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An Ensemble Model Minimising Misjudgment Cost: Empirical Evidence From Chinese Listed Companies

Kunpeng Yuan, Mohammad Abedin Orcid Logo, Petr Hajek

International Journal of Finance & Economics

Swansea University Author: Mohammad Abedin Orcid Logo

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DOI (Published version): 10.1002/ijfe.3097

Abstract

Predicting corporate financial distress is critical for bank lending and corporate bond investment decisions. Incorrect identification of default status can mislead lenders and investors, leading to substantial losses. This paper proposes an ensemble model that minimises the overall cost of misjudgm...

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Published in: International Journal of Finance & Economics
ISSN: 1076-9307 1099-1158
Published: Wiley 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa68742
first_indexed 2025-01-27T09:47:33Z
last_indexed 2025-06-18T04:54:12Z
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spelling 2025-06-17T16:19:21.2661586 v2 68742 2025-01-27 An Ensemble Model Minimising Misjudgment Cost: Empirical Evidence From Chinese Listed Companies 4ed8c020eae0c9bec4f5d9495d86d415 0000-0002-4688-0619 Mohammad Abedin Mohammad Abedin true false 2025-01-27 CBAE Predicting corporate financial distress is critical for bank lending and corporate bond investment decisions. Incorrect identification of default status can mislead lenders and investors, leading to substantial losses. This paper proposes an ensemble model that minimises the overall cost of misjudgment by considering the imbalanced ratio weighted loss of the unbalanced ratio of Type I and Type II errors in the objective function. Unlike existing static financial distress prediction models, the proposed model integrates panel data by using time-shifting to account for credit risk dynamics. To validate the prediction model, data were collected for Chinese listed companies, considering geographic area, ownership structure and firm size. We demonstrate that by weighting predictions from different classification models, the overall misjudgment cost can be minimised. This study identifies earnings per share and the product price index as the most relevant indicators affecting the financial performance of Chinese-listed companies. Overall, the results indicate that the proposed model has a predictive capacity of up to 5 years, with 98.7% for 1-year forecasting horizons and 96.8% for 5-year-ahead forecasting horizons. Furthermore, the proposed model outperforms existing distress prediction models in overall prediction performance by correctly identifying defaulting companies while avoiding misjudging good companies. Journal Article International Journal of Finance & Economics 0 Wiley 1076-9307 1099-1158 dynamics, ensemble model, financial distress, misjudgment cost, prediction 31 1 2025 2025-01-31 10.1002/ijfe.3097 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2025-06-17T16:19:21.2661586 2025-01-27T09:43:32.3469938 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Kunpeng Yuan 1 Mohammad Abedin 0000-0002-4688-0619 2 Petr Hajek 3 68742__33511__735fcb4fb0084322b1c5e4ca3a4ec060.pdf 68742.VOR.pdf 2025-02-05T15:22:43.2717998 Output 3166258 application/pdf Version of Record true © 2025 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0). true eng http://creativecommons.org/licenses/by/4.0/
title An Ensemble Model Minimising Misjudgment Cost: Empirical Evidence From Chinese Listed Companies
spellingShingle An Ensemble Model Minimising Misjudgment Cost: Empirical Evidence From Chinese Listed Companies
Mohammad Abedin
title_short An Ensemble Model Minimising Misjudgment Cost: Empirical Evidence From Chinese Listed Companies
title_full An Ensemble Model Minimising Misjudgment Cost: Empirical Evidence From Chinese Listed Companies
title_fullStr An Ensemble Model Minimising Misjudgment Cost: Empirical Evidence From Chinese Listed Companies
title_full_unstemmed An Ensemble Model Minimising Misjudgment Cost: Empirical Evidence From Chinese Listed Companies
title_sort An Ensemble Model Minimising Misjudgment Cost: Empirical Evidence From Chinese Listed Companies
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Mohammad Abedin
author Mohammad Abedin
author2 Kunpeng Yuan
Mohammad Abedin
Petr Hajek
format Journal article
container_title International Journal of Finance & Economics
container_volume 0
publishDate 2025
institution Swansea University
issn 1076-9307
1099-1158
doi_str_mv 10.1002/ijfe.3097
publisher Wiley
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
document_store_str 1
active_str 0
description Predicting corporate financial distress is critical for bank lending and corporate bond investment decisions. Incorrect identification of default status can mislead lenders and investors, leading to substantial losses. This paper proposes an ensemble model that minimises the overall cost of misjudgment by considering the imbalanced ratio weighted loss of the unbalanced ratio of Type I and Type II errors in the objective function. Unlike existing static financial distress prediction models, the proposed model integrates panel data by using time-shifting to account for credit risk dynamics. To validate the prediction model, data were collected for Chinese listed companies, considering geographic area, ownership structure and firm size. We demonstrate that by weighting predictions from different classification models, the overall misjudgment cost can be minimised. This study identifies earnings per share and the product price index as the most relevant indicators affecting the financial performance of Chinese-listed companies. Overall, the results indicate that the proposed model has a predictive capacity of up to 5 years, with 98.7% for 1-year forecasting horizons and 96.8% for 5-year-ahead forecasting horizons. Furthermore, the proposed model outperforms existing distress prediction models in overall prediction performance by correctly identifying defaulting companies while avoiding misjudging good companies.
published_date 2025-01-31T07:39:54Z
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score 11.067306

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