Exploring the forecasting potential of company annual reports

Abstract

Previous research indicates that the narration disclosure in company annual reports can be used to assist in assessing the company's short-term financial prospects. However, not much effort has been made to systematically and automatically assess the predictive potential of such reports using text classification, information retrieval, and machine learning techniques. In this study, we built SVM-based predictive models with different feature selection methods from ten years of annual reports of 30 companies. We used feature selection methods to reduce the term space and studied the classrelated vocabulary. Evaluation of predictive accuracy is performed with cross validation and t-test significance tests. We compare different models' performance and analyze misclassification rates by year and by industry. We identify the strengths and weaknesses of each model. Our results support the feasibility of automatically predicting next-year company financial performance from the current year's report. We suggest text features can be further studied to understand their roles as indicators of company's future performance. This research paves the way for large-scale automatic analysis of the relationship between annual reports and short-term performance, as well as the identification of interesting signals within annual reports.