December 12, 2022 (Monday)
11:00 a.m. to noon
Amount of Information in Earnings Announcements and Pricing Risk
Earnings announcements pose obvious risks to investors. We speculate that in a frictional market, earnings announcement risk premiums are likely to be realized in discrete processes and concentrated during periods of high cash flow news. As cash flow news updates and cash flow uncertainties change, investors adjust their return expectations for stocks and build a premium into stock prices. We construct an ex-ante measure of expected information intensity (EII) based on expected corporate events and find that when a company’s EII is high, there is a significant positive relationship between earnings release risk and stock return. discovered. A viable strategy for long (short) stocks with high (low) earnings release risk yields a 0.58% monthly return (6.96% annualized) on the Fama-French 5 Factor Alpha. Furthermore, consistent with our speculation, we show that premiums are mostly captured before and after company announcements. We provide additional evidence that the production and consumption of information drives risk pricing.
Dr. Jingjing Chen
Jingjing Chen is a Visiting Assistant Professor at Northeastern University. She got her Ph.D. She holds a PhD in Finance from Washington State University. Her research interests include empirical asset pricing, sustainable investing, market microstructures, and derivatives. Jingjing’s research studies how information is embedded in asset prices and what drives asset returns in the short term (liquidity, attention) and long term (cash flow, regulation, ESG preferences). . She has published papers in the Journal of Banking and Finance and has authored several of her working papers.
Jingjing teaches undergraduate, graduate and MBA courses. Her teaching positions include Investment, Corporate Finance, Financial Modeling, Quantitative Portfolio Management, FinTech, and Quantitative Analytics.
Finance, and data analysis. In 2021, Jingjing received her Outstanding Doctoral Student Teaching Award.