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首页> 外文期刊>Expert systems with applications >Financial health prediction models using artificial neural networks, genetic algorithm and multivariate discriminant analysis: Iranian evidence
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Financial health prediction models using artificial neural networks, genetic algorithm and multivariate discriminant analysis: Iranian evidence

机译：使用人工神经网络，遗传算法和多元判别分析的金融健康预测模型：伊朗证据

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The purpose of this study rs to design a model to predict financial health of companies. Financial ratios for 180 manufacturing companies quoted in Tehran Stock Exchange for one year (year ended March 21, 2008) have been used. Three models; based on artificial neural networks (ANN), genetic algorithm (GA), and multiple discriminant analysis (MDA) are utilized to classify the bankrupt from non bankrupt corporations. ANN model achieved 98.6% and 96.3% accuracy rates in training and holdout samples, respectively. To evaluate the reliability of the model, the data were examined with genetic algorithm and Multivariate discriminate analysis method. GA model attained only 92.5% and 91.5% accuracy rates and MDA reached 80.6% and 79.9 in training and holdout samples, respectively.%Department of Industrial Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran;Department of Industrial Engineering, Tarbiat Modares University, Tehran, Iran;Department of Industrial Engineering, Tarbiat Modares University, Tehran, Iran;
机译：本研究的目的是设计一个模型来预测公司的财务状况。使用了德黑兰证券交易所报价的180家制造公司为期一年（截至2008年3月21日的年度）的财务比率。三种模式；基于人工神经网络（ANN），遗传算法（GA）和多重判别分析（MDA）的公司将非破产公司的破产人分类。人工神经网络模型在训练样本和保持样本中的准确率分别达到98.6％和96.3％。为了评估模型的可靠性，使用遗传算法和多元判别分析方法对数据进行了检验。 GA模型在训练样本和保留样本中的准确率分别仅为92.5％和91.5％，MDA分别达到80.6％和79.9。％伊斯法罕工业大学工业工程系，伊斯法罕84156-83111；伊朗；塔比亚特工业工程系伊朗德黑兰Modares大学;伊朗德黑兰Tarbiat Modares大学工业工程系;

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