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Dynamic financial distress prediction with concept drift based on time weighting combined with Adaboost support vector machine ensemble

机译：基于时间权重和Adaboost支持向量机集成的概念漂移动态财务困境预测

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摘要

Dynamic financial distress prediction (DFDP) is important for improving corporate financial risk management. However, earlier studies ignore the time weight of samples when constructing ensemble FDP models. This study ptoposes two new DFDP approaches based on time weighting and Adaboost support vector machine (SVM) ensemble. One is the double expert voting ensemble based on Adaboost-SVM and Timeboost-SVM (DEVE-AT), which externally combines the outputs of an error-based decision expert and a time-based decision expert. The other is Adaboost SVM internally integrated with time weighting (ADASVM-TW), which uses a novel error-time-based sample weight updating function in the Adaboost iteration. These two approaches consider time weighting of samples in constructing Adaboost-based SVM ensemble, and they are more suitable for DFDP in case of financial distress concept drift. Empirical experiment is carried out with sample data of 932 Chinese listed companies' 7 financial ratios, and time moving process is simulated by dividing the sample data into 13 batches with one year as time step. Experimental results show that both DEVE-AT and ADASVM-TW have significantly better DFDP performance than single SVM, batch-based ensemble with local weighted scheme, Adaboost-SVM and Timeboost-SVM, and they are more suitable for disposing concept drift of financial distress. (C) 2016 Elsevier B.V. All rights reserved.
机译：动态财务困境预测（DFDP）对于改善公司财务风险管理非常重要。但是，早期的研究在构建整体FDP模型时忽略了样本的时间权重。本研究提出了两种基于时间加权和Adaboost支持向量机（SVM）集成的新DFDP方法。一种是基于Adaboost-SVM和Timeboost-SVM（DEVE-AT）的双重专家投票系统，它在外部组合了基于错误的决策专家和基于时间的决策专家的输出。另一个是内部集成了时间加权（ADASVM-TW）的Adaboost SVM，它在Adaboost迭代中使用了基于错误时间的新颖样本权重更新功能。这两种方法在构建基于Adaboost的SVM集成时均考虑了样本的时间加权，并且在财务困境概念漂移的情况下，它们更适合于DFDP。对932家中国上市公司的7种财务比率的样本数据进行了实证实验，并将样本数据分为13批，以一年为时间步长，对时移过程进行了模拟。实验结果表明，DEVE-AT和ADASVM-TW的DFDP性能明显优于单个SVM，具有局部加权方案的基于批处理的集成，Adaboost-SVM和Timeboost-SVM，它们更适合解决财务困境的概念漂移。 （C）2016 Elsevier B.V.保留所有权利。

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