scholarly journals Selected Methods of Predicting Financial Health of Companies: Neural Networks versus Discriminant Analysis

Information ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 505
Author(s):  
Jarmila Horváthová ◽  
Martina Mokrišová ◽  
Igor Petruška
Keyword(s):  
Discriminant Analysis ◽  
Financial Distress ◽  
Empirical Studies ◽  
Confusion Matrix ◽  
Point Of View ◽  
Brier Score ◽  
Financial Health ◽  
Feed Forward Neural Network ◽  
Error Type ◽  
Suitable Alternative

This paper focuses on the financial health prediction of businesses. The issue of predicting the financial health of companies is very important in terms of their sustainability. The aim of this paper is to determine the financial health of the analyzed sample of companies and to distinguish financially healthy companies from companies which are not financially healthy. The analyzed sample, in the field of heat supply in Slovakia, consisted of 444 companies. To fulfil the aim, appropriate financial indicators were used. These indicators were selected using related empirical studies, a univariate logit model and a correlation matrix. In the paper, two main models were applied—multivariate discriminant analysis (MDA) and feed-forward neural network (NN). The classification accuracy of the constructed models was compared using the confusion matrix, error type 1 and error type 2. The performance of the models was compared applying Brier score and Somers’ D. The main conclusion of the paper is that the NN is a suitable alternative in assessing financial health. We confirmed that high indebtedness is a predictor of financial distress. The benefit and originality of the paper is the construction of an early warning model for the Slovak heating industry. From our point of view, the heating industry works in the similar way in other countries, especially in transition economies; therefore, the model is applicable in these countries as well.

scholarly journals Comparison of the Results of a Data Envelopment Analysis Model and Logit Model in Assessing Business Financial Health

Information ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 160
Author(s):  
Jarmila Horváthová ◽  
Martina Mokrišová
Keyword(s):  
Data Envelopment Analysis ◽  
Logit Model ◽  
Estimation Accuracy ◽  
Data Envelopment Analysis Model ◽  
Type I ◽  
Data Envelopment ◽  
Financial Health ◽  
Error Type ◽  
Suitable Alternative ◽  
Dea Model

This paper focuses on business financial health evaluation with the use of selected mathematical and statistical methods. The issue of financial health assessment and prediction of business failure is a widely discussed topic across various industries in Slovakia and abroad. The aim of this paper was to formulate a data envelopment analysis (DEA) model and to verify the estimation accuracy of this model in comparison with the logit model. The research was carried out on a sample of companies operating in the field of heat supply in Slovakia. For this sample of businesses, we selected appropriate financial indicators as determinants of bankruptcy. The indicators were selected using related empirical studies, a univariate logit model, and a correlation matrix. In this paper, we applied two main models: the BCC DEA model, processed in DEAFrontier software; and the logit model, processed in Statistica software. We compared the estimation accuracy of the constructed models using error type I and error type II. The main conclusion of the paper is that the DEA method is a suitable alternative in assessing the financial health of businesses from the analyzed sample. In contrast to the logit model, the results of this method are independent of any assumptions.

Risk of Business Bankruptcy

2022 ◽  
pp. 148-177
Author(s):  
Jarmila Horváthová ◽  
Martina Mokrišová
Keyword(s):  
Logit Model ◽  
Correlation Matrix ◽  
Heat Supply ◽  
Prediction Models ◽  
Empirical Studies ◽  
Business Owners ◽  
Financial Health ◽  
Suitable Alternative ◽  
Financial Failure ◽  
Dea Method

Recently, the demand of business owners to ensure the sustainability of their businesses has come to the fore. It results in a focus on identifying the risks of businesses' financial failure. Several prediction models can be applied in a given area. Which of these models is most suitable for Slovak companies? The aim of this chapter was to point out the possibility of applying the DEA method in measuring the financial health of companies and predicting the risk of their possible bankruptcy. The research was carried out on a sample of companies operating in the field of heat supply. The indicators were selected using related empirical studies, a univariate Logit model, and a correlation matrix. In this chapter, two main models were applied: the DEA model and the Logit model. The main conclusion of the paper is that the DEA method is a suitable alternative in assessing businesses' financial health.

scholarly journals Determinants of Corporate Anti-Corruption Disclosure: The Case of the Emerging Economics

2021 ◽  
Vol 13 (6) ◽  
pp. 3462
Author(s):  
Maider Aldaz Odriozola ◽  
Igor Álvarez Etxeberria
Keyword(s):  
Empirical Studies ◽  
Geographical Area ◽  
Control Variable ◽  
Emerging Countries ◽  
Point Of View ◽  
Press Freedom ◽  
Negative Effects ◽  
Key Factor ◽  
Using Data ◽  
Positive Effect

Corruption is a key factor that affects countries’ development, with emerging countries being a geographical area in which it tends to generate greater negative effects. However, few empirical studies analyze corruption from the point of view of disclosure by companies in this relevant geographical area. Based on a regression analysis using data from the 96 large companies from 15 emerging countries included in the 2016 International Transparency Report, this paper seeks to understand what determinants affect such disclosure. In that context, this paper provides empirical evidence to understand the factors that influence reporting on anti-corruption mechanisms in an area of high economic importance that has been little studied to date, pointing to the positive effect of press freedom in a country where the company is located and with the industry being the unique control variable that strengthens this relationship.

