NLP-Based Analysis of Annual Reports: Asset Volatility Prediction and Portfolio Strategy Application

Leveraging recent developments in natural language processing (NLP), we constructed a prediction model using corporate financial annual reports to forecast the stock volatility indicator Beta (β), by analyzing risk discussions. The predicted Beta values were used to construct investment portfolios, whose market performance was then evaluated. Our research demonstrates that the Hierarchical Transformer-based model effectively captures complex risk information from annual reports, leading to improved returns in portfolio simulations. Our motivation arises from the need to better understand and process long, unstructured financial texts like annual reports, which contain crucial yet nuanced risk factors. By utilizing the hierarchical model, we aim to overcome traditional models’ limitations in handling such long documents, thereby improving the model’s understanding of both sentence-level and document-level contexts. The results highlight the potential of deep learning, particularly hierarchical models, in financial text prediction, and provide a novel perspective on asset management strategies. Compared to the S&P 500 benchmark, portfolios constructed using the predicted Beta values from our model achieved an average return increase of 21% over the same period.