Template-Type: ReDIF-Article 1.0 Author-Name: Rafal Balina Author-Name-First: Rafal Author-Name-Last: Balina Author-Email: rafal_balina@sggw.pl Author-Workplace-Name: Warsaw University of Life Sciences - SGGW Title: Forecasting Bankruptcy Risk in The contexts of Credit Risk Management - A Case Study on Wholesale Food Industry in Poland Abstract: The aim of the article was to identify the key factors influencing the increase in the risk of bankruptcy of companies from wholesale food industry in Poland. In addition, the research aimed to identifying the most effective method of forecasting the risk of bankruptcy of enterprises in the examined industry in the context of risk management in banks. The research showed that three key indicators determining the financial situation of the enterprise were the key for the analyzed industry: ratio of own equity and the revenue from sale; difference between the quick liquidity indicator for the industry and for the company and dynamics of company?s short term liabilities. These variables point to the areas of activity of companies from wholesale food industry, to which banks should pay special attention during the credit risk assessment process. Classification-JEL: G33, G21, G32 Keywords: bancruptcy, credit risk, food industry, neutral network, linear discriminat, logistic regresion Journal: International Journal of Economic Sciences Pages: 1-15 Volume: 7 Issue: 1 Year: 2018 Month: May File-URL: https://iises.net/international-journal-of-economic-sciences/publication-detail-1354 File-URL: https://iises.net/international-journal-of-economic-sciences/publication-detail-1354?download=1 Handle: RePEc:sek:jijoes:v:7:y:2018:i:1:p:1-15 Template-Type: ReDIF-Article 1.0 Author-Name: Hong Long Chen Author-Name-First: Hong Long Author-Name-Last: Chen Author-Email: along314@mail.nutn.edu.tw Author-Workplace-Name: National University of Tainan Title: Development of a stable corporate bankruptcy classification model: Evidence from Taiwan Abstract: This study constructs a corporate bankruptcy classification model with greater prediction accuracy that can be applied to a wide cross-section of industrial sectors. In Taiwan, development of a bankruptcy classification model for any one industry is difficult because of the small number of bankrupt companies per sector from it. Instead of using industry-relative ratios to stabilize the financial data, this study proposes an approach that combines financial ratio analysis and confirmatory factor analysis with logistic-regression analysis to estimate the probability of financial failure for public corporations. First, Mann-Whitney tests reveal a significant difference in the mean values of bankrupt and nonbankrupt companies for 41 financial ratios. Second, based on these financial ratios, a mathematical modeling procedure is used to develop bankruptcy classification model. Finally, validation of the bankruptcy model is by out-of-sample Type I accuracy, Type II accuracy, and overall correct classification rates. The research results suggest that the proposed modeling approach appears to be robust and relatively insensitive to differential industry effects and time variations. Classification-JEL: G32, G33, M10 Keywords: Bankruptcy; Financial failure; Financial management; Logit models Journal: International Journal of Economic Sciences Pages: 16-38 Volume: 7 Issue: 1 Year: 2018 Month: May File-URL: https://iises.net/international-journal-of-economic-sciences/publication-detail-1396 File-URL: https://iises.net/international-journal-of-economic-sciences/publication-detail-1396?download=2 Handle: RePEc:sek:jijoes:v:7:y:2018:i:1:p:16-38 Template-Type: ReDIF-Article 1.0 Author-Name: Wen-jhan Jane Author-Name-First: Wen-jhan Author-Name-Last: Jane Author-Email: krisenwerk@gmail.com Author-Workplace-Name: Department of Economics, Shih Hsin University Author-Name: Jia-Ling Yao Author-Name-First: Jia-Ling Author-Name-Last: Yao Author-Email: a911277@gmail.com Author-Workplace-Name: Department of Economics, National Taiwan University Author-Name: Jye-Shyan Wang Author-Name-First: Jye-Shyan Author-Name-Last: Wang Author-Email: jyeshyan@ntnu.edu.tw Author-Workplace-Name: Department of Physical Education, National Taiwan Normal University Title: Having Good Friends is a Good Thing: The Effects of Peers and Superstars on Performance in Swimming Competitions Abstract: By exploiting an unusually rich panel data set from the National Database of Student Athletes, this article addresses the issue of peer effects and superstar effects on performance in multi-stage swimming competitions. Four key findings are presented. First, the evidence from relay teams supports a positive inter-team peer effect from competitors in a male relay race, but a negative inter-team peer effect from competitors in a female relay race. Second, the evidence from both female and male relay teams