Failure prediction for hospitality firms in US and Korea using logit and neural networks models

This study developed failure prediction models for Korean and U.S. hospitality firms using logistic regression and artificial neural networks (ANN) techniques; For Korean hospitality firms, the one-variable logit model with interest coverage ratio correctly classified 83.74% of in-sample firms and 76.32% of hold-out firms. The ratio\u27s negative coefficient suggests that low interest coverage of a firm increases its failure probability. To prevent the failures, Korean hospitality firms need to move away from heavily leveraged financial structure. The developed ANN model demonstrated an overall classification rate of 86.18% for in-sample firms and 77.63% for hold-out firms. Empirically, this study shows that the logit model is not inferior to the ANN model in terms of prediction accuracy. In addition, the logit model allows its user to interpret the coefficient of each variable and draw practical implications. Therefore, it is recommended to employ the logit model for predicting hospitality firm failures in Korea; For U.S. hospitality firms, the logit model retained three ratios: earnings before interest, tax, depreciation and amortization (EBITDA) to current liabilities (CL), quick ratio, and debt ratio. These ratios imply that, to decrease the probability of failure, U.S. firms need to: (1) exercise a tight control on the operating costs; (2) increase sales revenue by pursuing market-share gains; (3) invest in operating assets that produce higher returns than cash or marketable securities; (4) adopt a conservative financing policy. The logit model correctly classified 83.33% of the in-samples firms and 77.63% of the holdout firms. The estimated ANN model, on the other hand, demonstrated overall classification rates of 91.98% on in-sample firms and 85% on hold-out firms. While the ANN model may achieve higher classification rates, the downside is the model\u27s lack of self-explanation capabilities. The decision for model selection, therefore, should be made based on the objective of classification. If the primary objective is to classify a set of observations as accurately as possible, then the ANN model may be used. Alternatively, if the researcher wishes to make a practical interpretation of the developed model, then it is recommended to use the logit model for predicting firm failures

A Review of Bankruptcy Prediction Studies: 1930-Present

One of the most well-known bankruptcy prediction models was developed by Altman [1968] using multivariate discriminant analysis. Since Altman\u27s model, a multitude of bankruptcy prediction models have flooded the literature. The primary goal of this paper is to summarize and analyze existing research on bankruptcy prediction studies in order to facilitate more productive future research in this area. This paper traces the literature on bankruptcy prediction from the 1930\u27s, when studies focused on the use of simple ratio analysis to predict future bankruptcy, to present. The authors discuss how bankruptcy prediction studies have evolved, highlighting the different methods, number and variety of factors, and specific uses of models. Analysis of 165 bankruptcy prediction studies published from 1965 to present reveals trends in model development. For example, discriminant analysis was the primary method used to develop models in the 1960\u27s and 1970\u27s. Investigation of model type by decade shows that the primary method began to shift to logit analysis and neural networks in the 1980\u27s and 1990\u27s. The number of factors utilized in models is also analyzed by decade, showing that the average has varied over time but remains around 10 overall. Analysis of accuracy of the models suggests that multivariate discriminant analysis and neural networks are the most promising methods for bankruptcy prediction models. The findings also suggest that higher model accuracy is not guaranteed with a greater number of factors. Some models with two factors are just as capable of accurate prediction as models with 21 factors

Predicting failure in the commercial banking industry

The ability to predict bank failure has become much more important since the mortgage foreclosure crisis began in 2007. The model proposed in this study uses proxies for the regulatory standards embodied in the so-called CAMELS rating system, as well as several local or national economic variables to produce a model that is robust enough to forecast bank failure for the entire commercial bank industry in the United States. This model is able to predict failure (survival) accurately for commercial banks during both the Savings and Loan crisis and the mortgage foreclosure crisis. Other important results include the insignificance of several factors proposed in the literature, including total assets, real price of energy, currency ratio and the interest rate spread.bank failure; banking crises; CAMELS ratings

Comparative Study of Logit and Artificial Neural Networks in Predicting Bankruptcy In the Hospitality Industry

