WEI WU
Assistant Professor of Finance
B.A., University of Colorado
M.B.A., Rutgers University
Ph.D., Rutgers University

EML:  wwu@willamette.edu

Wei Wu is an assistant professor of finance at the Atkinson Graduate School of Management at Willamette University. Professor Wu presented his paper, “ Game-Theoretic Efficient-Market Hypotheses and Lead-Lag Effects in Stock Returns,” at the 2007 Financial Management Association conference and has delivered presentations at other major professional conferences and to organizations, including Bank of America. Wu was the recipient of the Fisher-Long-Whitcomb Teaching Excellence Award at Rutgers University in New Jersey in 2006. His main research areas include Credit Risk, Fixed Income, and Derivatives, with a growing interest in Market Microstructure and Asset Pricing.

Professor Wu received his Ph.D. in 2008 from Rutgers University and earned a B.A. in Economics from the University of Colorado in 2001. He is fluent in Chinese.

Research Interests: Fixed Income, Derivatives, Market Microstructure, Empirical Asset Pricing

Teaching Interests: Credit Risk, Fixed Income, Derivatives, Risk Management, Investments

2006 Fisher-Long-Whitcomb Teaching Excellence Award, Rutgers Business School

“Testing Lead-Lag Effects under Game-Theoretic Efficient Market Hypotheses,”with Glenn Shafer, submitted to Journal of Empirical Finance.

A game-theoretic efficient market hypothesis says that a trading strategy will not multiply the capital it risks substantially relative to a specified market index. This implies that the autocorrelation of returns relative to the index will be small and that a signal x will have approximately the same lead-lag effect on all traded securities. These predictions do not depend on assumptions about probabilities and preferences. Instead they rely on the game-theoretic framework introduced by Shafer and Vovk in 2001, which unifies statistical testing with the notion of a trading strategy that risks only a fixed capital. In this framework, we reject market efficiency at significance level when the capital risked is multiplied by 1/ or more. This approach identifies the same anomalies as the conventional approach: statistical significance for the autocorrelations of small-cap portfolios and equal-weighted indices, as well as for the ability of other portfolios to lead them. Because it bases statistical significance directly on trading strategies, the approach allows us to measure the degree of market friction needed to account for this statistical significance. We find that market frictions provide adequate explanation.

Research in Process

“Informed Trading and its Implications for Corporate Bond Pricing ” with Xing Zhou