报告题目：Inflation Forecasting from Cross-Sectional Stocks
报告人：潘军 上海交通大学
报告时间：2023年10月24日(周二) 16:30-18:00
报告地点：中国科学院数学与系统科学研究院南楼N204
腾讯会议ID：393 3774 3329
内容摘要
We document strong and unique inflation forecastability using the relative pricing between stocks with high- and low-inflation exposures. We construct the stock-level headline- and core-focused inflation betas by taking advantage of the fact that stock returns exhibit persistent sensitivity to headline-CPI shocks during the calendar month of CPI, and to core-CPI news on CPI announcement days. Above and beyond the existing forecasting methods, our stock-based portfolios contain fresh and non-redundant predictive information, indicating active price discovery on inflation in cross-sectional stocks. The core-focused forecasting portfolio emerges as a unique and unparalleled predictor for core inflation, whose predictive power and economic significance increase dramatically during the inflation surge of 2021 and 1973. Moreover, our stock-based information is not incorporated by economists in their inflation forecasts, whose room for improvement is especially large during 2021-22. We also find stronger predictability under Fed’s QE and when the Fed is behind-the-curve in fighting inflation.
主讲人简介
Jun Pan is currently a Professor of Finance and SalF Chair Professor at Shanghai Advanced Institute of Finance (SAIF), Shanghai Jiao Tong University. She is an editor at the Review of Finance and an associate editor at the Journal of Finance. Prior to joining SAIF in 2019, Jun was the School of Management Distinguished Professor of Finance and Professor of Finance at MIT Sloan School of Management. Jun has a B.S. degree in Physics from Shanghai Jiao Tong University, a Ph.D. in Physics from New York University, and a Ph.D. in Finance from Stanford University.
