Speech emotion recognition and text sentiment analysis for financial distress prediction

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F23%3A39920759" target="_blank" >RIV/00216275:25410/23:39920759 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s00521-023-08470-8#Fun" target="_blank" >https://link.springer.com/article/10.1007/s00521-023-08470-8#Fun</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00521-023-08470-8" target="_blank" >10.1007/s00521-023-08470-8</a>

Result language

angličtina

Original language name

Speech emotion recognition and text sentiment analysis for financial distress prediction

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA22-22586S" target="_blank" >GA22-22586S: Aspect-based sentiment analysis of financial texts for predicting corporate financial performance</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Neural Computing and Applications

ISSN

0941-0643

e-ISSN

1433-3058

Volume of the periodical

35

Issue of the periodical within the volume

29

Country of publishing house

US - UNITED STATES

Number of pages

15

Pages from-to

21463-21477

UT code for WoS article

000953614000004

EID of the result in the Scopus database

2-s2.0-85150470351