讲座题目：Expected Returns and Foundation Models of Language

讲座时间：2022年12月7日（星期三）上午10:30-11:30

讲座方式：腾讯会议ID：916-518-287

密码：221207

讲座链接：//meeting.tencent.com/dm/6OM94M1NaSl3

主 讲 人：

修大成，现任芝加哥大学布斯商学院计量经济学和统计学教授。修大成教授的研究兴趣包括：设计统计方法并将其应用于金融数据，来研究数据中所反映的经济学含义。早期的研究涉及风险测量和投资组合管理，包括高频数据和衍生产品的计量经济学模型。目前的研究主要集中在设计机器学习方法来解决资产定价领域的大数据问题。他已经在Econometrica，Journal of Political Economy，Journal of Finance，Review of Financial Studies，Journal of the American Statistical Association，以及 Annals of Statistics等期刊上发表了研究成果。修教授担任Journal of Financial Econometrics的共同主编，是Review of Financial Studies，Management Science，Journal of Econometrics，Journal of Business & Economic Statistics，Journal of Applied Econometrics，the Econometrics Journal以及Journal of Empirical Finance等期刊的副主编。因其研究，他获得了多项荣誉，包括金融计量经济协会会士、Journal of Econometrics会士、Swiss Finance Institute Outstanding Paper Award、AQR Insight Award和欧洲金融协会年会最佳会议论文等。

讲座简介：

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.