Data Mining for Bankruptcy Prediction: An Experiment in Vietnam

Abstract

In the history of the world economy, the bankruptcy of some large companies has caused global financial crises. The study aimed to postulate a model of bankruptcy prediction for listed companies on Vietnam's stock market. The research used six popular algorithms in data mining to predict bankruptcy risk with data collected from 4693 observations in the period 2009-2020. The research results showed that Logistic algorithms, Artificial Neural Network, Decision Tree have a high level of predicting bankruptcy with an accuracy of 98%. The study identified the three most important indicators: inventory turnover ratio, debt to equity ratio, and debt ratio that affect the corporate bankruptcy prediction. The study showed the threshold points of 10-indicators to avoid bankruptcy likelihood. These results recommended that the model could be applied in practice to reduce risks for businesses and investors in the Vietnamese market.

Early Insolvency Prediction as a Key for Sustainable Business Growth

“…We used the gradient descent optimization algorithm as one of the most used optimization algorithms that updates the model parameters iteratively. The learning rate was set to 0.1 (Min and Lee [79]; Neves and Vieira [80]; Hájek and Olej [81], 2013; Sreedharan et al [82]) and the momentum was set to 0.2, referring to other insolvency prediction research papers (Hung et al [83]; Andone and Sireteanu [84]; Hung and Chen [85]; Vieira, et al [86]; Hájek and Olej [81], Rodan et al [87]). Batch training was selected as the most suitable type of training for small samples because it minimizes total error [77].…”