Speech Emotion Recognition from Earnings Conference Calls in Predicting Corporate Financial Distress
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F22%3A39919467" target="_blank" >RIV/00216275:25410/22:39919467 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-08333-4_18" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-08333-4_18</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-08333-4_18" target="_blank" >10.1007/978-3-031-08333-4_18</a>
Alternative languages
Result language
angličtina
Original language name
Speech Emotion Recognition from Earnings Conference Calls in Predicting Corporate Financial Distress
Original language description
Sentiment and emotion analysis is attracting considerable interest from researchers in the field of finance due to its capacity to provide additional insight into opinions and intentions of investors and managers. A remarkable improvement in predicting corporate financial performance has been achieved by considering textual sentiments. However, little is known about whether managerial affective states influence changes in overall corporate financial performance. To overcome this problem, we propose a deep learning architecture that uses vocal cues extracted from earnings conference calls to detect managerial emotional states and exploits these states to identify firms that could be financially distressed. Our findings provide evidence on the role of managerial emotional states in the early detection of corporate financial distress. We also show that the proposed deep learning-based prediction model outperforms state-of-the-art financial distress prediction models based solely on financial indicators.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA19-15498S" target="_blank" >GA19-15498S: Modelling emotions in verbal and nonverbal managerial communication to predict corporate financial risk</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
IFIP Advances in Information and Communication Technology. Vol. 646
ISBN
978-3-031-08332-7
ISSN
1868-4238
e-ISSN
1868-422X
Number of pages
13
Pages from-to
216-228
Publisher name
Springer Nature Switzerland AG
Place of publication
Cham
Event location
Hersonissos
Event date
Jun 17, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000928714700018