Financial distress prediction: a novel data segmentation research on Chinese listed companies

In the Chinese stock market, the unique special treatment (ST) warning mechanism can signal financial distress for listed companies. In existing studies, classification model has been developed to differentiate the two general listing states. However, this classification model cannot explain the in...

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Autores principales:	Fang-Jun Zhu, Lu-Juan Zhou, Mi Zhou, Feng Pei
Formato:	article
Lenguaje:	EN
Publicado:	Vilnius Gediminas Technical University 2021
Materias:	financial distress prediction Chinese listed companies ensemble learning data mining data segmentation special treatment Economic growth, development, planning HD72-88 Business HF5001-6182
Acceso en línea:	https://doaj.org/article/d3f45b867d0e4a539870935d25edc17e
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spelling	oai:doaj.org-article:d3f45b867d0e4a539870935d25edc17e2021-11-04T16:03:27ZFinancial distress prediction: a novel data segmentation research on Chinese listed companies10.3846/tede.2021.153372029-49132029-4921https://doaj.org/article/d3f45b867d0e4a539870935d25edc17e2021-11-01T00:00:00Zhttps://jeelm.vgtu.lt/index.php/TEDE/article/view/15337https://doaj.org/toc/2029-4913https://doaj.org/toc/2029-4921 In the Chinese stock market, the unique special treatment (ST) warning mechanism can signal financial distress for listed companies. In existing studies, classification model has been developed to differentiate the two general listing states. However, this classification model cannot explain the internal changes of each listing state. Considering that the requirement of the withdrawal of ST in the mechanism is relatively loose, we propose a new segmentation approach for Chinese listed companies, which are divided into negative companies and positive companies according to the number of times being labeled ST. Under the framework of data mining, we use financial indicators, non-financial indicators, and time series to build a financial distress prediction model of distinguishing the long-term development of different Chinese listed companies. Through data segmentation, we find that the negative samples have a huge destructive interference on the prediction effect of the total sample. On the contrary, positive companies improve the prediction accuracy in all aspects and the optimal feature set is also different from all companies. The main contribution of the paper is to analyze the internal impact of the deterioration of financial distress prediction in time series and construct an optimization model for positive companies. First published online 04 November 2021 Fang-Jun ZhuLu-Juan ZhouMi ZhouFeng PeiVilnius Gediminas Technical Universityarticlefinancial distress predictionChinese listed companiesensemble learningdata miningdata segmentationspecial treatmentEconomic growth, development, planningHD72-88BusinessHF5001-6182ENTechnological and Economic Development of Economy (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	financial distress prediction Chinese listed companies ensemble learning data mining data segmentation special treatment Economic growth, development, planning HD72-88 Business HF5001-6182
spellingShingle	financial distress prediction Chinese listed companies ensemble learning data mining data segmentation special treatment Economic growth, development, planning HD72-88 Business HF5001-6182 Fang-Jun Zhu Lu-Juan Zhou Mi Zhou Feng Pei Financial distress prediction: a novel data segmentation research on Chinese listed companies
description	In the Chinese stock market, the unique special treatment (ST) warning mechanism can signal financial distress for listed companies. In existing studies, classification model has been developed to differentiate the two general listing states. However, this classification model cannot explain the internal changes of each listing state. Considering that the requirement of the withdrawal of ST in the mechanism is relatively loose, we propose a new segmentation approach for Chinese listed companies, which are divided into negative companies and positive companies according to the number of times being labeled ST. Under the framework of data mining, we use financial indicators, non-financial indicators, and time series to build a financial distress prediction model of distinguishing the long-term development of different Chinese listed companies. Through data segmentation, we find that the negative samples have a huge destructive interference on the prediction effect of the total sample. On the contrary, positive companies improve the prediction accuracy in all aspects and the optimal feature set is also different from all companies. The main contribution of the paper is to analyze the internal impact of the deterioration of financial distress prediction in time series and construct an optimization model for positive companies. First published online 04 November 2021
format	article
author	Fang-Jun Zhu Lu-Juan Zhou Mi Zhou Feng Pei
author_facet	Fang-Jun Zhu Lu-Juan Zhou Mi Zhou Feng Pei
author_sort	Fang-Jun Zhu
title	Financial distress prediction: a novel data segmentation research on Chinese listed companies
title_short	Financial distress prediction: a novel data segmentation research on Chinese listed companies
title_full	Financial distress prediction: a novel data segmentation research on Chinese listed companies
title_fullStr	Financial distress prediction: a novel data segmentation research on Chinese listed companies
title_full_unstemmed	Financial distress prediction: a novel data segmentation research on Chinese listed companies
title_sort	financial distress prediction: a novel data segmentation research on chinese listed companies
publisher	Vilnius Gediminas Technical University
publishDate	2021
url	https://doaj.org/article/d3f45b867d0e4a539870935d25edc17e
work_keys_str_mv	AT fangjunzhu financialdistresspredictionanoveldatasegmentationresearchonchineselistedcompanies AT lujuanzhou financialdistresspredictionanoveldatasegmentationresearchonchineselistedcompanies AT mizhou financialdistresspredictionanoveldatasegmentationresearchonchineselistedcompanies AT fengpei financialdistresspredictionanoveldatasegmentationresearchonchineselistedcompanies
_version_	1718444679443251200

Financial distress prediction: a novel data segmentation research on Chinese listed companies

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