شناسایی متغیرهای غیرشکننده اقلام تعهدی بر اساس رویکردهای بیزین غیرخطی در بازار سرمایه ایران
محورهای موضوعی : اقتصاد سنجی مالی و روش‌های کمّی
اعظم رضائیان جویباری
1
,
جمادوردی گرگانلی دوجی
2
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,
مجید اشرفی
3
,
علی خامکی
4
1 - گروه حسابداری، واحد علی‌آباد کتول، دانشگاه آزاد اسلامی، علی‌آباد کتول، ایران.
2 - گروه حسابداری، واحد علی‌آباد کتول، دانشگاه آزاد اسلامی، علی‌آباد کتول، ایران.
3 - گروه حسابداری، واحد علی‌آباد کتول، دانشگاه آزاد اسلامی، علی‌آباد کتول، ایران.
4 - گروه حسابداری، واحد علی‌آباد کتول، دانشگاه آزاد اسلامی، علی‌آباد کتول، ایران.
کلید واژه: مدیریت سود, اقلام تعهدی, میانگین‌گیری بیزین,
چکیده مقاله :
هدف: عموماً مدل‌های اقلام تعهدی در محیط کشورهای پیشرفته (بازار سرمایه عمیق) شکل گرفته‌اند. پژوهشی که باتوجه‌به عدم توسعه‌یافتگی و شرایط خاص بازار سرمایه کشور، اقدام به مدلسازی اقلام تعهدی نماید، بسیار نادر است؛ در پژوهش حاضر سعی شده است برای تعیین مدل اقلام تعهدی کارا با استفاده از رویکردهای نوین میانگین‌گیری بیزین با تأکید بر TVP-BMA، TVP-DMA و TVP-DMS در مقایسه با رگرسیون سنتی (OLS) اقدام به شناسایی متغیرهای غیرشکننده مؤثر بر اقلام تعهدی در بازار سرمایه ایران نماییم.
روش‌شناسی پژوهش: روش تحقیق حاضر از لحاظ هدف کاربردی است. نمونه آماری پژوهش شامل 171 شرکت بورسی در دوره زمانی 1390 تا 1400 می‌باشد.
یافته‌ها: در این پژوهش متغیرهای بکار رفته مؤثر بر اقلام تعهدی در پژوهش‌های پیشین وارد مدل‌های بیزین و سنتی گردیدند. نتایج بیانگر این است که از میان مدل‌های بررسی و آزمون شده در پژوهش، مدل BMA با شاخص‌های ارزیابی عملکرد پیش‌بینی مدل به‌عنوان کاراترین مدل تعیین گردید. بر اساس مدل BMA، تعداد 10 متغیر غیرشکننده مؤثر بر اقلام تعهدی شناسایی شدند.
اصالت / ارزش‌افزوده علمی: شناسایی متغیرهای مؤثر بر تخمین اقلام تعهدی با داده‌های شرکت‌های موردمطالعه به‌منظور بومی‌سازی و تعیین اعتبار مدل با کمک میانگین‌گیری بیزین جهت مدلسازی از مشخصه متمایز به کار گرفته شده در پژوهش است. ضمن اینکه جهت جامعیت در مدلسازی از هر سه نوع مدل‌های اقلام تعهدی اختیاری (تأکید بر دارایی‌ها و فروش، هزینه‌های اختیاری)، مدل درآمد اختیاری (تأکید بر سود، درآمد و جریان وجه نقد) و مدل‌های ترکیبی در طراحی مدل بهینه به کار گرفته شده است.
Purpose: Accrual models have generally been developed in the context of developed countries with deep capital markets. However, due to the underdeveloped nature and specific characteristics of the Iranian capital market, studies aiming to model accruals in such environments are rare. This research seeks to identify robust predictors of accruals in Iran’s capital market by developing an efficient accrual model using advanced Bayesian model averaging techniques—namely Time-Varying Parameter Bayesian Model Averaging (TVP-BMA), TVP Dynamic Model Averaging (TVP-DMA), and TVP Dynamic Model Selection (TVP-DMS)—and comparing them with the traditional Ordinary Least Squares (OLS) approach.
Research Methodology: This applied study analyzes data from 171 publicly listed companies on the Tehran Stock Exchange over the period 2011 to 2021 (1390–1400 SH). Variables previously identified in the literature as determinants of accruals were incorporated into both Bayesian and traditional models.
Findings: Among the evaluated models, the BMA model outperformed others in terms of prediction performance indicators and was identified as the most efficient. According to the BMA results, 10 robust predictors were identified as significant influencers of accruals.
Originality / Value: This study contributes to the literature by identifying the most influential variables in estimating accruals based on firm-level data, thereby enabling the localization and validation of accrual models through Bayesian averaging. A distinguishing feature of the research is the integration of all three major categories of accrual models: discretionary accruals models (focused on assets, sales, and discretionary expenses), discretionary revenue models (focused on income, earnings, and cash flows), and hybrid models, for designing an optimal specification adapted to the Iranian context.
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