Abstract
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 delisting requirement. The experiment’s results highlight the outperformance of the SVM model with F-score selection method in a data sample containing the highest percentage of non-financially distressed firms.
Highlights
According to Beaver (1966) in the first study on financial distress prediction, a firm is considered financially distressed or failed if the company fails to fulfill its financial obligations when mature
The Support Vector Machine (SVM) models with F-score feature selection outperform the Logistic Regression models
The logistic regression tries to compute the likeli- There are 12 models constructed with combinations hood of being “financially distressed” for a listed of the different data sampling techniques, feature firm
Summary
According to Beaver (1966) in the first study on financial distress prediction, a firm is considered financially distressed or failed if the company fails to fulfill its financial obligations when mature. There are numerous models that have been created and tested, it has been revealed that the performance of a financial distress prediction model varies if different sets of predictors, data samples and classifiers are applied. This research aims to build models to predict the financial distress condition of listed firms on securities exchange in Vietnam that focuses on the role of the feature selection method in association with different sampling choices in improving the model’s performance. Feature selection, defined as the approach for se- based sampling technique or stratified random lecting the optimal set of predictors, has been ap- sampling is used when the available distressed plied broadly in existing papers It is designed companies and only a part of the non-financially to produce better performance, reduce the cost of distressed companies are kept in the sample. Problem and degradation in the final prediction performance (Liang et al, 2015)
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