报告人：Wolfgang Karl Härdle 教授
时间：2023年10月23日下午 13:30-14:30
地点：本部维格堂319
摘要:
We uncover networks from news articles to study cross-sectional stock returns. By analyzing a huge dataset of more than 1 million news articles collected from the internet, we construct time-varying directed networks of the S&P500 stocks. The well-defined directed news networks are formed based on a modest assumption about firm-specific news structure, and we propose an algorithm to tackle type-I errors in identifying the stock tickers. We find strong evidence for the comovement effect between the news-linked stocks returns and reversal effect from the lead stock return on the 1-day ahead follower stock return, after controlling for many known effects. Furthermore, a series of portfolio tests reveal that the news network attention proxy, network degree, provides a robust and significant cross-sectional predictability of the monthly stock returns. Among different types of news linkages, the linkages of within-sector stocks, large size lead firms, and lead firms with lower stock liquidity are crucial for cross-sectional predictability.
个人简介：Wolfgang Karl Härdle德国柏林洪堡大学统计与经济计量系终生教授，著名计量经济学家和统计学家，研究领域为高维非平稳时间序列分析、机器学习、文本分析、半参数和非参数计量经济学、金融市场风险建模等。在Annals of Statistics，Journal of Econometrics, Journal of Business Economic & Statistic, ,Journal of the American Statistical Association, Econometric Theory, Journal of the Royal Statistical Society, Journal of Financial Econometrics, Journal of Time Series Analysis等国际权威期刊上发表上百篇学术论文。同时是北京大学光华管理学院和厦门大学王亚南经济研究院客座教授。
邀请人：严继高