Financial Risk Measurement and Prediction Modelling for Sustainable Development of Business Entities Using Regression Analysis

Valášková, Katarína; Klieštik, Tomáš; Švábová, Lucia; Adamko, Peter

doi:10.3390/su10072144

Cited by 110 publications

(82 citation statements)

References 41 publications

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“…Thus, this lets us state that the modified model can be used in a very precise way and possibilities can be identified without any errors. It should be mentioned that the analysis of significance value is also supported by the fact that such scientists as Valaskova et al (2018a) also applied regression analysis to estimate the significance value. For a comparison, the significance value that we have obtained is higher than Valaskova et al (2018b) identified, but a little bit lower than established by Calabrese and Osmetti (2013).…”

Section: Reliability Of Bankruptcy Prediction For Bankrupt Companiesmentioning

confidence: 98%

A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

et al. 2019

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Different economic environments differ in their characteristics; this prevents the usage of the same bankruptcy prediction models under different conditions. Objectively, the abundance of bankruptcy prediction models gives rise to the idea that these models are not in compliance with the changing business conditions in the market and do not meet the increasing complexity of business tasks. The purpose of this study is to assess the suitability of existing bankruptcy prediction models and the possibilities to increase the effectiveness of their application. In order to analyze theoretical aspects of the application of bankruptcy forecasting models and frame the research methodology, a systemic comparative and logical analysis of the scientific literature and statistical data, graphic data representation, induction, deduction and abstraction are employed. Results of the analysis confirm research hypotheses that bankruptcy prediction models based on macroeconomic variables are effective in identifying the number of corporate bankruptcies in a country and that the application of the model created on the grounds of macroeconomic indicators together with the traditional bankruptcy prediction model can improve the reliability of bankruptcy prediction. However, it was identified that models which are not specially adapted to companies in the construction sector are also suitable for forecasting their bankruptcies.

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Section: Reliability Of Bankruptcy Prediction For Bankrupt Companiesmentioning

confidence: 98%

A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

et al. 2019

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“…Financial institutions evaluate the sustainability of the borrowers by using the corporate financial distress prediction model. That is, the corporate financial distress prediction model plays an essential role for risk management of the financial institutions [1,2]. As a result, corporate financial distress prediction has long been an important research topic in finance and management literature.…”

Section: Introductionmentioning

confidence: 99%

“…Although SVMs have many advantages, the application of SVM is not quite simple because it requires the proper setting of some design factors. For example, selecting an appropriate kernel function and its parameters (e.g., C, d, σ 2 ) and selecting an appropriate feature subset in SVMs may substantially affect the prediction performance. In addition, selecting proper instance selection (that is, prototype selection) may also improve the classification accuracy by eliminating irrelevant and distorting training samples.…”

Section: Introductionmentioning

confidence: 99%

Predicting Corporate Financial Sustainability Using Novel Business Analytics

2018

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Measuring and managing the financial sustainability of the borrowers is crucial to financial institutions for their risk management. As a result, building an effective corporate financial distress prediction model has been an important research topic for a long time. Recently, researchers are exerting themselves to improve the accuracy of financial distress prediction models by applying various business analytics approaches including statistical and artificial intelligence methods. Among them, support vector machines (SVMs) are becoming popular. SVMs require only small training samples and have little possibility of overfitting if model parameters are properly tuned. Nonetheless, SVMs generally show high prediction accuracy since it can deal with complex nonlinear patterns. Despite of these advantages, SVMs are often criticized because their architectural factors are determined by heuristics, such as the parameters of a kernel function and the subsets of appropriate features and instances. In this study, we propose globally optimized SVMs, denoted by GOSVM, a novel hybrid SVM model designed to optimize feature selection, instance selection, and kernel parameters altogether. This study introduces genetic algorithm (GA) in order to simultaneously optimize multiple heterogeneous design factors of SVMs. Our study applies the proposed model to the real-world case for predicting financial distress. Experiments show that the proposed model significantly improves the prediction accuracy of conventional SVMs.

