تأثیر غیرخطی نقدینگی و پرداخت بدهی بر سودآوری بانک‌ها در عراق

محورهای موضوعی : اقتصاد پولی

کیومرث شهبازی ¹ (استاد اقتصاد، دانشکده اقتصاد و مدیریت، دانشگاه ارومیه، ارومیه، ایران، (نویسنده مسئول) پست الکترونیکی: [e-mail])
محمد علی مهدی عبد ² (دانشجوی دکتری اقتصاد مالی، دانشکده اقتصاد و مدیریت، دانشگاه ارومیه، ایران، پست الکترونیکی: [e-mail])

تاریخ دریافت : 1402/07/08 تاریخ پذیرش : 1402/10/16 تاریخ انتشار : 1402/11/21

کلید واژه: سودآوری, نقدینگی, پرداخت بدهی, مؤسسات اقتصادی,

چکیده مقاله :

هدف این مقاله ارزیابی تاثیر غیرخطی شاخص‌های نقدینگی و توان پرداخت بدهی بر بازده دارایی با استفاده از روش رگرسیون انتقال ملایم پنلی در دوره ی زمانی 2020-2011 است.. شاخص‌های نقدینگی شامل نسبت دارایی‌های جاری به کل سپرده‌ها، نسبت کل وام‌های اعطاء شده به کل سپرده‌ها و نسبت کل وام‌های اعطا شده به کل دارایی‌ها بوده و شاخص‌های توان پرداخت بدهی شامل نسبت بدهی و نسبت پوشش بهره است. نتایج نشان می دهد تأثیر همه شاخص ها بر بازده دارایی‌ها غیرخطی است. مقدار و چگونگی اثرگذاری متغیرهای مستقل بر بازده دارایی‌ها در سطوح مختلف آن متفاوت است به گونه ای که اثرگذاری متغیرهای مستقل قبل و بعد از مقدار آستانه‌ای 0586/1 متفاوت است. براساس نتایج، سیاستگذاران بانکی می‌توانند برای دستیابی به سودآوری بانک‌ها براساس میزان بازده دارایی، مقدار بهینه نقدینگی را انتخاب کنند.

چکیده انگلیسی:

This paper seeks to explore the nonlinear association between liquidity indicators and debt repayment capacity in relation to asset returns. For this purpose, the Panel Smooth Transition Regression (PSTR) method was employed to examine the nonlinear impact of liquidity indicators (including the ratios of current assets to total deposits, total loans granted to total deposits, and total loans granted to total assets and debt payment capacity indicators (such as the debt ratio and interest coverage ratio) on asset returns from 2011 to 2020. The findings revealed a nonlinear effect of all calculated indicators on asset returns. The magnitude and manner of the independent variables' influence on asset returns varied at different levels, with noticeable distinctions in the effect before and after the threshold value of 1.0586. In light of these results, bank policymakers can strategically select an optimal liquidity level to enhance bank profitability based on asset returns.

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