演講者簡介 : Professor Wang received his Ph.D. in Finance at Kenan-Flagler Business School, UNC-Chapel Hill. He is currently a Professor of Finance at Department of Economics and Finance, University of Dayton. His research interests are Market Microstructure, Behavioral Finance, Asset Pricing, Investments, and Systemic Risk.
演講摘要 : We develop a computationally straightforward measure for the probability of informed trading. This measure, termed PCM, captures the adverse selection component of bid-ask spreads, becomes elevated around earnings announcements, and exhibits similar patterns to Kyle lambda in time series. A long-short portfolio based on PCM generates a large alpha of 28.6 percent annually. In regression analysis, PCM significantly explains cross-sectional returns while controlling for other confounding factors. This effect is robust to alternative setups and becomes even stronger after the decimalization in 2001. In sum, PCM is straightforward to implement and yet still renders an effective proxy for informed trading.
