Financial Distress Prediction plays a crucial role in the economy by accurately forecasting the number and probability of failing structures, providing insight into the growth and stability of a country's economy. However, predicting financial distress for Small and Medium Enterprises is challenging due to their inherent ambiguity, leading to increased funding costs and decreased chances of receiving funds. While several strategies have been developed for effective FCP, their implementation, accuracy, and data security fall short of practical applications. Additionally, many of these strategies perform well for a portion of the dataset but are not adaptable to various datasets. As a result, there is a need to develop a productive prediction model for better order execution and adaptability to different datasets. In this review, we propose a feature selection algorithm for FCP based on element credits and data source collection. Current financial distress prediction models rely mainly on financial statements and disregard the timeliness of organization tests. Therefore, we propose a corporate FCP model that better aligns with industry practice and incorporates the gathering of thin-head component analysis of financial data, corporate governance qualities, and market exchange data with a Relevant Vector Machine. Experimental results demonstrate that this strategy can improve the forecast efficiency of financial distress with fewer characteristic factors.

翻译：财务困境预测通过准确预测失败结构的数量与概率，为国家经济增长与稳定性提供洞察，对经济具有关键作用。然而，中小企业的固有模糊性导致其财务困境预测极具挑战性，进而推高融资成本并降低资金获取概率。尽管已有多种策略被开发用于有效的财务困境预测，但其实现方式、准确性与数据安全性均未达到实际应用标准。此外，多数策略仅对部分数据集表现良好，缺乏对不同数据集的适应性。因此，亟需构建一种兼具高效订单执行与多数据集适应性的预测模型。本文提出一种基于要素信用与数据源采集的特征选择算法。现有财务困境预测模型主要依赖财务报表，忽视了组织测试的时效性。为此，我们构建了一个更契合行业实践的财务困境预测模型，该模型整合了财务数据薄头成分分析、公司治理特征及市场交易数据，并采用相关向量机进行建模。实验结果表明，该策略能够以更少的特征因子提升财务困境预测效率。