Purpose

This study aims to develop a model based on the financial variables for better accuracy of financial distress prediction on the sample of private French, Spanish and Italian firms. Thus, firms in financial difficulties could timely request for troubled debt restructuring (TDR) to continue business.

Design/methodology/approach

This study used a sample of 312 distressed and 312 non-distressed firms. It includes 60 French, 21 Spanish and 231 Italian firms in both distressed and non-distressed groups. The data are extracted from the ORBIS database. First, the authors develop a new model by replacing a ratio in the original Z”-Score model specifically for financial distress prediction and estimate its coefficients based on linear discriminant analysis (LDA). Second, using the modified Z”-Score model, the authors develop a firm TDR probability index for distressed and non-distressed firms based on the logistic regression model.

Findings

The new model (modified Z”-Score), specifically for financial distress prediction, represents higher prediction accuracy. Moreover, the firm TDR probability index accurately depicts the probabilities trend for both groups of distressed and non-distressed firms.

Research limitations/implications

The findings of this study are conclusive. However, the sample size is small. Therefore, further studies could extend the application of the prediction model developed in this study to all the EU countries.

Practical implications

This study has important practical implications. This study responds to the EU directive call by developing the financial distress prediction model to allow debtors to do timely debt restructuring and thus continue their businesses. Therefore, this study could be useful for practitioners and firm stakeholders, such as banks and other creditors, and investors.

Originality/value

This study significantly contributes to the literature in several ways. First, this study develops a model for predicting financial distress based on the argument that corporate bankruptcy and financial distress are distinct events. However, the original Z”-Score model is intended for failure prediction. Moreover, the recent literature suggests modifying and extending the prediction models. Second, the new model is tested using a sample of firms from three countries that share similarities in their TDR laws.

中文翻译：

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私营企业的财务困境预测：开发问题债务重组模型

目的

本研究旨在开发一个基于财务变量的模型，以提高对法国、西班牙和意大利私营企业样本的财务困境预测的准确性。因此，陷入财务困难的企业可以及时请求问题债务重组（TDR）以继续经营。

设计/方法论/途径

本研究使用了 312 家陷入困境的公司和 312 家非陷入困境的公司的样本。其中包括 60 家法国公司、21 家西班牙公司和 231 家意大利公司，包括陷入困境和非陷入困境的公司。数据是从 ORBIS 数据库中提取的。首先，作者通过替换原始 Z”-Score 模型中专门用于财务困境预测的比率，开发了一个新模型，并基于线性判别分析（LDA）估计其系数。其次，作者使用修改后的 Z”-Score 模型，基于逻辑回归模型，为陷入困境和非陷入困境的公司开发了一个公司 TDR 概率指数。

发现

新模型（修改后的Z”-Score）专门针对财务困境预测，代表着更高的预测准确性。此外，公司 TDR 概率指数准确地描述了陷入困境和非陷入困境的公司的概率趋势。

研究局限性/影响

这项研究的结果是结论性的。然而，样本量很小。因此，进一步的研究可以将本研究中开发的预测模型的应用扩展到所有欧盟国家。

实际影响

这项研究具有重要的实际意义。本研究响应欧盟指令号召，开发财务困境预测模型，让债务人及时进行债务重组，从而继续经营。因此，这项研究对于从业者和公司利益相关者（例如银行和其他债权人以及投资者）可能有用。

原创性/价值

这项研究在几个方面对文献做出了重大贡献。首先，本研究基于公司破产和财务困境是不同事件的论点，开发了一个预测财务困境的模型。然而，最初的 Z”-Score 模型旨在用于故障预测。此外，最近的文献建议修改和扩展预测模型。其次，使用来自三个国家的 TDR 法律相似的公司样本来测试新模型。