Artificial Neural Networks (ANNs) have received a great deal of attention in the area of decision support system because of their outstanding ability to forecast and classify events to make a decision This study employed Artificial Neural Networks (ANNs) to predict bankruptcy among hospitality firms and compared the performance of ANNs in predicting hospitality firms' bankruptcy to the more conventional statistical logit model. From empirical results of the two methodologies, it was shown that neural network obtained a higher accuracy rate than did a logit model in an in-sample test as well as in holdout (testing) sample test. This result confirmed previous assertions made by many researchers stating the superiority of neural network over logit models in classification and prediction tasks.Department of Nutritional Science

Three essays on the use of neural networks for financial prediction

The number of studies trying to explain the causes and consequences of the economic and financial crises usually rises considerably after a banking crisis occurs. The dramatic effects of the most recent financial crisis on the real economy around the world call for a better comprehension of previous crises as a way to anticipate future crisis episodes. It is precisely this objective, preventing future crises, the main motivation of this PhD dissertation. We identify two important mechanisms that have failed during the latest years and that are closely related to the onset of the financial crisis: The assessment of the solvency of banks along with the systemic risk over the time, and the detection of the macroeconomic imbalances in some countries, especially in Europe, which made the financial crisis evolve through a sovereign crisis. Our dissertation is made up of three different essays, trying to go a step ahead in the knowledge of these mechanisms.Departamento de Economía Financiera y ContabilidadDoctorado en Economía de la Empres

Prediction of Banks Financial Distress

In this research we conduct a comprehensive review on the existing literature of prediction techniques that have been used to assist on prediction of the bank distress. We categorized the review results on the groups depending on the prediction techniques method, our categorization started by firstly using time factors of the founded literature, so we mark the literature founded in the period (1990-2010) as history of prediction techniques, and after this period until 2013 as recent prediction techniques and then presented the strengths and weaknesses of both. We came out by the fact that there was no specific type fit with all bank distress issue although we found that intelligent hybrid techniques considered the most candidates methods in term of accuracy and reputatio

Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model

The aim of this study is to show how a Kohonen map can be used to increase the forecasting horizon of a financial failure model. Indeed, most prediction models fail to forecast accurately the occurrence of failure beyond one year, and their accuracy tends to fall as the prediction horizon recedes. So we propose a new way of using a Kohonen map to improve model reliability. Our results demonstrate that the generalization error achieved with a Kohonen map remains stable over the period studied, unlike that of other methods, such as discriminant analysis, logistic regression, neural networks and survival analysis, traditionally used for this kind of task

An Adaptive Neural Network Approach To Predict The Capital Adequacy Ratio

Financial institutions, policy makers and regulatory authorities need to implement stress tests in order to test both resilience and the consequences of adverse shocks. The European Central Bank and the European Banking Authority regularly conduct these tests, whose importance is more and more evident after the financial crisis of 2007-2008. The stress tests’ nonlinear features of variables and scenarios triggered the need of general and robust strategies to perform this task. In this paper we want to introduce an adaptive Neural Network approach to predict the Capital Adequacy Ratio (CAR), which is one of the main ratios monitored to retrieve useful information along many stress test procedures. The Neural Network approach is based on a comparison between feed-forward and recurrent networks, and is run after a meaningful pre-processing operations definition. Results show that our approach is able to successfully predict CAR by using both Neural Networks and recurrent networks

Corporation robots

Nowadays, various robots are built to perform multiple tasks. Multiple robots working together to perform a single task becomes important. One of the key elements for multiple robots to work together is the robot need to able to follow another robot. This project is mainly concerned on the design and construction of the robots that can follow line. In this project, focuses on building line following robots leader and slave. Both of these robots will follow the line and carry load. A Single robot has a limitation on handle load capacity such as cannot handle heavy load and cannot handle long size load. To overcome this limitation an easier way is to have a groups of mobile robots working together to accomplish an aim that no single robot can do alon