News Sentiment and Equity Returns

Abstract

We investigate the impact of financial news on equity returns and introduce a non-parametric model to generate a sentiment signal, which is then used as a predictor for short-term, single-stock equity return forecasts.

We build on Google's BERT model (for Bidirectional Encoder Representations for Transformers, see Devlin et al., 2018) and sequentially train it on financial news from Thomson Reuters (We thank Thomson Reuters / Refinitiv for providing us with this comprehensive set of news data.) covering the period from 1996 to 2020. With daily return data of S&P 500 constituents, our analysis shows that financial news carry information that is not immediately reflected in equity prices. News is largely priced-in within one day, with diffusion varying across industries.

We test a simple trading strategy based on the sentiment signal and report a return per trade of 24.06 bps and significant alpha of 77.56 % p.a. with respect to a Fama-French 5-factor model plus momentum over an 18 year out-of-sample period.