报告题目：What can we learn from machine forecasting error? 

报告人：申睿  香港中文大学（深圳）经济与管理学院和新加坡南洋理工大学bat365中文官方网站副教授

时间： 2018年9月20日(星期四)  9:30-11:30

地点：  bat365中文官方网站大楼210教室

邀请部门： 会计学系、公司财务研究所

报告简介：In this study, we utilize four popular machine learning algorithms to generate earnings forecasts for each firm. We find statistically significant associations between ex post machine forecasting errors and existing public information (input variables) such as Tobin’s Q, accruals, past stock returns, past return volatility and past forecasting errors. These associations are qualitatively and quantitatively similar to those documented in sell-side financial analyst forecasting errors. Our machine forecasting error evidence cannot be explained by agency incentives or over-weighting private information. We provide evidence consistent with an estimation uncertainty explanation.

报告人简介：

申睿，毕业于香港科技大学，获会计学博士学位，现任新加坡南洋理工大学bat365中文官方网站和香港中文大学（深圳）经济管理学院学院副教授，研究方向为资本市场、公司金融、自愿披露、分析师预测等领域，相关成果发表在The Accounting Review, Journal of Financial and Quantitative Analysis, Strategic Management Journal, Journal of Banking and Finance等顶尖学术期刊上。