Financial Anomalies and Creditworthiness: A Python-Driven Machine Learning Approach Using Mahalanobis Distance for ISE-Listed Companies in the Production and Manufacturing Sector
This paper investigated the effect of financial ratio anomalies on the
creditworthiness of companies in the production and manufacturing sectors
listed on the Istanbul Stock Exchange. Financial ratios are crucial in
assessing creditworthiness as they reflect potential financial risks, but
anomalies in these ratios can skew credit evaluations. The statistical analysis
is carried out using the Mahalanobis distance, a Python-driven machine learning
technique, with financial data from 254 companies over the period of 2014 to
2023. The findings show that 89.61% of the observations were identified as
normal, falling into low and moderate risk categories, whereas 10.39% were
considered anomalies or high-risk, indicating that a substantial proportion of
companies maintained financial indices within expected ranges. Besides the
study observed significant risk category variations, particularly in the latter
half of each year, likely reflecting differing reporting and auditing
practices. Farther, Principal Component (PCA) analysis indicated that Asset
Profitability, EBITDA to Total Debts, Short-Term Debt to Current Assets, Net
Sales to Short-Term Debts, and Current Ratio are the most influential financial
ratios. On the other hand, the least influential financial ratios in PCA
include Net Working Capital Turnover, Operating Expenses to Average Assets,
Market Value to EBITDA, Efficiency Ratio (Assets Turnover Ratio), and Change in
Depreciation Expenses Ratio (annual difference in depreciation expense). In
conclusion, this study underscores the complex role of financial ratio
anomalies in Turkish industries in assessing creditworthiness.
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