In this paper, we apply the BERT model, a cut-edging deep learning model, to construct a novel textual sentiment index in the Chinese stock market. By introducing the market returns as sentiment labels, our BERT model successfully extracts useful sentiment-related information contained in asset...

We provide a comprehensive study on the cross-sectional predictability of corporate bond returns using big data and machine learning. We examine whether a large set of equity and bond characteristics drive the expected returns on corporate bonds. Using either set of characteristics, we find that...

This paper examines the predictability and economic grounds of high-dimensional fundamental characteristics on stock price crash risk. By building a comprehensive set of characteristics in the Chinese stock market and using various machine learning algorithms, we highlight the outperformance of...

In this paper we apply economic narratives to inflation forecasting using a large news corpus and machine learning algorithms. We measure economic narratives quantitatively from the full text content of over 880,000 Wall Street Journal articles and represent them as interpretable news topics....

We introduce a variation of Yu(2011)'s weighted bagging estimation method and show it substantially improves the predictability of the equity premium and other economic variables. This new machine learning method sharply improves equity premium predictability of many models with significant...