报告题目：Forecasting Option Returns with News
主讲嘉宾：韩冰, 加拿大多伦多大学罗特曼管理学院金融学教授，多伦多证券交易所资本市场讲座教授。韩冰教授的主要研究领域是资产定价，投资，行为金融学，房地产金融。他的多篇论文发表在顶级经济，金融和管理学学术杂志上，包括Journal of Finance, Journal of Financial Economics，Review of Financial Studies， Review of Economic Studies，International Economic Review, Journal of Economic Theory，Management Science等。他的研究成果受到《纽约时报》、《华尔街日报》、《华盛顿邮报》、《经济学人》等媒体的专访和报导。韩冰教授获得了众多国际知名学术奖项，包括欧洲金融协会最佳论文奖，中国金融协会会议最佳论文奖，美国个人投资者协会在资产定价研究中获优秀论文奖，上海风险论坛最佳论文奖, 中国国际金融与政策论坛杰出论文奖, 全球金融专业人士协会终身成就奖。韩冰教授现任Financial Management，Journal of Economic Dynamics and Control，Journal of Empirical Finance，International Review of Finance和Pacific-Basin Finance Journal主编和副主编。
报告摘要：This paper investigates whether text data contains useful information about the cross-section of expected equity option returns. We apply both lexicon-based and machine learning approaches to extract qualitative signals from over six million news articles. The machine learning methods outperform lexicon-based approaches in predicting delta-hedged option returns and generate sizable profits. Our results are robust after controlling for known option return predictors including volatility-related variables and various underlying stock characteristics. An analysis of the keywords identified by machine learning methods suggests the option return predictability is largely related to firm-specific sentiment and option mispricing. Our work highlights the importance of analyzing unstructured data like texts for pricing derivatives.
报告时间：2023年05月30日(周二)，10:00-11:00
线下地点：厦门大学经济楼N302
线上地点：腾讯会议ID：929 2139 6317