shows that there exists a positive intra-team peer effect from teammates. In our estimations for female relay races, a foolish teammate does more harm (+3.11 seconds) than a brilliant opponent does good (+0.55 seconds) in the estimation of the Heckman Selection Model based on panel data. Third, a male team with average-quality swimmers performs better than a team with dispersed-quality swimmers. Fourth, for the super-team effects in these tournaments, on average, the female relay teams? (/male relay teams?) times are approximately 2.85(/2.09) seconds faster/slower when the previous year?s winning team participates. Classification-JEL: J13, J18, L83 Keywords: Heterogeneous tournaments; Multi-stage tournaments; Peer effects; Superstar effects Journal: International Journal of Economic Sciences Pages: 39-64 Volume: 7 Issue: 1 Year: 2018 Month: May File-URL: https://iises.net/international-journal-of-economic-sciences/publication-detail-1385 File-URL: https://iises.net/international-journal-of-economic-sciences/publication-detail-1385?download=3 Handle: RePEc:sek:jijoes:v:7:y:2018:i:1:p:39-64 Template-Type: ReDIF-Article 1.0 Author-Name: Tomá? Karel Author-Name-First: Tomá? Author-Name-Last: Karel Author-Email: tomas.karel@vse.cz Author-Workplace-Name: University of Economics Prague Author-Name: Petr Hebák Author-Name-First: Petr Author-Name-Last: Hebák Author-Email: hebak@centrum.cz Author-Workplace-Name: Metropolitan University Prague Title: Forecasting Czech GDP using Bayesian dynamic model averaging Abstract: Forecasting future path of macroeconomic aggregates has become crucial for monetary and fiscal policymakers. Using Czech data, the aim of this paper is to demonstrate the benefits of the Bayesian dynamic averaging and Bayesian Vector Autoregressive Models (BVAR) in forecasting real GDP growth. Estimation of richly parameterized VARs often leads to unstable estimates and inaccurate forecasts in models with many variables. Bayesian inference and proper choice of informative priors offers an effective solution to this problem by shrinking the variance of model parameters. Bayesian dynamic model averaging (DMA) then makes it possible to account for model uncertainty by combining predictive abilities of many competing VAR models considered by a researcher. Since forecasting performance of individual models may vary over time, the DMA can adapt their weights in dynamic and optimal way. It is shown that the application of DMA leads to substantial forecasting gains in forecasting Czech real GDP. Classification-JEL: E17, C10, C11 Keywords: Bayesian dynamic model averaging, Minnesota prior, Bayesian Vector Autoregressive model, GDP forecasting Journal: International Journal of Economic Sciences Pages: 65-81 Volume: 7 Issue: 1 Year: 2018 Month: May File-URL: https://iises.net/international-journal-of-economic-sciences/publication-detail-1721 File-URL: https://iises.net/international-journal-of-economic-sciences/publication-detail-1721?download=4 Handle: RePEc:sek:jijoes:v:7:y:2018:i:1:p:65-81 Template-Type: ReDIF-Article 1.0 Author-Name: Michal Mirvald Author-Name-First: Michal Author-Name-Last: Mirvald Author-Email: michal.mirvald@vse.cz Author-Workplace-Name: Department of Economics, Faculty of Economics, University of Economics in Prague Author-Name: Martina Tománková Author-Name-First: Martina Author-Name-Last: Tománková Author-Email: xtomm50@vse.cz Author-Workplace-Name: Department of Economic and Social Policy, Faculty of Economics, University of Economics in Prague Title: The Determinant of Success in Basic Economics Courses Taught by the Department of Economics at the University of Economics in Prague Abstract: In comparison with other basic courses at the University of Economics in Prague there are basic economics courses taught by the Department of Economics that regularly show higher fail rates, specifically more than 35 %. A standard evaluation should be done in time and shall use different methods. Using quantitative methods, our analysis tries to identify key determinants of students? success. The data were obtained via a questionnaire during the last lectures in the winter semester of 2016/17 and in the winter semester of 2017/18. As opposed to existing studies, we also consider variables that weren?t previously possible to observe, such as the use of IT technologies during lectures and studying from materials in electronic form. Classification-JEL: A10, A20, C20 Keywords: Economics, economics courses, education, determinant, fail rate, midterm points, test score Journal: International Journal of Economic Sciences Pages: 82-90 Volume: 7 Issue: 1 Year: 2018 Month: May File-URL: https://iises.net/international-journal-of-economic-sciences/publication-detail-1849 File-URL: https://iises.net/international-journal-of-economic-sciences/publication-detail-1849?download=5 Handle: RePEc:sek:jijoes:v:7:y:2018:i:1:p:82-90