Speech Emotion Recognition from Earnings Conference Calls in Predicting Corporate Financial Distress

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Speech Emotion Recognition from Earnings Conference Calls in Predicting Corporate Financial Distress

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Speech Emotion Recognition from Earnings Conference Calls in Predicting Corporate Financial Distress

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GA19-15498S" target="_blank" >GA19-15498S: Modelování emocí ve verbální a neverbální manažerské komunikaci pro predikci podnikových finančních rizik</a><br>

Návaznosti

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

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

IFIP Advances in Information and Communication Technology. Vol. 646

ISBN

978-3-031-08332-7

ISSN

1868-4238

e-ISSN

1868-422X

Počet stran výsledku

13

Strana od-do

216-228

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Cham

Místo konání akce

Hersonissos

Datum konání akce

17. 6. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000928714700018