Bankruptcy prediction of small- and medium-sized enterprises in Poland based on the LDA and SVM methods

Ptak-Chmielewska, Aneta

doi:10.21307/stattrans-2021-010

Cited by 14 publications

(8 citation statements)

References 26 publications

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“…Ptak-Chmielewska built a bankruptcy prediction model based on the SVM algorithm. By comparing three typical corporate distress models with this model, they concluded that traditional neural network algorithms can effectively predict corporate distress from the perspective of financial statements [ 16 ]. Zhou et al discussed the effectiveness and limitations of SVM when applied to the bankruptcy prediction problem, and the experimental results showed that the SVM method of their classifier performed better than the BPN neural network model on corporate bankruptcy problems, and with the reduction of the training set size, the accuracy and generalization degree of the SVM algorithm are higher than those of the BPN algorithm [ 17 ].…”

Section: Related Workmentioning

confidence: 99%

Design of Financial Management Model Using the Forward Neural Network Based on Particle Swarm Optimization Algorithm

Pan

2022

Computational Intelligence and Neuroscience

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The financial crisis of listed companies will bring huge losses to investors, so it is very important to establish a financial early warning model for investors and other stakeholders. The forward neural network model of particle swarm optimization is used to model and analyze the financial early warning of listed companies. In terms of data selection, earnings management indicators are substituted into the model for the common phenomenon of earnings management in listed companies. The results show that the accuracy of the model considering earnings management factors is improved from 65% to 70%. In the process of modeling, this paper uses the logistic regression model to further modify the model. The empirical results show that the accuracy of the model can be improved from 70% to 75%. When using the forward neural network model based on particle swarm optimization to make an empirical analysis of financial early warning of listed companies, adding quantitative indicators of earnings management can improve the accuracy of the model. In the demonstration, the correction of logistic regression model can also improve the accuracy of the particle swarm neural network financial early warning model. This greatly reduces the risk that companies with poor financial conditions will face bankruptcy and liquidation.

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Section: Related Workmentioning

confidence: 99%

Design of Financial Management Model Using the Forward Neural Network Based on Particle Swarm Optimization Algorithm

Pan

2022

Computational Intelligence and Neuroscience

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“…Then, the XGB based classification optimized using JO method named JO XGB is utilized for classifying the financial data. In Ptak-Chmielewska [16], compared the efficiency of LDA and SVM predictions. An instance of SME was utilized in the empirical analyses, financial ratios have been used and non-financial aspects have been considered.…”

Section: Prior Fcp Models For Smesmentioning

confidence: 99%

Artificial Intelligence Based Optimal Functional Link Neural Network for Financial Data Science

Hilal¹,

Alsolai²,

Al‐Wesabi³

et al. 2022

Computers, Materials &Amp; Continua

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In present digital era, data science techniques exploit artificial intelligence (AI) techniques who start and run small and medium-sized enterprises (SMEs) to have an impact and develop their businesses. Data science integrates the conventions of econometrics with the technological elements of data science. It make use of machine learning (ML), predictive and prescriptive analytics to effectively understand financial data and solve related problems. Smart technologies for SMEs enable allows the firm to get smarter with their processes and offers efficient operations. At the same time, it is needed to develop an effective tool which can assist small to medium sized enterprises to forecast business failure as well as financial crisis. AI becomes a familiar tool for several businesses due to the fact that it concentrates on the design of intelligent decision making tools to solve particular real time problems. With this motivation, this paper presents a new AI based optimal functional link neural network (FLNN) based financial crisis prediction (FCP) model for SMEs. The proposed model involves preprocessing, feature selection, classification, and parameter tuning. At the initial stage, the financial data of the enterprises are collected and are preprocessed to enhance the quality of the data. Besides, a novel chaotic grasshopper optimization algorithm (CGOA) based feature selection technique is applied for the optimal selection of features. Moreover, functional link neural network (FLNN) model is employed for the classification of the feature reduced data. Finally, the efficiency of the FLNN model can be improvised by the use of cat swarm optimizer (CSO) algorithm. A detailed experimental validation process takes place on Polish dataset to ensure the performance of the presented model. The experimental studies demonstrated that the CGOA-FLNN-CSO model has accomplished maximum prediction

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“…In recent years, Brozyna's team has used classic regression models and other methods to predict the bankruptcy of enterprises in individual industries with relatively accurate results [20]. Ptak-Chmielewska compared the validity of LDA and SVM predictions using Polish SMEs as a case study [21]. Kitowsk's research team selected 50 companies in Poland to verify the international applicability of the traditional Logit model [22].…”

Section: Introductionmentioning

confidence: 99%

Business Bankruptcy Analysis with Explanations and Suggestions Based on Companies in Poland

Li¹

2023

Advances in Economics, Business and Management Research

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This is a study on business bankruptcy analysis and prediction. Traditionally, business bankruptcy prediction study was to build models through different methods and then verify their validity and accuracy with the actual financial information of bankrupt companies. It was still developing over the years. The purpose of this study is to find out the obvious financial ratio that affects the company's bankruptcy, and to give a detailed explanation and analysis, rather than a mathematical equation or a conclusive model. The data was the change in the financial ratios of bankrupt and non-bankrupt companies over time instead of single financial information in the year of bankruptcy. Factor analysis, binary logistic regression, and non-parametric tests were used. By looking for commonalities and priorities in financial ratios, this study speculated on the causes of changes in financial ratios and provided suggestions to managers. The experimental results showed that "Total Assets Related", "Short-term Liabilities Related", "Total Liabilities Related", and "Fixed Assets Related" were significantly different between bankrupt and non-bankrupt companies. After further analysis, the study concluded that sources of financing, the liquidity of the company, and investment were likely to be significant factors that influenced business bankruptcy. The highlight of this study was the more detailed explanation of financial ratio analysis, and its value was the method of avoiding business bankruptcy rather than the judgment of the likelihood of business bankruptcy, as most previous studies have done.

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Bankruptcy prediction of small- and medium-sized enterprises in Poland based on the LDA and SVM methods

Cited by 14 publications

References 26 publications

Design of Financial Management Model Using the Forward Neural Network Based on Particle Swarm Optimization Algorithm

Design of Financial Management Model Using the Forward Neural Network Based on Particle Swarm Optimization Algorithm

Artificial Intelligence Based Optimal Functional Link Neural Network for Financial Data Science

Business Bankruptcy Analysis with Explanations and Suggestions Based on Companies in Poland

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