Research Highlight: Qianqiu Liu

QIANQIU LIU

Professor Liu’s current research interests are in the areas of empirical asset pricing, financial econometrics, market microstructure, personal financial planning, and international finance. His research has been published (or forthcoming) in journals including Review of Financial Studies, Journal of Applied Econometrics, Journal of International Money and Finance, Journal of Banking and Finance, International Review of Finance, Financial Services Review, Annals of Economics and Finance, Journal of Investment Management, and Journal of Wealth Management. His research, “Reality Check:  The Implications of Applying Sustainable Withdrawal Rate Analysis to Real World Portfolios” (joint with Rosita Chang, Jack De Jong, and John Robinson), and “Are Lifecycle Funds Getting a Bum Rap?” (joint with Rosita Chang, Jack De Jong, and John Robinson), won 2008 and 2010 Academy of Financial Services (AFS) Best Paper Award, respectively. He received Shidler College’s Shirley M. Lee research award in 2009. His recent research, “Decomposition Short-term Return Reversal” (joint with Zhi Da, and Ernst Schaumburg) was selected as a semifinalist for the best paper in Investments at the 2010 FMA Annual Meeting. His papers have been cited by leading academic journals in Economics, Finance, and Management, such as American Economic Review, Journal of Econometrics, Journal of Financial Economics, Review of Financial Studies, Management Science, etc. 

In his paper,Return Reversals, Idiosyncratic Risk, and Expected Returns” (joint with Victor Huang, Ghon Rhee, and Liang Zhang, Review of Financial Studies, Vol. 23, No. 1, 2010, 147-168), Professor Liu and his coauthors investigate the relation between monthly idiosyncratic volatility and the cross-section of expected stock returns. They demonstrate that the omission of previous month stock returns can lead to a negatively biased estimate of the relation. Although a negative relation exists when monthly idiosyncratic volatility is estimated based on daily data, it disappears when short-term return reversals controlled for. In contrast, there is a robust and significantly positive relation between idiosyncratic risk and expected returns using monthly data. This paper solves an important puzzle and helps understanding the role of idiosyncratic risk in asset pricing models.

In his paper, “On Portfolio Optimization: How and When Do We Benefit from High-Frequency Data” (Journal of Applied Econometrics, Vol. 24, No. 4, 2009, 560-582), Professor Liu examines how the use of high-frequency (intraday) data impacts the portfolio optimization decision. He finds that the benefits of using high-frequency data depend upon the rebalancing frequency and estimation horizon. If the portfolio is rebalanced monthly and the manager has access to at least the previous 12 months of data, daily data has the potential to perform as well as high-frequency data. However, substantial improvements in the portfolio optimization decision from high-frequency data are realized if the manager rebalances daily or has less than a six-month estimation window.