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Analysis of Credit Loss Provisions According to Micro and Macro Variables: Turkish Deposit Banks


Author: Nuray İslatince*
Faculty of Business Administration Department of Accounting and Finance Anadolu University, Eskişehir, Turkiye.
Published Date: 2024-02-15
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Keywords: Loan Loss Provisions, Banking Sector, Income-Expense Structure, GDP, Factor Analysis
Abstract:
Banks bring together those with surplus funds and those in need of funds within the financial system, thereby fulfilling their core functions with the right place, timing, and terms. Banks reintroduce the collected deposits to the system through granted loans while also allocating specific proportions of loan loss provisions to protect themselves from the undertaken risks. Therefore, they take precautions against possible credit losses by reflecting the value losses that may be faced in their financial statements in advance. Given the significance of financial institutions within the economic framework, this 'caution' holds paramount importance in ensuring the stability of both the sector and the broader economy.This study aims to reveal the relationship of the determined micro and macro variables on the loan loss provisions (LLPMB) levels through factor analysis. For the analysis, data for the period 2018-2022 of the top 10 largest deposit banks operating in Turkey in terms of their asset sizes were used. The factor structure of the factor analysis was first determined. The independent variables were observed to combine into 4 factors in a very strong structure. The impact of these factors on loan loss provisions (LLPMB) levels was then associated with an established model. The values and assumptions of all analyses were found to be quite strong and sufficient. It can be concluded from the results of this study that the variables generally had individual effects on LLPMB levels. In addition, these parameters were observed to have a combined effect by creating a high level of consistency, except for the macro data. The impact at the individual level is important. However, in the real market, where there are many measurable or unmeasurable parameters, revealing the combined effect of such variables may enhance the effectiveness of the predictions.