Evaluating Sentiment in Annual Reports for Financial Distress Prediction Using Neural Networks and Support Vector Machines

被引：0
|
作者：
Hajek, Petr [1 ]
Olej, Vladimir [1 ]
机构：
[1] Univ Pardubice, Fac Econ & Adm, Inst Syst Engn & Informat, Pardubice 53210, Czech Republic
来源：
ENGINEERING APPLICATIONS OF NEURAL NETWORKS, PT II | 2013年 / 384卷
关键词：
Sentiment analysis; annual reports; financial distress; neural networks; support vector machines; BANKRUPTCY PREDICTION; INFORMATION-CONTENT; TEXT;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
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.
引用
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页码：1 / 10
页数：10
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