Abstract
Two novel Natural Language Processing (NLP) classification techniques are applied to the analysis of corporate annual reports in the task of financial forecasting. The hypothesis is that textual content of annual reports contain vital information for assessing the performance of the stock over the next year. The first method is based on character n-gram profiles, which are generated for each annual report, and then labeled based on the CNG classification. The second method draws on a more traditional approach, where readability scores are combined with performance inputs and then supplied to a support vector machine (SVM) for classification. Both methods consistently outperformed a benchmark portfolio, and their combination proved to be even more effective and efficient as the combined models yielded the highest returns with the fewest trades.
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Butler, M., Kešelj, V. (2009). Financial Forecasting Using Character N-Gram Analysis and Readability Scores of Annual Reports. In: Gao, Y., Japkowicz, N. (eds) Advances in Artificial Intelligence. Canadian AI 2009. Lecture Notes in Computer Science(), vol 5549. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01818-3_7
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DOI: https://doi.org/10.1007/978-3-642-01818-3_7
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