Speech emotion recognition and text sentiment analysis for financial distress prediction

Petr Hájek · Michal Munk

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Financial Distress and Bankruptcy Prediction Sentiment Analysis and Opinion Mining Stock Market Forecasting Methods

Abstract

Abstract 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.

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References

2011When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks 2018The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English 2016Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman's Z‐ Score Model 2019Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers 2011Earnings conference calls and stock returns: The incremental informativeness of textual tone 2012The Power of Voice: Managerial Affective States and Future Firm Performance 2018Deep learning models for bankruptcy prediction using textual disclosures 2013Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches

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2024A Systematic Literature Review of Modalities, Trends, and Limitations in Emotion Recognition, Affective Computing, and Sentiment Analysis 2024Corporate financial distress prediction using the risk-related information content of annual reports

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