一、主题：Forecasting Stock Returns Under Model and Parameter Uncertainty: A Machine Learning Approach

二、主讲人：姜富伟，太阳集团tyc539副教授，资产管理研究中心研究员。主要研究方向包括金融大数据与机器学习、行为金融、资产定价、投资管理等。曾在Journal of Financial Economics、Review of Financial Studies、Journal of International Money and Finance、Journal of Banking and Finance、Journal of Portfolio Management、《金融研究》等重要期刊发表多篇学术论文。曾获得国际财务管理协会CFA最佳论文奖、中国金融评论国际研讨会Emerald优秀论文奖、《金融研究》优秀论文三等奖、全美华人金融协会最佳论文奖等学术奖项。

三、时间：2018年4月3日（周二），12:30-13:30

四、地点：学院南路校区主教学楼913会议室

五、主持人：黄志刚，太阳集团tyc539副教授

摘要：We propose a machine learning approach for combination forecasts of stock returns. When forecasting stock returns out of sample, sophisticated combinations, which average uni- and multivariate predictive regression forecasts, often fail to beat the historical average return, while simple mean combinations of univariate predictive regression forecasts often perform superior. In this paper, we apply the AdaBoost technique in machine learning to reduce parameter estimation risk and overfitting that impairs predictability of sophisticated combinations. Empirically, our new approach strongly beat historical average and simple mean combination with large out-of-sample $R^2_{OS}$. The predictability generates large utility gain for investors. In addition, the predictability is economically strong in both good times and bad times, and is linked to macroeconomic conditions.