TITLE:
Predicting the Financial Failures of Manufacturing Companies Trading in the Borsa Istanbul (2007-2019)
AUTHORS:
Hasan Demirhan, Güven Sayilgan
KEYWORDS:
Financial Distress, Financial Failure Prediction (FFP), Financial Ratios, Logit Regression, Market and Macroeconomic Variables
JOURNAL NAME:
Journal of Financial Risk Management,
Vol.10 No.4,
October
29,
2021
ABSTRACT: 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.