Application of boruta feature selection in enhancing financial distress prediction performance of hybrid MLP_GA (original) (raw)
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Model Selection and Feature Ranking for Financial Distress Classification
In this paper we apply several learning machine techniques to the problem of financial distress classification of medium-sized private companies. Financial data was obtained from Diana, a large database containing financial statements of French companies. Classification accuracy is evaluated with Artificial Neural Networks, Classification and Regression Tress (CART), TreeNet, Random Forests and Liner Genetic Programs (LGPs). We analyze both type I (bankrupted companies misclassified as healthy) and type II (healthy companies misclassified as bankrupted) errors on two datasets containing balanced and unbalanced class distribution. LGPs have the best performance accuracy in both balanced data and unbalanced dataset. Our results demonstrate the potential of using learning machines, with respect to discriminant analysis, in solving important economics problems such as bankruptcy detection. We also address the related issue of ranking the importance of input features, which is itself a p...
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Feature Selection for Bankruptcy Prediction
International Journal of Natural Computing Research, 2010
In this work a Multi-Objective Evolutionary Algorithm (MOEA) was applied for feature selection in the problem of bankruptcy prediction. This algorithm maximizes the accuracy of the classifier while keeping the number of features low. A two-objective problem, that is minimization of the number of features and accuracy maximization, was fully analyzed using the Logistic Regression (LR) and Support Vector Machines (SVM) classifiers. Simultaneously, the parameters required by both classifiers were also optimized, and the validity of the methodology proposed was tested using a database containing financial statements of 1200 medium sized private French companies. Based on extensive tests, it is shown that MOEA is an efficient feature selection approach. Best results were obtained when both the accuracy and the classifiers parameters are optimized. The proposed method can provide useful information for decision makers in characterizing the financial health of a company.
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Investment Management and Financial Innovations
The research is taken to integrate the effects of variable selection approaches, as well as sampling techniques, to the performance of a model to predict the financial distress for companies whose stocks are traded on securities exchanges of Vietnam. A firm is financially distressed when its stocks are delisted as requirement from Vietnam Stock Exchange because of making a loss in 3 consecutive years or having accumulated a loss greater than the company’s equity. There are 12 models, constructed differently in feature selection methods, sampling techniques, and classifiers. The feature selection methods are factor analysis and F-score selection, while 3 sets of data samples are chosen by choice-based method with different percentages of financially distressed firms. In terms of classifying technique, logistic regression together with SVM are used in these models. Data are collected from listed firms in Vietnam from 2009 to 2017 for 1, 2 and 3 years before the announcement of their d...
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Risks, 2021
In the face of rising defaults and limited studies on the prediction of financial distress in Morocco, this article aims to determine the most relevant predictors of financial distress and identify its optimal prediction models in a normal Moroccan economic context over two years. To achieve these objectives, logistic regression and neural networks are used based on financial ratios selected by lasso and stepwise techniques. Our empirical results highlight the significant role of predictors, namely interest to sales and return on assets in predicting financial distress. The results show that logistic regression models obtained by stepwise selection outperform the other models with an overall accuracy of 93.33% two years before financial distress and 95.00% one year prior to financial distress. Results also show that our models classify distressed SMEs better than healthy SMEs with type I errors lower than type II errors.
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