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首页> 外文期刊>International journal of intelligent systems in accounting, finance & management >FORECASTING FIRM RISK IN THE EMERGING MARKET OF CHINA WITH SEQUENTIAL OPTIMIZATION OF INFLUENCE FACTORS ON PERFORMANCE OF CASE-BASED REASONING: AN EMPIRICAL STUDY WITH IMBALANCED SAMPLES
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FORECASTING FIRM RISK IN THE EMERGING MARKET OF CHINA WITH SEQUENTIAL OPTIMIZATION OF INFLUENCE FACTORS ON PERFORMANCE OF CASE-BASED REASONING: AN EMPIRICAL STUDY WITH IMBALANCED SAMPLES

机译：预测中国新兴市场的企业风险，依次优化案例推理的绩效影响因素：不平衡样本的实证研究

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With the development of the Chinese economy, how to make the right decision regarding firms' risk is becoming more and more important. Case-based reasoning (CBR) is a potential method that can help forecast business risk status in advance; it is easy to apply and is able to provide good explanations of output. In order to obtain more accurate prediction with CBR, it is essential to investigate factors that influence CBR's performance, and to optimize these factors sequentially for the improvement of CBR's performance in firm risk prediction in emerging markets under a more practicable assumption. We verified that sequential optimization of feature selection, feature weighting, instance selection and the number of nearest neighbours is a possible alternative for improving predictive performance of CBR forecasting under the assumption that the number of failed samples is smaller than that of nonfailed samples. The detailed implementation includes: (1) selecting significant features through a correlation matrix and reducing feature dimensions with factor analysis; (2) using variance contribution ratios of features from factor analysis as feature weights; (3) eliminating noisy cases via a state matrix; and (4) obtaining the optimal number of nearest neighbours from empirical results among different numbers of nearest neighbours. To validate the usefulness of the sequential optimization approach, we applied it to a real-world case: firm risk prediction with imbalanced data from the emerging market of China. Experimental results show that predictive accuracy of CBR applied in the emerging market was improved with the sequential optimization approach. Insightful thoughts from the results of the sequential optimization of the CBR forecasting system on modelling social tasks are also provided.
机译：随着中国经济的发展，如何对企业的风险做出正确的决策变得越来越重要。基于案例的推理（CBR）是一种可能有助于提前预测业务风险状态的方法。它易于应用，并且能够提供输出的良好解释。为了使用CBR获得更准确的预测，必须研究影响CBR绩效的因素，并在最可行的假设下依次优化这些因素，以改善CBR在新兴市场公司风险预测中的绩效。我们验证了在失败样本数量小于未失败样本数量的假设下，依次进行特征选择，特征权重，实例选择和最近邻居数量的优化可能会提高CBR预测的预测性能。具体实现包括：（1）通过相关矩阵选择重要特征，并通过因子分析减小特征维； （2）使用因子分析中特征的方差贡献率作为特征权重； （3）通过状态矩阵消除嘈杂的案件； （4）从不同数量的最近邻中的经验结果中获得最佳最近邻数。为了验证顺序优化方法的有效性，我们将其应用于一个实际案例：使用来自中国新兴市场的不平衡数据进行的公司风险预测。实验结果表明，采用顺序优化方法可以提高新兴市场中CBR的预测精度。还提供了基于CBR预测系统的顺序优化结果对社交任务建模的深刻见解。

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