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

EML:  wwu@willamette.edu

"The lessons we just learned from the very recent credit crisis at the end of 2007 and beginning of 2008 are important, but they have been important for a long time. The recent crisis just elevates it. Corporate managers must understand the risks with any liability, after all, they factor it into their decision making. In the past, people really put their full faith on mathematical models. Most practitioners, including banks and some managers at corporations, ignored the limitations of the models and used them as they wished. They made a lot of mistakes. After the crisis, people started realizing the limitations of the models. People now interpret the models differently. They use more human judgement today."

Some people believe credit risk belongs to fixed income, in fact, credit risk has long been thought of as a sub category of fixed income. But now people are talking about credit risk in parallel to fixed income. Any company, including non-profit and government, issues a lot of debt, so they must understand the cost and benefits of it. For government and the public sector, it's even more important to understand the role of debt and the risk associated with debt.

I think fixed income is very important and actually understanding it is very important for the public sector too. As an example, you have micro finance, which is very popular today. It's where lenders make the smallest loans to extremely poor people who are entrepreneurial. You can make a $50 loan to someone in Bangladesh, you can charge a high-interest rate, and they're still likely to pay this back. Many banks are doing micro financing, because it's an emerging market, and it's an emerging market with good potential. A non-profit organization, however, in theory, cannot issue stock, because they cannot issue a dividend. (A stock is profit driven after all). We can basically classify non-profit like the public sector.

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.

Personal Interests
Professor Wu enjoys basketball, tennis, badminton and hiking in the Pacific Northwest. He also enjoys working on vehicles mechanically. 

Teaching: Credit risk, fixed income. Derivatives, risk management and macroeconomics and the financial system.

Research: Fixed income, derivatives, market micro structure and empirical asset pricing.  

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

“Valuing Temporary Hypergrowth” with Michael Dothan