Hybrid Neural Network Bankruptcy Prediction: An Integration of Financial Ratios, Intellectual Capital Ratios, MDA, and Neural Network Learning

Hsieh, Wen-Kuei; Liu, Shangming; Hsieh, Sung-Yi

doi:10.2991/jcis.2006.323

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…Among the chosen indicators, the gross margin (AV/S) holds significant prominence as evidenced by Ptak-Chmielewska (2019), Ben Jabeur (2017), and Hsieh et al (2006). In the profitability domain, the return on sales (ROS) indicator is widely acknowledged, according to Zmeškal et al (2023), Thanh-Long et al (2022, Štefko et al (2021), Ptak-Chmielewska (2019), and Ben Jabeur (2017).…”

Section: Methodsmentioning

confidence: 99%

Is Artificial Intelligence Really More Accurate in Predicting Bankruptcy?

Letkovský,

Jenčová,

Vašaničová

2024

IJFS

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Predicting bankruptcy within selected industries is crucial because of the potential ripple effects and unique characteristics of those industries. It serves as a risk management tool, guiding various stakeholders in making decisions. While artificial intelligence (AI) has shown high success rates in classification tasks, it remains uncertain whether its use significantly enhances the potential for early warning of impending problems. The following question arises: will classical methods eventually replace the effectiveness of these advanced techniques? This paper sheds light on the fact that even classical methods continue to achieve results that are not far behind, highlighting their enduring importance in financial analysis. This paper aims to develop bankruptcy prediction models for the chemical industry in Slovakia and to compare their effectiveness. Predictions are generated using the classical logistic regression (LR) method as well as AI techniques, artificial neural networks (ANNs), support vector machines (SVMs), and decision trees (DTs). The analysis aims to determine which of the employed methods is the most efficient. The research sample consists of circa 600 enterprises operating in the Slovak chemical industry. The selection of eleven financial indicators used for bankruptcy prediction was grounded in prior research and existing literature. The results show that all of the explored methods yielded highly similar outcomes. Therefore, determining the clear superiority of any single method is a difficult task. This might be partially due to the potentially reduced quality of the input data. In addition to classical statistical methods employed in econometrics, there is an ongoing development of AI-based models and their hybrid forms. The following question arises: to what extent can these newer approaches enhance accuracy and effectiveness?

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Section: Methodsmentioning

confidence: 99%

Is Artificial Intelligence Really More Accurate in Predicting Bankruptcy?

Letkovský,

Jenčová,

Vašaničová

2024

IJFS

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show abstract

“…In the same view, Kiani, Khurshid and Kasten (2006) used macro-financial variables to forecast recession. Meanwhile, Hsieh, Liu and Hsieh (2006) used MDA assisted neural network to predict bankruptcy of Taiwan company. However, only few such studies have been conducted using neural networks such as; Al-Osaimy (1998) and Maximilian (2008) who employed neural network in Islamic banking research.…”

Section: Prior Methods Used To Predict Business Cyclementioning

confidence: 99%

Predicting Future Depositor`s Rate of Return Applying Neural Network: A Case-study of Indonesian Islamic Bank

Anwar¹,

Watanabe²

2010

IJEF

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Islamic bank has to perform well in order to deliver better return in compensating depositor`s money. This paper is conducted to identify the relative significance assigned to macroeconomics variables for maximizing depositor’s opportunity. Furthermore, it becomes very necessary to have a prediction of future rate of return to get a clear picture in making deposit decision. This research uses some key macroeconomic variables such as; Jakarta Stock Indices (JSI), inflation rate (INFR), central bank`s interest rate certificate (INTR), exchange rate (ER), and money in circulation (MIC). Since these variables are characterized as nonlinearities time series data, Artificial Neural Networks (ANN) is employed using back propagation algorithm as learning algorithm. From observation resulted that central bank’s interest rate certificate (INTR) and Money In Circulation (MIC) could be used as leading indicators to face the problem with 94.95% accuracy.

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“…Charambous et al (2000) compared logistic regression with BP ANN on a sample of 139 US companies. Hsieh et al (2006) and Horvathova et al (2021) compared neural networks with multivariate discriminant analysis. Tinoco and Wilson (2013) compared neural networks with Altman's original Z-score specification on a sample of 23,218 companies during 1980-2011.…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial intelligence in predicting the bankruptcy of non-financial corporations

Gavurová

Jenčová

Bačík

et al. 2022

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Research background: In a modern economy, full of complexities, ensuring a business' financial stability, and increasing its financial performance and competitiveness, has become especially difficult. Then, monitoring the company's financial situation and predicting its future development becomes important. Assessing the financial health of business entities using various models is an important area in not only scientific research, but also business practice. Purpose of the article: This study aims to predict the bankruptcy of companies in the engineering and automotive industries of the Slovak Republic using a multilayer neural network and logistic regression. Importantly, we develop a novel an early warning model for the Slovak engineering and automotive industries, which can be applied in countries with undeveloped capital markets. Methods: Data on the financial ratios of 2,384 companies were used. We used a logistic regression to analyse the data for the year 2019 and designed a logistic model. Meanwhile, the data for the years 2018 and 2019 were analysed using the neural network. In the prediction model, we analysed the predictive performance of several combinations of factors based on the industry sector, use of the scaling technique, activation function, and ratio of the sample distribution to the test and training parts. Findings & value added: The financial indicators ROS, QR, NWC/A, and PC/S reduce the likelihood of bankruptcy. Regarding the value of this work, we constructed an optimal network for the automotive and engineering industries using nine financial indicators on the input layer in combination with one hidden layer. Moreover, we developed a novel prediction model for bankruptcy using six of these indicators. Almost all sampled industries are privatised, and most companies are foreign owned. Hence, international companies as well as researchers can apply our models to understand their financial health and sustainability. Moreover, they can conduct comparative analyses of their own model with ours to reveal areas of model improvements.

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Hybrid Neural Network Bankruptcy Prediction: An Integration of Financial Ratios, Intellectual Capital Ratios, MDA, and Neural Network Learning

Cited by 5 publications

References 14 publications

Is Artificial Intelligence Really More Accurate in Predicting Bankruptcy?

Is Artificial Intelligence Really More Accurate in Predicting Bankruptcy?

Predicting Future Depositor`s Rate of Return Applying Neural Network: A Case-study of Indonesian Islamic Bank

Artificial intelligence in predicting the bankruptcy of non-financial corporations

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