Predicting the Financial Failures of Manufacturing Companies Trading in the Borsa Istanbul (2007-2019)
| Autor: | Hasan Demirhan, Güven Sayilgan |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Journal of Financial Risk Management. 10:416-452 |
| ISSN: | 2167-9541 2167-9533 |
| DOI: | 10.4236/jfrm.2021.104023 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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