities, and the probabilities of information-based trading.
Keywords: Risk premiums;
Cross-sectional asset pricing;
Risk and Uncertainty;
Trading Imbalances and the Law of One Price
We study trading and prices of Chinese (mainland)/Hong Kong dual-listed shares. Relative prices can diverge by a factor of two and exhibit significant variation over time. Order imbalances explain contemporaneous changes in relative prices at daily and weekly frequencies.
Keywords: Law of one price;
Balance and Stability;
Financial Services Industry;
Asset Price Dynamics with Limited Attention
This paper studies the role that limited attention and inefficient risk sharing play in stock price deviations from the efficient prices at horizons from one day to one month. We expand the Due (2010) slow-moving capital model to analyze multiple groups of investors who have varying levels of attention. We test the model's implications through an analysis of the joint dynamics of stock price movements and trading by the different types of investors. The model is consistent with contemporaneous, lead, and lag correlations among returns and trading at daily, weekly, biweekly, and monthly frequencies. We quantify limited attention's economic effects on asset prices by estimating a reduced form version of our model on New York Stock Exchange data. A one standard deviation change in market maker inventories is associated with transitory price movements of 65 basis points at a daily frequency and 159 basis points at a monthly frequency. 8% of a stock's daily idiosyncratic return variance and 25% of a its monthly idiosyncratic variance are due to transitory price changes (noise) and the trading variables explain 32% of this noise.
Keywords: transitory volatility;