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:金融危机预测在经济中发挥着关键作用,准确预测了失败结构的数目和概率,提供了对一国经济增长与稳定的洞察力。然而,预测中小企业的财政困难因其固有的模糊性而具有挑战性,导致融资成本增加,接受资金的机会减少。虽然已经为有效的金融交易控制制定了若干战略,但其实施、准确性和数据安全都达不到实际应用。此外,许多这些战略对于部分数据集而言表现良好,但不能适应各种数据集。因此,需要开发一个富有成效的预测模型,以便更好地执行和适应不同的数据集。在本次审查中,我们提出基于要素信用和数据来源收集的金融交易控制方案特征选择算法。目前的财务困难预测模型主要依靠财务报表,忽视组织测试的及时性。因此,我们建议采用公司财务危机预测模型,更好地与行业做法保持一致,并纳入对金融数据、公司治理质量和市场交换数据与相关矢量机的光头部分分析。实验结果表明,这一战略能够以较少的特征因素改进财务困难预测效率。