Evaluating sentiment in annual reports for financial distress prediction using neural networks and support vector machines

被引：0
作者：
机构：
[1] Institute of System Engineering and Informatics, University of Pardubice, 532 10 Pardubice
来源：
| 1600年 / Springer Verlag卷 / 384期
关键词：
Annual reports; Financial distress; Neural networks; Sentiment analysis; Support vector machines;
D O I：
10.1007/978-3-642-41016-1_1
中图分类号：
学科分类号：
摘要：
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. © Springer-Verlag Berlin Heidelberg 2013.
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页码：1 / 10
页数：9
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