题 目：Twits versus Tweets: Does Adding Social Media Wisdom Trump Admitting Ignorance when Forecasting Volatility?

报告人：谢天 教授  厦门大学

主持人：梁友莎 博士  浙江大学经济学院

时 间：2017年11月20日（周一）   15:30-17:00

地 点：浙江大学玉泉校区经济学院418室  

Abstract

A rapidly growing literature has documented improvements in forecasting financial return volatility measurement via use of variants of the heterogeneous autoregression (HAR) model. At the same time, there is an increasing number of products made from social media that are suggested to improve forecast accuracy. In this paper, we first develop a model averaging heterogeneous autoregression (MAHAR) model that can account for model uncertainty. Second, we use a deep learning algorithm on a 10% random sample of Twitter messages at the hourly level to construct a sentiment measure that is being marketed by the Wall Street Journal. Our empirical results suggest that jointly incorporating model averaging techniques and sentiment measures from social media can significantly improve the forecasting accuracy of financial return volatility.

报告人简介：