主讲嘉宾：修大成，芝加哥大学布斯商学院
讲座时间：2023年2月13日（周一），上午11:00-13:00
讲座地点：腾讯会议（线上）
嘉宾简介：修大成，芝加哥大学布斯商学院教授（终生职）。中国科学技术大学数学、理工学学士，美国普林斯顿大学应用数学硕士、博士。研究领域为Financial Econometrics、Empirical Asset Pricing、Machine Learning in Finance、High-Dimensional Statistics、Quantitative Finance等。其研究成果发表于Econometrica、Journal of Econometrics、Review of Financial Studies、Journal of Finance、Annals of Statistics、Journal of the American Statistical Association等国际知名期刊。同时，修教授担任以下顶尖学术期刊的主编或编委：Review of Financial Studies、Journal of the American Statistical Association、Journal of Financial Econometrics、the Journal of Econometrics、Journal of Business & Economic Statistics、Management Science、Journal of Applied Econometrics、Journal of Empirical Finance等。
讲座摘要: We extract contextualized representations of news text to predict returns using the state-of-the-art foundation models in natural language processing. The contextualized representation of news reflects its content more accurately than the bag-of-words representation prevalent in the literature. In particular, the latter approach is more prone to errors when negation words appear in news articles. Moreover, we provide polyglot evidence on news-induced return predictability in 16 international equity markets with news written in 13 different languages. Information in newswires is assimilated into prices with an inefficient delay that is broadly consistent with limits-to-arbitrage, yet can be exploited in a real-time trading strategy. Furthermore, a trading strategy that exploits fresh news in the form of news alerts leads to even higher Sharpe ratios.
主办：中国互联网经济研究院
此次讲座获得北京高等学校卓越青年科学家计划资助（BJJWZYJH01201910034034），特此致谢。
