2.2. Company Specific Model
Sayari and Mugan [
19] recommended creating company specific bankruptcy prediction models. They further proved that financial relations do indeed boom business features and that information content of specific fractions varies among different industries, diverging impact of industry characteristics on companies. Chen [
20], on the contrary, took an alternative approach, and in his opinion a more robust one, choosing not to create a company specific model, but rather combining financial ratio analysis, confirmatory factor analysis and logistic-regression analysis to estimate the probability of the financial failure of public corporations. Alaminos et al. [
21] created a global model with a high capacity for bankruptcy prediction. The results they obtained confirm the supremacy of global models in comparison to regional models over periods of up to three years preceding bankruptcy. In all instances, research techniques have shown some significant differences in the process of model creation, as well as in the attained results [
21]. According to Hu and Sathye [
22], a model that incorporates company-specific money related factors, company-specific non-budgetary factors, and a large-scale monetary variable is a superior indicator of fiscal misfortune than a model that incorporates just the primary arrangement of factors or a model that incorporates the last two arrangements of factors in the Hong Kong growth enterprise markets. Future research involving the use of the Apache Mahout Tool may improve bankruptcy prediction because it enables the integration of evolutionary algorithms with machine learning methods [
5]. Managers should be able to recognize the signals of financial failure in advance and comprehend future progression trends; hence, the financial path prediction model can be an active boost to the research field of financial catastrophe analysis and prediction [
6]. Vochozka [
23] adds that it is necessary for a company to be assessed in terms of its financial situation at each stage of its development. There are a lot of options. An analysis of financial indicators and various comparison methods can be used. From the point of view of interpretation, it seems advantageous to use comprehensive evaluation methods.
In general, there are a number of applications, as well as the emergence of new predictive models based on, for example, the training autoregressive model of recurrent neural networks [
24]. Lei [
25] presents a predictive model for assessing the quality of a society’s decision-making process based on deep learning algorithms. Hybrid models of neural networks used to predict financial performance based on a comparison of five methods are preferred by Khashei and Hajirahimi [
26]. Neural networks for calculating the elasticity of a company’s ability to neutralize financial risk in relation to systemic risk factors are used in the Ukrainian environment by Kolupaieva et al. [
27].
Lohmann and Ohliger [
28] answer the very question of whether the evaluation of the bankruptcy prediction model should take into account the total cost of misclassification with a positive result. Gulsoy and Kulluk [
29] point out the need to take into account differences in the size of a company and therefore the need for adjustments to the evaluation methods. The same authors also focus on the evaluation of mutual funds using the fast adaptive neural network classifier (FANNC) [
29]. Atsalakis [
30] focuses specifically on the area of emission allowance prices, creating a model based on computational intelligence techniques for their prediction, including a hybrid neurofusion controller which forms a closed-loop feedback mechanism; an artificial neural network system (ANN) and an adaptive inference system (ANFIS). In their work, Šestanović and Arenrić [
31] look for the optimal neural network for inflation prediction.
Neural networks are not always used to assess a whole company. For example, Wang et al. [
32] use deep neural networks to predict the level of film quality and especially its attendance based on trailers, including the impact on expected commercial performance. Huber et al. [
33] focus on the distribution of fast-moving consumer goods and offer a solution through a model based on data generated by machine learning and quantile regression. Aggregates of the back neural network are then used by Cao and Wang [
34] to predict stock levels. Lei [
25] creates a model for investment decision support in e-commerce based on the calculation of in-depth learning and the assessment of the state of a market participant. Extensive comparisons of the performance of hybrid machine learning and deep learning methods were performed on a credit card fraud detection model by Kim et al. [
35], whereby the latter was found to be the more accurate. Kim, Kim, and Kim [
36] also analyse the effectiveness of fraud detection by conventional methods and the method of hierarchical clusters based on deep neural networks (HC-DNN), again with the result that the newly introduced method was more accurate. Another comparison of methods of the deep learning approach for sales forecasting in the fashion industry is presented by Loureiro, Miguéis, and DaSilva [
37] who prefer deep learning to, for example, the Random Forest method.
