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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

  • Czech description

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