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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Evaluating Sentiment in Annual Reports for Financial Distress Prediction Using Neural Networks and Support Vector Machines
verfasst von : Petr Hájek, Vladimír Olej
Erschienen in: Engineering Applications of Neural Networks
Verlag: Springer Berlin Heidelberg
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Sentiment in annual reports is recognized as being an important determinant of future financial performance. The aim of this study is to examine the effect of the sentiment on future financial distress. We evaluated the sentiment in the annual reports of U.S. companies using word categorization (rule-based) approach. We used six categories of sentiment, together with financial indicators, as the inputs of neural networks and support vector machines. The results indicate that the sentiment information significantly improves the accuracy of the used classifiers.