Similarly, Wei and Cheng [
38] use fuzzy multiple attribute (FMADM) methods within the Six Sigma model to increase business quality. Mahdiraji et al. [
39] use another fuzzy (Interval Valued Intuitionistic Fuzzy) method to evaluate development projects. The same authors [
39] then evaluate the optimization processes in the form of an improved heuristic method of the Kalman algorithm. The risks of investing in oil companies are addressed through comprehensive evaluations by Li et al. [
40]. Municipal-owned companies are specifically examined by Wang and Jin [
41] using the best/worst method (BWM) to evaluate the structural risks of their diversified funding. De et al. [
42] focus on the assessment of credit risk in Chinese utility companies in the form of fuzzy sets, taking into account the mechanism of variable weight of dynamic adjustment. Another combination of methods, combining, among other things, a credit risk assessment file model that integrates multiple sampling and a fuzzy self-organizing map with multiple cores is used by Wang et al. [
43]. Fuzzy theory is also used by Wu and Zhou [
44] to identify critical risk factors for photovoltaic operators in China. In general, the use of credit risk assessment through modern assessment methods is a relatively extensive area of research, where, for example, Liang and He [
45] use the learning strategies of the AdaBoost file to construct an assessment model. A specific example is provided by Li and Chen [
46] who demonstrate how a commercial bank solves the same problem through a combination of a logical regression algorithm and a neural network. A hybrid approach combining methods using neural networks and fuzzy logic is provided by Muré, Combeerti, and Demichela [
47]. Another hybrid model that combines time series feature extraction and a deep neural network is used by Zhao, Fan, and Zhai [
48] to evaluate and predict traffic development.
Pearson’s correlation coefficient and subsequent regression methods are used in the evaluation of the Sajnóg company [
49], thereby proving the correlation of diversity between performance compensation and profitability calculated with net profit and comprehensive income. Lee, Jeong, and Woo [
50] create an evaluation system based on an integrated production planning process for shipbuilding companies in South Korea. Hanine et al. [
51] introduce Modified Delphi decision-making techniques, the fuzzy analytical hierarchical process (fuzzy-AHP) and the organizational method of ordering preferences for enrichment evaluation (PROMETHEE) in order to improve the performance of Geospatial Business Intelligence (Geospatial BI). Borges and Tan [
52] solve the problem of evaluating intangible aspects within a comprehensive assessment of a company. Eight financial and seven non-financial indicators and their evaluation using the TOPSIS method are included in the comprehensive evaluation of companies by [
53]. The size of a company with respect to its evaluation is addressed by Yang et al. [
54]; the methodology of analysis of dynamic network data packages in order to provide a comprehensive assessment (including the inclusion of CSR factors) for the insurance sector is provided by Kuo et al. [
55]. Hsu and Lee [
56] test the Random Forest method for comprehensive audits. The same method, with declared excellent results, is chosen by Petropoulos et al. [
57] for assessing the economic health of financial institutions. Random Forest optimized using a genetic algorithm with profit scores (RFoGAPS) is used by Ye, Dong, and Ma [
58] to classify credit risks. Based on a comparison of three machine learning techniques—Random Forest, K-Nearest Neighbour, and Neural Networks for the automatic classification of online outputs, Salminen et al. [
59] state that NNs perform the best with an F1 score of 70%. On the other hand, machine learning methods are not always considered more powerful. Within natural gas consumption prediction models, Qiao et al. [
60] report better results from hybrid models with a Volterra filter compared to a backlink propagation neural network. Ojstersek and Buchmeister [
61] find optimal use and comprehensive evaluation of pipeline resources in the form of data envelopment analysis (DEA) and subsequent analytical hierarchy process (AHP), with transmission efficiency and economic efficiency compromised. An index system of risk assessment for a transnational network project is created by Li et al. [
62] in order to provide reference and decision-making support to government energy sectors and investment companies. The importance of prediction and the need to constantly refine the methods is emphasized by Makridakis, Spiliotis, and Assimakopoulos [
63], who conclude in favour of hybrid or combined models. In the field of security, Gao et al. [
64] successfully apply the functional resonance analysis (FRAM) process to the evaluation of China’s security system. Through the process of analytical hierarchy, fuzzy complex evaluation, and time series prediction methods, Han et al. [
65] optimize transportation systems.
