FH&P RATING MODEL AND ITS APPLICATION WITHIN THE SLOVAK SPA ENTERPRISES

The aim of the paper is to propose a suitable structure of the newly designed Financial Health & Prediction (FH&P) rating model, and by putting it into practice in Slovak spa enterprises, to contribute to the development of financial management concepts for spa facilities operating in the field of tourism. The quantification of individual dimensions of the FH&P rating model was based on the calculation of selected ten key financial ratio indicators and prediction models. The values (in different units of measure) were converted to points using compiled transformation tables which formed the final score of the FH&P rating model and subsequently the proposed A-FX rating. Based on the results, Kúpele Bojnice, Inc. (SE03), Špecializovaný liečebný ústav Marína, s.e. (SE21) and Kúpele Nimnica, Inc. (SE07) received the best rating. This innovative model provides financial managers actual, simple and understandable overview of the financial health of a spa company and its future financial perspective. With a several adjustments, the FH&P rating model is easily applicable in any economic sector of Slovakia.

Benchmark test for calculated default probability values derived from the application of rating-based models

Subject. This article deals with the issues of validation of the consistency of rating-based model forecasts. Objectives. The article aims to provide developers and validators of rating-based models with a practical fundamental test for benchmarking study of the estimated default probability values obtained as a result of the application of models used in the rating system. Methods. For the study, I used the classical interval approach to testing of statistical hypotheses focused on the subject area of calibration of rating systems. Results. In addition to the generally accepted tests for the correspondence of the predicted probabilities of default of credit risk objects to the historically realized values, the article proposes a new statistical test that corrects the shortcomings of the generally accepted ones, focused on "diagnosing" the consistency of the implemented discrimination of objects by the rating model. Examples of recognizing the reasons for a negative test result and negative consequences for lending are given while maintaining the current settings of the rating model. In addition to the bias in the assessment of the total frequency of defaults in the loan portfolio, the proposed method makes it possible to objectively reveal the inadequacy of discrimination against borrowers with a calibrated rating model, diagnose the “disease” of the rating model. Conclusions and Relevance. The new practical benchmark test allows to reject the hypothesis about the consistency of assessing the probability of default by the rating model at a given level of confidence and available historical data. The test has the advantage of practical interpretability based on its results, it is possible to draw a conclusion about the direction of the model correction. The offered test can be used in the process of internal validation by the bank of its own rating models, which is required by the Bank of Russia for approaches based on internal ratings.

Credit Rating Model of Small Enterprises Based on Optimal Discriminant Ability and Its Empirical Study

As an important part of the national economy, small enterprises are now facing the problem of financing difficulties, so a scientific and reasonable credit rating method for small enterprises is very important. This paper proposes a credit rating model of small enterprises based on optimal discriminant ability; the credit score gap of small enterprises within the same credit rating is the smallest, and the credit score gap of small enterprises between different credit ratings is the largest, which is the dividing principle of credit rating of small enterprises based on the optimal discriminant ability. Based on this principle, a nonlinear optimization model for credit rating division of small enterprises is built, and the approximate solution of the model is solved by a recursive algorithm with strong reproducibility and clear structure. The small enterprise credit rating division not only satisfies the principle that the higher the credit grade, the lower the default loss rate, but also satisfies the principle that the credit group of small enterprises matches the credit grade, with credit data of 3111 small enterprises from a commercial bank for empirical analysis. The innovation of this study is the maximum ratio of the sum of the dispersions of credit scores between different credit ratings and the sum of the dispersions of credit scores within the same credit rating as the objective function, as well as the default loss rate of the next credit grade strictly larger than the default loss rate of the previous credit grade as the inequality constraint; a nonlinear credit rating optimal partition model is constructed. It ensures that the small enterprises with small credit score gap are of the same credit grade, while the small enterprises with large credit score gap are of different credit grades, overcoming the disadvantages of the existing research that only considers the small enterprises with large credit score gap and ignores the small enterprises with small credit score gap. The empirical results show that the credit rating of small enterprises in this study not only matches the reasonable default loss rate but also matches the credit status of small enterprises. The test and comparative analysis with the existing research based on customer number distribution, K-means clustering, and default pyramid division show that the credit rating model in this study is reasonable and the distribution of credit score interval is more stable.

CAMEL Rating System and Financial Performance of Islamic Banks in Kenya

Kenyan Islamic banks are facing a myriad of credit risk issues adversely affecting their financial performance. Importantly. CAMEL rating system has been identified as an effective approach when making credit risk management decisions. However, most of the research associating financial performance of Islamic banks has ignored issues arising in emerging market such as Kenya, a knowledge gaps this research locked. Quantitative approach was utilised while adopting correlational research design. The research had its target population as being the three (3) Islamic banks which had been operating in Kenya between the year 2012and 2020 where census was employed. The researcher compiled financial data from secondary data for between 2012 and 2020. Quantitative analysis approach was applied in the analysis to yield respective statistics; descriptive and inferential. The study concludes that; capital adequacy has a statistically significant positive, assets quality has a statistically significant negative, management efficiency has a statistically significant positive, earnings ability has a statistically significant positive, and liquidity has significant negative, effect on financial performance of Islamic banks in Kenya. Accordingly, CAMEL rating model is appropriate for assessing financial performance of Islamic bank in Kenya. The study recommends that the Kenyan Islamic bank, should employ the optimal investment strategy for capital adequacy determination, manage their assets, enhance their management efficiency capability, improve their earnings ability, and strictly adhere to recommended liquidity levels. The research recommends that Kenyan IBs should be employing CAMEL rating model on yearly basis to identify elements requiring special attention.

