Artificial intelligence in predicting the bankruptcy of non-financial corporations

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 develop-ment 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 engineer-ing 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 engi-neering 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 regres-sion 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 bank-ruptcy using six of these indicators. Almost all sampled industries are privatised, and most com-panies 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 com-parative analyses of their own model with ours to reveal areas of model improvements.KEGA [001PU-4/2022]; Scientific Grant Agency of the Ministry of Education, Science, Research, and Sport of the Slovak Republic; Slovak Academy Sciences [1/0590/22]1/0590/22; Kultúrna a Edukacná Grantová Agentúra MŠVVaŠ SR, KEGA: 001PU-4/202

FUZZY, DISTRIBUTED, INSTANCE COUNTING, AND DEFAULT ARTMAP NEURAL NETWORKS FOR FINANCIAL DIAGNOSIS

This paper shows the potential of neural networks based on the Adaptive Resonance Theory as tools that generate warning signals when bankruptcy of a company is expected (bankruptcy prediction problem). Using that class of neural networks is still unexplored to date. We examined four of the most popular networks of the class — fuzzy, distributed, instance counting, and default ARTMAP. In order to illustrate their performance and to compare with other techniques, we used data, financial ratios, and experimental conditions identical to those published in previous studies. Our experiments show that two financial ratios provide highest discriminatory power of the model and ensure best prediction accuracy. We examined performance and validated results by exhaustive search of input variables, cross-validation, receiver operating characteristic analysis, and area under curve metric. We also did application-specific cost analysis. Our results show that distributed ARTMAP outperforms the other three models in general, but the fuzzy model is best performer for certain vigilance values and in the application-specific context. We also found that ARTMAP outperforms the most popular neural networks — multi-layer perceptrons and other statistical techniques applied to the same data.Neural networks, data mining, ARTMAP, bankruptcy prediction

Impact of sectors and political influence on financial distress across Pakistani public listed firms

Identifying financial distress provides information on ways to control and direct firms in achieving their goals. The common approach is to study the relationship between set of explanatory variables and financial distress. However, in order to improve the firms‘ financial structure, there is a need to understand the impact of sectors and different political conditions that affect financial distress. This study investigated the industry effects on financial distress as it is identified that financial distress might differ for firms due to the unique nature of each industry. The study dealt with projected key ideas to evaluate and compare diverse financial distress models to show the robustness of Pakistani listed firms across industries, and study how good financial distress can be predicted. Finally, to alleviate the severe consequences of political instability, the current study underlined the differences in financial distress determinants during different political regimes (Dictatorship and Democratic). The study analysed 153 non-financial firms listed on Karachi Stock Exchange (KSE) during a ten-year period (2004-2013) featuring two political periods; 2004 to 2008 as dictatorship period and 2009 to 2013 as democratic period. Four models were employed, namely logit analysis, decision tree, neural network and paired t-test. A diversity of models was employed to check the strength and prediction correctness of the models and t-test was employed to compare two different political regimes. From the findings, the indirect impact is clearly noticeable due to changes in the signs and magnitude of determinants across sectors. Logit analysis shows better results as compared to the other models as it was based on different industry-level variables and two different political regimes. The mechanism among the variables and financial distress is dependent on different political conditions of the country. The result shows that the impact of different political conditions varies across sectors. In addition, the results of this study are valuable for financial institutions to forecast financial distress and estimate minimum capital requirements to reduce the cost of risk management