Financial Failure Prediction Using Financial Ratios: An Empirical Application on Istanbul Stock Exchange (original) (raw)
Related papers
2011
This study examined sixty-four (64) listed companies over a period of ten years using a classic univariate method. Most studies on failure and bankruptcy predictions in the past forty years or more have been dominated by various multivariate statistical methods or some form of artificially intelligent systems. This study however, showed that the predictive powers of the individual ratios used individually and independently of each other has produced highly successful results. The means of the ratios showed significant differences between the companies that failed and those that were non-failed. A dichotomous classification test performed on the holdout sample using the cut-off point obtained from the analysis sample showed average classification accuracy of between 79% and 84%. One ratio, the Cash Flow to Total Debt perform particularly well with correct classification results of failed companies of between 81%% and 94% for both the analysis and the holdout sample and for all the fo...
Journal of Financial Risk Management, 2021
This study aims to develop financial failure prediction (FFP) models by utilizing the firm-specific financial ratios and variables related to the stock market and macroeconomic indicators for Turkish manufacturing corporations, which traded stocks on the Borsa Istanbul between 2007 and 2019. The statistical methodology utilizes binary logit analysis to construct FFP models for less restrictive assumptions and the most relevant independent variables every three years before the financial failure. Model scores are built for the sector groups: "Production and Manufacturing", "Trade and Transportation", and "IT and Administrative Services". Companies data are further divided into two subsets for each sector: training (60% samples) and test models (40%). After the factor analysis exercise performed at the initial stage, liquidity, leverage, and profitability ratios are found to be the important financial factors in the model predictions. Besides, macroeconomic and stock market variables such as non-performing loans-to-total loans ratio, loan interest rates, and BIST industrial index are also observed to be critical factors in the financial failure prediction model. In the next stage and subsequent to the application of the stepwise logistic method, the reduced financial ratios regarding the leverage and profitability along with only the Borsa Istanbul industrial index are observed as the most effective contributive variables in predicting an accurate model before one, two, and three-year prior to the financial failure in across the three sub-sectors. The test sample's predictive power strongly validates the high classification results obtained from the trained model within each sub-sector.
International Journal of Science and Research (IJSR), 2019
Corporate failure models can be broadly classified into two groups: quantitative models, which are mainly based on published financial information and qualitative models, which are based on an internal assessment of the company concerned. Both types attempt to determine characteristics, whether financial or non-financial, which can then be used to distinguish between surviving and failing companies. This study sought to predict companies that are potential to corporate financial distress utilizing the Altman Z-Score model and current ratio. Forty-five companies currently listed on the Philippine Stock Exchange were randomly selected, and their corresponding audited financial statements published online were downloaded and assessed using the financial ratios. Findings of the study revealed, 35 companies have been chosen potential for becoming financially distressed when subjected to the Altman Zscore model. On the other hand, 12 companies were also found experiencing financial difficulties based on current ratio analysis. Accordingly, these companies have classified either failure or non-failure based on Altman and current ratio. The study concluded both the Altman Z-Score Model and current ratio are financial analytical tools investors and financial analysts can use in assessing the financial health of the company before investing or buying the stock. For businesses having low or declining Z-index, requires an indepth analysis of the accounts in the financial statements to verify the cause of the problem or potential risk.
Predictive capability of Financial Ratios for forecasting of Corporate Bankruptcy
Bankruptcy of a business firm is an event which results substantial losses to creditors and stockholders. A model which is capable of predicting an upcoming business failure will serve as a very useful tool to reduce such losses by providing warning to the interested parties. This was the main motivation for Beaver (1966) and Altman (1968) to construct bankruptcy prediction models based on the financial data (Deakin 1972). This research study also initiated with a great interest on this subject to investigate the predictive capability of financial ratios for forecasting of corporate distress and bankruptcy events. This study is expounded on similar previous studies by Altman (1968), Ohlson (1980), Beaver (1966) by examining the effectiveness of financial ratios for predicting of corporate distress. The logistics regression analysis (LRA) statistical method is used to scan the risk factors from the previous financial year data and prediction models are constructed which can reasonably classify the expected bankruptcy group and can reasonably predict the solvency status of a firm. The research has been focused on the USA companies only. A set of bankrupted and non-bankrupted company financial data are used for constructing the bankruptcy prediction model and then a second set of bankrupted and non-bankrupted company financial data has been used to test the classification accuracy of the constructed models. The result of this study is consistent with the previous bankruptcy prediction researches outcomes. This study also investigates the time factor implication of bankruptcy prediction models using 5 years financial ratios. Like other research projects this project is not without certain limitations and weaknesses. The bankrupted company data collection and compilation was a great challenge due to most of the bankrupted companies cease to operate or cease to be existed. Thanks to the great treasure of Mergent online database which facilitated collection of bankrupted company data. In order to facilitate identifying and collecting bankrupted company data, it is presumed that the companies which show as inactive status in Mergent online database are distressed or bankrupted companies. Another practical obstacle was the functionality of SPSS software and the output interpretation of the SPSS software; I used Andy Field's "Discovering Statistics using SPSS" book to decipher the statistical jargons and to formulate the bankruptcy equations. Our constructed prediction model cannot be used universally as the study depended upon exclusively on US firm's financial data, therefore the constructed prediction model can proved to be very useful tool for the US financial analysts and turnaround specialists to identify the distressed firms. In the case we need to use this model in other geographical location, the coefficients of the predictor variables must be re-estimated using the particular country's financial data.
