Analyzing Firm Reports for Volatility Prediction: A Knowledge-Driven Text-Embedding Approach

Predicting stock return volatility is the key to investment and risk management. Traditional volatility-forecasting methods primarily rely on stochastic models. More recently, many machine-learning approaches, particularly text-mining techniques, have been implemented to predict stock return volatility, thus taking advantage of the availability of large amounts of unstructured data such as firm financial reports. Most existing studies develop simple but effective models to analyze text, such as dictionary-based matching algorithms that use a set of manually constructed keywords. However, the latent and deep semantics encoded in text are usually neglected. In this study, we build on recent progress in representation learning and propose a novel word-embedding method that incorporates external knowledge from a well-known finance-domain lexicon (the Loughran and McDonald (2011) word list), which helps us learn semantic relationships among words in firm reports for better volatility prediction. Using over 10 years of annual reports from Russell 3000 firms, we empirically show that, compared with cutting-edge benchmarks, our proposed method achieves significant improvement in terms of prediction error, for example, a 28.4% reduction on average. We also discuss the practical and methodological implications of our findings. Our financial-specific word-embedding program is available as open-source information so that researchers can use it to analyze financial reports and assess financial risks.