The incidence of evaluating a company with an emphasis on sustainable development and comprehensive reporting has been increasing in recent years. Yazdani et al. [
66] use for this purpose the integration of rough numerical decision experiments and evaluation laboratories (DEMATEL) and the method of multiple approximation of boundary approximation (MABAC). So-called “green” supply chains are evaluated by Wang and Li [
67] using the fuzzy orthopair methods; a comprehensive supply chain evaluation is also undertaken by Luo et al. [
68]. Castro and Chousa [
69], who consider financial analysis to be an appropriate means of assessing the financial and economic situation of a company, state that this tool should also include sustainability issues in its logic, ideally through some framework for the assessment of sustainable corporate governance and the impact of sustainability issues on financial performance. In connection with this statement, the authors propose an integrated model for financial analysis, which takes into account the social, environmental, and economic results of the company and the expression thereof using data that is quantitative and qualitative, accounting and non-accounting, physical and monetary. Macikova et al. [
70], in turn, contributed to the emergence of an integral indicator of corporate sustainability, which, through the method of financial ratios, correlation and linear regression, linked to economic added value. The results of their research show that financial performance is strongly dependent on the integrated indicator of corporate sustainability. Rita et al. [
71], by combining an integrated application of cognitive mapping and the analytical hierarchy (AHP) method, created a benchmark (so-called green index) that serves as a tool for evaluating and supporting decision-making for strategic planning in the SME sector. This index focuses on two of the main limitations of current evaluation approaches. The first limitation is the way in which evaluation criteria are defined in the assessment of the environmental performance of SMEs, while the second is the method by which the weights of the same criteria are calculated. Sustainability-oriented researchers use, among other things, company environmental performance assessments (CEPs), which they call comprehensive and consistent, with the use of fuzzy multicriteria decision-making (MCDM), which is used, for example, by Escrig-Olmedo et al. [
72]. Within this context, the area of CSR is specifically singled out by the likes of Fatma and Khan [
73], who use the theory of social identity to evaluate a company. In general, researchers in this field are looking for methods to evaluate their impact. Persecution theory, attribution theory, and qualitative research of authenticity are used by Schaefer, Terlutter, and Diehl [
74]. Over the years, managers have utilized several prediction techniques to forecast company bankruptcy. However, research is still ongoing for an accurate and more reliable prediction model. Companies themselves are vehemently interested in creating a model to describe the qualities of a potential bankrupt company. Bankruptcy prediction is crucial because it has great impact on a company’s economic strength. Political, economic, social, technological, and environmental factors, among others, are crucial to modern companies and with respect to bankruptcy.
Decades ago, logistic regression and other techniques, such as univariate and multivariate techniques, were the popular models for bankruptcy prediction [
75]. Since then, significant progress has been made in the development of prediction models. Neural networks are now more widely applied than traditional single statistical methods for model creation, generating more precise predictions on the future of companies. When predicting profitability, business analysts use such bankruptcy models. Researchers are, however, trying to innovate a single best model that will accurately predict a company bankruptcy by testing existing methods [
76]. This drive has led to extensive research into the topic. For example, Vochozka and Vrbka [
77] sought to create a prediction model through in-depth learning, specifically with the help of artificial neural networks (NN) with at least one layer of LSTM networks. They achieved their goal because a NN model was developed that is able to predict the future development of a company operating in the manufacturing sector in the Czech Republic. It can be used by small, medium-sized, and large manufacturing companies, as well as by financial institutions, investors, or auditors. It can also be used as an alternative to assess the financial health of companies in the field. Furthermore, Machová and Vochozka [
78] performed an analysis of a company in the Czech Republic using an artificial neural network and subsequently estimated the development of this branch of the national economy.
In conclusion, comprehensive company evaluation is essential for companies to verify the success of their operations management, to help improve their decision-making, and for financial sustainability [
79]. The literature reviewed shows that Neural Network (NN) models outperform and are more effective in bankruptcy prediction than other models. This is, in particular, due to the complexities of modern companies and the applicability of NN models in all industrial sectors. Based on our research, we can state that NN models in combination with other models perform better than any single model. It is recommended that in constructing an evaluation method for companies, both internal and external factors must be considered. This review is appropriate for policymakers, investors, and company auditors.
The objective of this contribution is to develop a comprehensive method for the evaluation of an industrial company that could be used for forecasting possible bankruptcy in the future.