Moody’s Ratings Statistical Forecasting for Industrial and Retail Firms

Long-term ratings of companies are obtained from public data plus some additional nondisclosed information. A model based on data from firms’ public accounts is proposed to directly obtain these ratings, showing fairly close similitude with published results from Credit Rating Agencies. The rating models used to assess the creditworthiness of a firm may involve some possible conflicts of interest, as companies pay for most of the rating process and are, thus, clients of the rating firms. Such loss of faith among investors and criticism toward the rating agencies were especially severe during the financial crisis in 2008. To overcome this issue, several alternatives are addressed; in particular, the focus is on elaborating a rating model for Moody’s long-term companies’ ratings for industrial and retailing firms that could be useful as an external check of published rates. Statistical and artificial intelligence methods are used to obtain direct prediction of awarded rates in these sectors, without aggregating adjacent classes, which is usual in previous literature. This approach achieves an easy-to-replicate methodology for real rating forecasts based only on public available data, without incurring the costs associated with the rating process, while achieving a higher accuracy. With additional sampling information, these models can be extended to other sectors.

P128 Sleep Need is more Strongly Associated with Self-rated Health and Daytime Function than Sleep Duration

Introduction Most studies examining associations between sleep and health outcomes focus on sleep duration or efficiency, ignoring individual differences in sleep need. We investigated whether sleep need is a more influential correlate of self-rated daytime function and health than sleep duration. Methods This study is a secondary analysis of the 2019 Sleep Health Foundation online survey of adult Australians (N=2,044, aged 18–90 years). Hierarchical multiple linear regressions assessed variance explained by demographics (Model 1: age, sex, BMI), self-reported sleep duration (Model 2: Model 1 + weighted variable of weekday/weekend sleep duration), and individual sleep need (Model 3: Model 2+ how often they get enough sleep to feel their best the next day, on a 5-point scale) on daytime function items for fatigue, concentration, motivation, and overall self-rated health (EQ-5D, VAS 0–100). Results Sleep need explained an additional 17.5–18.7% of the variance in fatigue, concentration, motivation, and health rating (all p < 0.001 for R² change) in Model 3. Model 2 showed that sleep duration alone only explained 2.0–4.1% of the variance in these outcomes. Findings were similar when stratified by sex. Sleep need also explained greater variance for older adults than for younger and middle-aged adults, especially on health rating (Model 3: R² change = 0.11 for ages 18-24y, 0.14 for 45-54y, 0.27 for 75y+). Conclusions Sleep need explains more variance in daytime function and self-rated health than sleep duration. The role of sleep need on other daytime consequences, and in clinical populations, needs further exploration.

Construction and Application of the Online Finance Credit Risk Rating Model Based on the Artificial Neural Network

The low-cost, highly efficient online finance credit provides underfunded individuals and small and medium enterprises (SMEs) with an indispensable credit channel. Most of the previous studies focus on the client crediting and screening of online finance. Few have studied the risk rating under a complete credit risk management system. This paper introduces the improved neural network technology to the credit risk rating of online finance. Firstly, the study period was divided into the early phase and late phase after the launch of an online finance credit product. In the early phase, there are few manually labeled samples and many unlabeled samples. Therefore, a cold start method was designed for the credit risk rating of online finance, and the similarity and abnormality of credit default were calculated. In the late phase, there are few unlabeled samples. Hence, the backpropagation neural network (BPNN) was improved for online finance credit risk rating. Our strategy was proved valid through experiments.

Research on Credit Evaluation of Financial Enterprises Based on the Genetic Backpropagation Neural Network

In this paper, an improved neural network enterprise credit rating model, which is grounded on a genetic algorithm, is suggested. With the characteristics of self-adaptiveness and self-learning, the genetic algorithm is utilized to adjust and enhance the thresholds and weights of the neural network connections. The potential problems of the backpropagation (BP) neural network with slothful speed of convergence and the possibility of falling into the local minimum point are solved to a convinced degree using the genetic algorithm in combination. The hybrid technique of the genetic BP neural network is applied to a credit rating system. Using commercial banks’ datasets, our experimental evaluations suggest that, using a combination of the BP neural network and the genetic algorithm, the proposed model has high accuracy in enterprise credit rating and has good application value. Moreover, the proposed model is approximately 15.9% more accurate than the classical BP neural network approach.