Financial distress prediction is a key challenge every financing provider faces when determining borrower creditworthiness. Inherent opaqueness of Small and Medium Enterprise business complicates credit decision making process, therefore increasing cost to finance and lowering probability of receiving funds. This paper used data on 12.000 SMEs to estimate binomial classifiers for financial distress prediction using Logistic Regression, Artificial Neural Networks and Random Forest techniques. Classical financial ratios were used to estimate initial single-period predictors, which were later enhanced with time, credit history and age factors to retrieve multi-period models. Contrary to other studies, financial distress is understood as a significant challenge to company’s ability to cover liabilities rather than probability to go bankrupt. Highest prediction accuracy was reached using Random Forest algorithm with additional factors. It was concluded that period-at-risk adjustment is necessary to ensure highest financial distress prediction accuracy.
Economic (sometimes called "business") cycle research is one of the most popular topics of scientific literature discussions over the last years encompassing global economy long-term grow and recession starting from 2007. Such cycles can also be observed in banking activities-decreasing crediting volumes can be noticed in the majority of countries. However, the interaction between financial and business cycles is not fully revealed and differs in different countries. In the case of Lithuania credit volume and business activeness cyclic interaction can be also named as specific, reflected in gross domestic product (GDP) and credit volume fluctuations. Based on various economic cycle stages identification methodologies, not every single fluctuations can be assigned as an economic cycle stage: some of them are not identified as significant and are not recognised as economic cycles, the others match an economic cycle stage and time criterions and can be recognized as economic cycles. In the current situation, when global economy and the situation in Lithuania show recovery signs from recession to growth, credit market reacts respectively. Though the question of business and credit volume cycles is very actual, because knowing credit market dynamics indications and synchronization level between credit and economic cycles different financial stability implementation politics measures can be developed. The importance of business and credit volume cycles interaction research is also evident from the number of theoretic studies, however, to investigate interaction between an economic cycle and crediting activities in Lithuania banks there was adopted methodology developed by Kress (2004) and Avouyi-Dovi et al. (2006), including these main stages: 1) identification of turning points according to Avouyi-Dovi et al. (2006) adopted Harding and Pagan methodology, identifying peaks and troughs when two different economic cycle indicators are selected (Industrial production (Lt) and GDP (Lt)) with three credit cycle indicators (Total loans (Lt); Household loans (Lt); Loans to non-financial corporations (Lt)); 2) Calculation of concordance index between economic cycle and banks provided credit volume using business and credit cycle indicators; 3) Valuation of dependence between economic indicators and banks credit activities ratios using correlation function; 4) Conclusions delivering. Research methodology consists of separate independent stages, what makes possible to compare the results in separate stages and deliver more comprehensive conclusions. Obtained results revealed that peak in Total loans indicator converge with the peak in economic cycle indicators, but the peak in Household loans is accessed earlier than the peak in economic cycle indicators. These tendencies were also approved by the correlation analysis and calculation of a conformity indicator. The results allow to deliver significant conclusions about government monetary political decisions influencing the country's economic cycle fluctuations and determining fi...
Public debt has diverse effects on GDP varying from country to country and resulting from a number of different factors. This project is dedicated to research the effects of various macroeconomic indicators on GDP, with an emphasis on debt related predictors, using a multiple linear regression model. Findings of this research confirm the hypothesis that country determinants influence the efficiency of public borrowing and its effect on GDP. Surprisingly, no relation between debt crisis, level of government debt and its effect on GDP could be found. On the contrary, private borrowing showed a positive effect on the economy in every country where it resulted statistically significant. Interesting results were achieved concerning the openness of the economy and foreign direct investment. They were unequal, whereas initially supposed to be mostly positive
The purpose of this study was to determine the level of service quality from the point of customers' view using the services of institutions offering social insurance, and also to present how important is to measure service quality at insurance market. SERVQUAL method was applied with a seven-point Likert scale. The SERVQUAL method also took into account the extended minimum size. The study was conducted among the customers of the Social Insurance Institution (ZUS) and the Agricultural Social Insurance Fund (KRUS) in northeastern Poland. For five tested areas (tangibles, reliability, responsiveness, assurance, empathy) 20 questions were assigned. Actual, expected, and minimum quality of provided services was determined. Additionally, the gaps were defined which allowed organizations to evaluate the service in terms of efficient and effective customer service policy. The gaps identify the areas on which the company should pay a particular attention. According to the respondents, assurance area plays a major role among the dimensions mentioned. However, the research shows that empathy area was rated as the highest by the respondents. In turn, customers have the highest expectations in relation to the area of assurance. The biggest gap was observed in the dimension of responsiveness. In general it can be stated that quality of insurance services provided by the surveyed institutions were assessed at the good level. In many areas, the amendments should be introduced indicated in the next part of the article. Moreover, customers were grouped by demographic and social variables, depicting that older and better educated customers are more satisfied with the insurance service quality. Scientific contribution of the authors is preparation of the research questionnaire measuring the quality of insurance services that can be used by studied institutions to make good decisions. The conclusion briefly describes the potential for appication of customer satisfaction research and customer service strategies in the social insurance sector. Due to the study carried out, market institutions can properly identify customer needs in the services they provide and see occurring discrepancies.
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