Are Disasters Disastrous to Nonprofit Organizations? Investigating the Financial Implications of Hurricane Sandy for Nonprofits

2021 ◽  
pp. 089976402199845
Author(s):  
Xintong Chen
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Nonprofit Organizations ◽  
Financial Distress ◽  
Logistic Regression Model ◽  
Hurricane Sandy ◽  
Financial Health ◽  
External Funds

Nonprofit organizations are sensitive to external disasters due to their high reliance on external funds and volunteers. In this study, I investigate how disasters affect the financial health of nonprofits and what factors make them more vulnerable within the context of disaster. The sample in this study includes nonprofits directly and indirectly affected by Hurricane Sandy. Using a logistic regression model, I explore if the disaster contributed to the likelihood of a nonprofit experiencing financial distress. Disaster, as an external shock, increases risks of nonprofits experiencing financial distress, especially for smaller nonprofits and nonprofits not relying on commercial revenue.

scholarly journals Valuing the Future, Discounting in Random Environments: A Review

2021 ◽  
Author(s):  
Jaume Masoliver ◽  
Miquel Montero ◽  
Josep Perelló ◽  
J. Doyne Farmer ◽  
John Geanakoplos
Keyword(s):  
Empirical Studies ◽  
Market Price ◽  
Point Of View ◽  
Random Environments ◽  
Long Run ◽  
Price Of Risk ◽  
Future Discounting ◽  
Pricing Theory ◽  
Risk Neutral Measures ◽  
Far Future

We address the process of discounting in random environments which allows to value the far future in economic terms. We review several approaches to the problem regarding different well-established stochastic market dynamics in the continuous-time context and include the Feynman-Kac approach. We also review the relation between bond pricing theory and discount and introduce the market price of risk and the risk neutral measures from an intuitive point of view devoid of excessive formalism. We provide the discount for each economic model and discuss their key results. We finally present a summary of our previous empirical studies on several countries of the long-run discount problem.

scholarly journals Using Piotroski F-Score for Assessing Financial Health: Evidence from Leading Indian Private Banks

2021 ◽  
Vol 8 (S1-Feb) ◽  
pp. 117-132
Author(s):  
S Rangapriya ◽  
J Meenakumari
Keyword(s):  
Financial Distress ◽  
Investment Decision ◽  
Relative Strength ◽  
Valuable Insight ◽  
Financial Health ◽  
Investment Decision Making ◽  
Financial Strength ◽  
Private Banks ◽  
Level Analysis

This research investigates the efficacy of Piotroski F-score to screen firms with good financial health and to identify early signs of financial distress in Indian banking stocks. This study complements existing empirical evidence which indicate that the venerable model can provide valuable insight for investment decision making and risk management.The evidence is drawn from valuation signals across leading private banks in India for a period ranging from 2014-2020. Piotroski F-score evaluates companies with a discrete number between zero and nine, the score facilitates determination of financial strength of the company. Higher score indicates better financial health and viceversa. The F-score is calculated as a sum of criteria which evaluates profitability signals, leverage and liquidity, sources of funds and operating efficiencies. In this study, each of these ratios have been analyzed to gain valuable insight on the banks (company-level). Analysis of Variance (ANOVA) of various ratios, ascertains intensity of relationship across banks (industry-level). This can help manage exposure in the portfolio as per the economic environment.The Piotroski F-score evaluates the generic financial health of the firm and indicates the direction in which the firm is headed. By studying individual factors, relative strength can be assessed. Piotroski F-score ranged between 0-7 for all the banks under study, indicating that none of them were a ‘compellingbuy’ (score 8 or 9) over the seven-year horizon. Some banks have consistently shown depleting F-score over at least 3 years, this can be interpreted as a signal of financial distress. It is evident that consistent monitoring of F-score empowers pro-active risk management.This work attempts to introduce Piotroski F-score as an integral valuation metric in evaluating Indian banking stocks. F-score can be used for initial screening, it’s consistent monitoring can facilitate optimized returns at risk-adjusted levels.

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