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“…Such credits should be big (up to 80% of the building cost), long (up to 10 years) and with the lowest possible interest (in order to cheapen the production). T his is the reason, why many countries (for example, Italy, and primarily -Genoa; Finland and Malta) have specifi c state programs for the support of this cluster, which allows them to heighten the global competitiveness, and to support and actively develop the shipbuilding cluster even in times of crisis, reduce fi nancial risks [ 19] [20]. As we know, Russia suff ers from the lack of "cheap and long money", which means that here you can only take a credit for shorter term and for bigger interest than abroad, which, in its turn, makes the national shipbuilding more expensive in comparison to its foreign competitors.…”

Section: The Current State Of the Shipbuilding Industry In Russia Andmentioning

confidence: 99%

The Positions of Russia and Croatia Shipbuilding Products on World Markets and Prospects of Co- Operation (Analytical Overview)

Tulyakova¹,

Dengov²,

Gregová³

2019

Naše more

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Both Russia and Croatia are the great seafaring nations with glorious past, complicated present and, hopefully, a rewarding future. The countries that historically had long sea borders are almost destined to become the global shipbuilding centres. Since the ancient times, the marine shipping routes have connected the most distant nations, countries and entire continents through trade and economic relations. At the same time, the high seas were also a place for the constant battles between the fl eets of the major seafaring nations. Thus, both civil and naval shipbuilding was and will always be of utmost importance for any country with a sea access. The product of this industry stays in demand at any time and under any circumstances-both during the periods of growth and the times of crises. The shipbuilding is subject to the same kind of diffi culties as the economy as a whole, but due to the technological specifi cs of this industry it has some additional specifi c problems. This article deals with the comparative analysis of the recent and current state of the shipbuilding industries of Croatia and Russia. We tried to fi nd out the problems that are similar for the shipbuilding industries of both countries, as well as their national particularities. The main diffi culty for the comparative analysis was the fact that while the major part of the Croatian shipbuilding is civil, the Russian industry is predominantly naval. However, it does not preclude the existence of common problems, for the research of which the comparative analysis could be a useful tool. Sažetak I Rusija i Hrvatska poznate su pomorske zemlje sa slavnom pomorskom tradicijom, složenim okolnostima u današnjim vremenima i, nadajmo se, budućnošću koja obećava. Zemlje koje imaju dugu obalu gotovo neizbježno postaju svjetska središta brodogradnje. Od davnih su vremena putovi prijevoza morem povezivali najudaljenije narode, zemlje i cijele kontinente s pomoću trgovine i ekonomskih odnosa. Istovremeno, oceani su predstavljali mjesta stalnih sukoba između fl ota najvećih pomorskih nacija. Stoga je i civilna i ratna brodogradnja bila i bit će uvijek najznačajnija za svaku zemlju koja ima izlaz na more. Uvijek postoji potražnja za proizvodima ove industrije-i u vrijeme rasta i u vrijeme krize. Brodogradnja je izložena jednakim problemima kao i ekonomija u cjelini, ali zbog svojih tehnoloških posebnosti ova industrija ima dodatne specifi čne probleme. U ovome radu daje se komparativna analiza stanja brodograđevnih industrija u Hrvatskoj i Rusiji kakve su bile u posljednje vrijeme i kakve su danas. Pokušalo se otkriti probleme u brodograđevnoj industriji koji su slični u objema zemljama, kao i različitosti. Najveća prepreka u komparativnoj analizi bila je činjenica da je većina brodogradnje u Hrvatskoj za civilne potrebe, dok je u Rusiji pretežito za vojne potrebe. Međutim, time se ne isključuju zajednički problemi, a ova komparativna analiza može poslužiti boljem istraživanju tih problema. KEY WORDS civil shipbuilding military shipbuilding branch problems s...

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Financial Risk Measurement and Prediction Modelling for Sustainable Development of Business Entities Using Regression Analysis

Cited by 110 publications

References 41 publications

A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

Predicting Corporate Financial Sustainability Using Novel Business Analytics

The Positions of Russia and Croatia Shipbuilding Products on World Markets and Prospects of Co- Operation (Analytical Overview)

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