利用自组织图识别僵尸企业并将其可视化

IF 4.4 3区 管理学 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
David Veganzones, Eric Séverin
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引用次数: 0

摘要

发达国家僵尸企业的比例不断增加,危及经济增长,但人们对这些缺乏竞争力的企业的了解仍然有限。为了对 "僵尸企业 "有新的认识和理解,本研究依靠数据分析和预测建模,旨在建立一种基于 "僵尸企业 "财务状况自组织图及其 "僵尸化 "路径的财务诊断方法。本文还提出了一个旨在预测僵尸企业的 SOM-SVM 预测模型。研究结果发现了僵尸企业的不同特征;它们从最初的风险阶段到僵尸化的财务演变过程并不一致。导致僵尸化的财务恶化往往无法提前观察到,这对区分僵尸企业和健康企业的努力构成了重大障碍,并限制了在初始阶段识别僵尸企业的预测方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Identification and visualisation of zombie firms using self-organizing maps

Identification and visualisation of zombie firms using self-organizing maps

The growing share of zombie firms in developed countries puts economic growth at risk, yet understanding of these uncompetitive firms remains limited. To develop new insights and understanding of zombie firms, the current study relies on data analysis and predictive modeling and aims to establish a financial diagnosis method, based on a self-organizing map of the financial profiles of zombie firms and their pathways to zombification. This article also presents a SOM-SVM prediction model that seeks to anticipate zombie firms. The findings identify diverse profiles of zombie firms; their financial evolution from initial risky phases to zombification are not uniform. The financial deterioration that leads to zombification often cannot be observed in advance, which represents a major hurdle to efforts to differentiate zombie firms from healthy ones and restricts the effectiveness of prediction methods for identifying zombie firms in initial phases.

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来源期刊
Annals of Operations Research
Annals of Operations Research 管理科学-运筹学与管理科学
CiteScore
7.90
自引率
16.70%
发文量
596
审稿时长
8.4 months
期刊介绍: The Annals of Operations Research publishes peer-reviewed original articles dealing with key aspects of operations research, including theory, practice, and computation. The journal publishes full-length research articles, short notes, expositions and surveys, reports on computational studies, and case studies that present new and innovative practical applications. In addition to regular issues, the journal publishes periodic special volumes that focus on defined fields of operations research, ranging from the highly theoretical to the algorithmic and the applied. These volumes have one or more Guest Editors who are responsible for collecting the papers and overseeing the refereeing process.
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