The value of financial ratio analysis in predicting the failure of JSE listed companies
2014
The objective of this study investigated the successful prediction of business failure of JSE listed companies using financial ratio analysis. During the research, financial statement data of failed and non-failed JSE listed companies during 2007-2012 financial periods were analysed, compared and interpreted. The interpretation of the trends and comparisons is of a quantitative nature, together with a qualitative genre which examines the tables, figures and equations in order to get the entire picture of the company's performance for a five year period. The combination of literature on various failure predictor models and experience of these models resulted in the development of a modified model. The conclusion from the study indicated that financial ratio analysis successfully predicts failure and non-failure of the 16 companies that were investigated. These companies were grouped into eight delisted (failed) and eight listed (non-failed) JSE companies, which were paired in accordance to industry, fiscal period and closest asset size. The adoption of the traditional ratio analysis methods and EMS model yielded some interesting findings. The traditional ratio analysis methods (trend and comparative ratio analysis) were used with the Emerging Market Score (EMS) Model. The outcomes indicated the traditional methods are viable company failure prediction tools and the EMS model points out companies at a score of 2.60 and above as being financially stable. Between 2.60 and 1.10 the results are not very dependable because it is known that the company is in distress, yet uncertain whether the company has financially failed and below 1.10 the company has failed. It was concluded that a combination of the various prediction models enhances the accuracy of failure prediction. v Therefore further research is required to assist stakeholders of South African companies to predict business failure by developing an adjusted model in a South African context. vi TABLE OF CONTENTS
The Financial Ratio Analysis in Predicting the Conditions of Financial Distress
Almana : Jurnal Manajemen dan Bisnis
Financial distress is a stage of decline in a company's financial condition that occurs before bankruptcy or liquidation. The Indicators of financial distress from results of the test scores using financial ratios, financial ratios are figures obtained from results comparisons between one financial statement item and another that have a relevant and significant relationship. The purpose of this study is to examine the effect of financial ratios to predict financial distress on manufacturing companies in Indonesia Stock Exchange. The research population was all manufacturing companies listed on the Indonesia stock exchange, period 2015-2019. The research sample used the purposive sampling technique. The data analysis method used logistic regression analysis. The results showed liquidity, leverage, and activity profitability, respectively simultaneously affect financial distress. Partially profitability has a positive effect on financial distress. Liquidity, leverage, and activity...
Predicting business failures in non-financial turkish companies
2015
Cataloged from PDF version of article.The prediction of corporate bankruptcies has been widely studied in the finance literature. This paper investigates business failures in non-financial Turkish companies between the years 2000 and 2015. I compare the accuracies of different prediction models such as multivariate linear discriminant, quadratic discriminant, logit, probit, decision tree, neural networks and support vector machine models. This study shows that accounting variables are powerful predictors of business failures one to two years prior to the bankruptcy. The results show that three financial ratios: working capital to total assets, net income to total assets, net income to total liabilities are significant in predicting business failures in non-financial Turkish companies. When the whole sample is used, all five models predict the business failures with at least 75% total accuracy, where the decision tree model has the best accuracy. When the hold-out samples are used, n...
Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, 2018
The purpose of this study is to establish a reliable model for predicting the financial failures of the Technology Sector enterprises traded in the BIST 1 year in advance. For this purpose, the previous studies on financial failure and predicting financial failure were examined first, then the concept of financial failure and its reasons were discussed. Then the theory of financial ratios and the use of logistic regression analysis techniques in predicting financial failure are explained theoretically. In the last part, the financial ratios of the companies in the Technology Sector traded in the BIST and the financial success and failure categories of the companies were analyzed and evaluated. As a result of the evaluation, it was determined that the financial success situations of the enterprises increased estimation power 1 year ago.
Investment Management and Financial …, 2009
The main aim of this study is to develop a financial failure prediction model that can be utilized by all actors in the economy. As a financial failure assumption, we consider Turkish Bankruptcy Law article 179 pursuant to Turkish Trade Law articles 324 and 434, and negative equity value. The study is conducted using 53 financial ratios extracted from financial statements of industrial companies listed on the ISE (Istanbul Stock Exchange) during economic crises between November 2000 and February 2001; and follows four main steps. In the first step one-way ANOVA test is conducted to financial ratios which are compiled from previous central studies and Turkish independent investment investigation company, to define how financial ratios differentiate between distressed and non-distressed firms. Then in the second step, discriminant analysis and logistic regression analysis are applied to those selected ratios. In the third step factor analysis is conducted to find out if the models measure different corporate characteristics, and in the conclusion both models are combined to construct an objective early warning system.
FINANCIAL DISTRESS PREDICTION ON PUBLIC LISTED BANKS IN INDONESIA STOCK EXCHANGE
The 3rd International Congress on Interdisciplinary Behavior & Social Science 2014
A financial distress of company should be able anticipated smartly by its management to rerun the business without having any loss due to business failure. Thus, we need a model which could provide an early signal to company the probability of financial distress so that remedial efforts can be run immediately. This study aims to explore CAMEL’s ratio as an early classificator, and also to reexamine the capacity of CAMEL ratio as a predictor of banks distress. Using a logit binary to classified the probability of distress and non-distress, then multiple regression to determines the ability of financial ratios as a predictor of distress issuers which obtained the following results: a) An exploration CAMEL ratios as an early classificator resulting high classification capacity with a range of 78.7%-91.4%, Furthermore, when CAMEL ratio were used as a predictors, still resulted a high of capability to classify samples accurately by 82.4%.