Corporate Financial Distress Prediction Using the Risk-related Information Content of Annual Reports

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F24%3A39922245" target="_blank" >RIV/00216275:25410/24:39922245 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0306457324001791" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0306457324001791</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ipm.2024.103820" target="_blank" >10.1016/j.ipm.2024.103820</a>

Result language

angličtina

Original language name

Corporate Financial Distress Prediction Using the Risk-related Information Content of Annual Reports

Original language description

This study presents a financial distress prediction model focusing on the linguistic analysis of risk-related sections of corporate annual reports. Here, we introduce a novel methodology that leverages BERT-based contextualized embedding models for nuanced extraction of financial sentiment and topic coherence. This stands in contrast to existing research, which predominantly relies on dictionary-based or non-contextual word embeddings and addresses their limitations in context sensitivity. Furthermore, we apply an innovative financial distress prediction model that combines the robust XGBoost algorithm with unsupervised outlier detection techniques. This hybrid model is specifically designed to tackle the issue of class imbalance, a persistent challenge in financial distress prediction. The efficacy of the proposed model is empirically validated using a comprehensive dataset of 2545 companies listed on major global stock exchanges. Our findings indicate that the introduced model not only significantly outperforms most existing state-of-the-art financial distress prediction models in terms of predictive accuracy, but also significantly outperforms the Loughran &amp; McDonald dictionary-based approach and the Word2Vec model, underlining its potential as a superior analytical tool for financial distress prediction.

Czech name

—

Czech description

—

Type

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

CEP classification

—

OECD FORD branch

50204 - Business and management

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

2024

Confidentiality

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

Name of the periodical

Information Processing and Management

ISSN

0306-4573

e-ISSN

1873-5371

Volume of the periodical

61

Issue of the periodical within the volume

5

Country of publishing house

GB - UNITED KINGDOM

Number of pages

21

Pages from-to

103820

UT code for WoS article

001347745100001

EID of the result in the Scopus database

2-s2.0-85196624576