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Speech emotion recognition and text sentiment analysis for financial distress prediction
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Speech emotion recognition and text sentiment analysis for financial distress prediction
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Journal Article
Speech emotion recognition and text sentiment analysis for financial distress prediction
2023
Overview
In recent years, there has been an increasing interest in text sentiment analysis and speech emotion recognition in finance due to their potential to capture the intentions and opinions of corporate stakeholders, such as managers and investors. A considerable performance improvement in forecasting company financial performance was achieved by taking textual sentiment into account. However, far too little attention has been paid to managerial emotional states and their potential contribution to financial distress prediction. This study seeks to address this problem by proposing a deep learning architecture that uniquely combines managerial emotional states extracted using speech emotion recognition with FinBERT-based sentiment analysis of earnings conference call transcripts. Thus, the obtained information is fused with traditional financial indicators to achieve a more accurate prediction of financial distress. The proposed model is validated using 1278 earnings conference calls of the 40 largest US companies. The findings of this study provide evidence on the essential role of managerial emotions in predicting financial distress, even when compared with sentiment indicators obtained from text. The experimental results also demonstrate the high accuracy of the proposed model compared with state-of-the-art prediction models.
Publisher
Springer London,Springer Nature B.V
Subject
/ Computational Biology/Bioinformatics
/ Computational Science and Engineering
/ Data Mining and Knowledge Discovery
/ Emotions
/ Image Processing and Computer Vision
/ Probability and Statistics in Computer Science
/ Profits
/ S.I.: Technologies of the 4th Industrial Revolution with applications
/ Special Issue on Technologies of the 4th Industrial Revolution with applications
