Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry

Abstract

In recent years in Taiwan, scholars who study financial bankruptcy have mostly focused on individual listed and over-the-counter (OTC) industries or the entire industry, while few have studied the independent electronics industry. Thus, this study investigated the application of an advanced hybrid Z-score bankruptcy prediction model in selecting financial ratios of listed companies in eight related electronics industries (semiconductor, computer, and peripherals, photoelectric, communication network, electronic components, electronic channel, information service, and other electronics industries) using data from 2000 to 2019. Based on 22 financial ratios of condition attributes and one decision attribute recommended and selected by experts and in the literature, this study used five classifiers for binary logistic regression analysis and in the decision tree. The experimental results show that for the Z-score model, samples analyzed using the five classifiers in five groups (1:1–5:1) of different ratios of companies, the bagging classifier scores are worse (40.82%) than when no feature selection method is used, while the logistic regression classifier and decision tree classifier (J48) result in better scores. However, it is significant that the bagging classifier score improved to over 90% after using the feature selection technique. In conclusion, it was found that the feature selection method can be effectively applied to improve the prediction accuracy, and three financial ratios (the liquidity ratio, debt ratio, and fixed assets turnover ratio) are identified as being the most important determinants affecting the prediction of financial bankruptcy in providing a useful reference for interested parties to evaluate capital allocation to avoid high investment risks.

An Analysis of the Financial Liquidity Management Strategy in Construction Companies Operating in the Podkarpackie Province

“…Financial liquidity in this context is defined as an ability to convert assets into cash in the shortest possible time and without losing their value, with the lowest transaction costs associated with this exchange. Financial liquidity is identified as being the most important determinants affecting the predictor of financial bankruptcy (Chen et al 2021;Chłodnicka and Zimon 2020). Financial liquidity and profitability are strongly related to working capital.…”

Market Efficiency and Stock Investment Loss Aversion Guide During COVID-19 Pandemic Events: The Case for Applying Data Mining

“…For example, variables that utilize firm characteristics are listed, and the foundation years, firm size (market value), debt rates, EPS, and enterprises’ new investment activity intent (Tobin’s Q ; Liu, 2021) are analyzed. Chen et al (2021) used financial ratios to study capital gains and losses in stock markets. Excluding the unpredictable limitation view of the stock market, data mining and artificial intelligence methods were used to study stock market capital gains and losses.…”

“…The established tree structure model summarizes important rules and makes predictions for unknown samples. It is widely used in finance (Basak et al, 2019; Chen et al, 2021; Coelho et al, 2007). For example, bankruptcy prediction models have been used to build financial crises in Taiwan’s electronics industry (Chen et al, 2021) and apply detection models for stock market manipulation (Liu et al, 2021).…”

“…It is widely used in finance (Basak et al, 2019; Chen et al, 2021; Coelho et al, 2007). For example, bankruptcy prediction models have been used to build financial crises in Taiwan’s electronics industry (Chen et al, 2021) and apply detection models for stock market manipulation (Liu et al, 2021). Cheng et al (2021) explored the relationship between financial indicators, corporate governance, and stock returns in the sports and leisure industries through decision trees to establish a stock investment decision model.…”

Artificial intelligence in predicting the bankruptcy of non-financial corporations

“…Jabeur (2017) developed a logistic model for French companies using 33 financial ratios. Chen et al (2021) predicted bankruptcy in Taiwan's electronics industry based on data from 2000 to 2019. Out of the 22 financial indicators used, the authors found that three affect corporate bankruptcy the most: liquidity ratio, debt ratio, and fixed assets turnover ratio.…”