December 12, 2022 (Monday)
Information Intensity and Pricing of Earnings Announcement Risk
Earnings announcements present a clear risk to investors. We conjecture that in a market with frictions, the premium of earnings announcement risk is likely realized in a discrete process and concentrated on periods with high intensity of cash-flow news. When there is update of cash-flow news and change in cash-flow uncertainty, investors adjust expectation of stock returns and incorporate the premium into stock prices. We construct an ex ante measure of expected information intensity (EII) based on anticipated corporate events and find that when firms have high EII, there is a significantly positive relation between earnings announcement risk and stock returns. A feasible strategy of long (short) stocks with high (low) earnings announcement risk yields a monthly 0.58% (annualized 6.96%) return in Fama-French five-factor alpha. Furthermore, we show that consistent with our conjecture, the premium is earned mostly around the date of corporate announcements. We provide additional evidence that information production and consumption trigger the pricing of risk.
Dr. Jingjing Chen
Jingjing Chen is a visiting assistant professor at Northeastern University. She received her Ph.D. in Finance from Washington State University. Her research interests are empirical asset pricing, sustainable investing, market microstructure, and derivatives. Jingjing’s research studies how information is incorporated into asset prices, and what drives asset returns in the short term (liquidity, attention) and the long term (cash flows, regulation, ESG preferences). She has a publication at Journal of Banking and Finance and a few working papers.
Jingjing teaches undergraduate, graduate and MBA courses. Her teaching interests include investments, corporate finance, financial modeling, quantitative portfolio management, FinTech, quantitative
finance, and data analytics. In 2021, Jingjing received Outstanding Doctoral Student Teaching Award.