Tests of Asset Pricing Models with A Large Number of Assets

Dashan Huang is an associate professor of finance and BNP Paribas Fellow at the Lee Kong Chian School of Business, Singapore Management University (SMU). He joined SMU in 2013 after obtaining his PhD in finance from Washington University in St. Louis. His research focuses on asset pricing, behavioural finance, big data, and machine learning, and has been published in the Review of Financial Studies, Journal of Financial Economics, Journal of Financial and Quantitative Analysis, Management Science, etc. 

We propose both statistical and economic asset pricing tests that extend the well-known Gibbons, Ross, and Shanken (1989) test to allow for many assets whose dimensionality exceeds sample size. Empirically, we find that the tests reject six well-known asset pricing models at the stock level, as well as recently developed machine learning models. The economic test provides a real time profitable trading strategy that exploits mispricing, and the mispricing patterns are similar across all the models. The significant profitability is unexplained by limits-to-arbitrage, prospect theory, and expectation extrapolation, suggesting that new factors are needed to better understand the cross section of stock returns.