Financial News Sentiment Learned by BERT: A Strict Out-of-Sample Study

Abstract

I 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. I build on Google’s BERT model and sequentially pre-train and fine-tune it using Thomson Reuters financial news data covering the period from 1996 to 2020. With daily return

data of S&P 500 constituents, the 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. A trading strategy that leverages the sentiment signal generates an average return per trade of 24.06 bps over an 18 year out-of-sample period.

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