Creating a Comprehensive Method for the Evaluation of a Company

Horák, Jakub; Krulický, Tomáš; Rowland, Zuzana; Machová, Veronika

doi:10.3390/su12219114

Cited by 5 publications

(3 citation statements)

References 83 publications

(89 reference statements)

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“…Although classical prediction models are still used and justified, the estimate made by the artificial intelligence model may act as an indication of high potential risk, and thus, initiate a more detailed analysis to confirm the adequacy of the suspicion of financial and economic problems (Horak et al, 2020b).…”

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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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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“…Bahmani et al forecasted Iran's oil consumption using the BA with ANN [31]. Horák et al [32], applied an artificial neural network to show how financial and nonfinancial indicators play important roles in the explanation of corporate health and support a company's possibilities of reaching relevant performance levels. Vochozka et al [33], used artificial neural networks to create a methodology for the prediction of a company failure.…”

Section: Related Workmentioning

confidence: 99%

A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran

et al. 2021

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Coal as a fossil and non-renewable fuel is one of the most valuable energy minerals in the world with the largest volume reserves. Artificial neural networks (ANN), despite being one of the highest breakthroughs in the field of computational intelligence, has some significant disadvantages, such as slow training, susceptibility to falling into a local optimal points, sensitivity of initial weights, and bias. To overcome these shortcomings, this study presents an improved ANN structure, that is optimized by a proposed hybrid method. The aim of this study is to propose a novel hybrid method for predicting coal consumption in Iran based on socio-economic variables using the bat and grey wolf optimization algorithm with an artificial neural network (BGWAN). For this purpose, data from 1981 to 2019 have been used for modelling and testing the method. The available data are partly used to find the optimal or near-optimal values of the weighting parameters (1980–2014) and partly to test the model (2015–2019). The performance of the BGWAN is evaluated by mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), standard deviation error (STD), and correlation coefficient (R^2) between the output of the method and the actual dataset. The result of this study showed that BGWAN performance was excellent and proved its efficiency as a useful and reliable tool for monitoring coal consumption or energy demand in Iran.

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“…Based on factor analyses of both financial and nonfinancial metrics groups, key metrics were found; these metrics must be used by companies in almost all cases of the effectiveness verification process. According to Horák et al [76], financial and nonfinancial indicators play important roles in the explanation of corporate health and support a company's possibilities of reaching relevant performance levels. The health of a company is given not only by its financial results but also by nonfinancial results in connection to the environment [77].…”

mentioning

confidence: 99%

The Concept of Strategic Control in Marketing Management in Connection to Measuring Marketing Performance

2021

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This study deals with the issue of marketing control (the function that ties together the process of marketing management). More directly, we aim to provide a scientific reflection on the model presentation of the strategic level of such control. We present the views discussed in the literature on marketing control, which can be treated as its model conceptual and structural solutions. The main objective of the study was to define key factors in individual areas (the market area, the area of the customer’s value, the financial area) that are connected to business activities and show their interconnection. We further analyzed the relevance of marketing and business activities in connection to performance evaluation in three areas: market, customer, and financial performance. The empirical evidence of the study came from quantitative, firm-level data gathered through an email questionnaire, which yielded 708 qualified responses from companies in the Czech Republic. The analysis employed factor analysis on the way to identify the key marketing indicators supporting corporate strategy marketing in specific areas. Furthermore, Pearson’s chi-square test was used to find possible dependencies between observed factors. According to the obtained results and the application of the chosen statistic methods, we identified seven factors of which five factors were acceptable. For these five factors, we identified seven statistical dependencies. The obtained results show that companies primarily use financial indicators to monitor and check their activities in the marketing area. The usage of financial indicators in connection to the marketing area is based mainly on the traditional approach of companies in the measuring process. The whole control mechanism in the company requires continuous control of all corporate activities, with relevant changes in implementation, and a comparison of obtained results to competitors or a company’s results in previous years.

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Creating a Comprehensive Method for the Evaluation of a Company

Cited by 5 publications

References 83 publications

Artificial intelligence in predicting the bankruptcy of non-financial corporations

Artificial intelligence in predicting the bankruptcy of non-financial corporations

A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran

The Concept of Strategic Control in Marketing Management in Connection to Measuring Marketing Performance

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