Identification of non-fragile variables of accruals based on non-linear bayesian approaches in the Iranian capital market

Subject Areas : Financial Econometrics and Quantitative Methods

Azam Rezaian Joybari ¹ , Jamadori Gorganli Davaji ^{2 *} , Majid Ashrafi ³ , Ali Khamaki ⁴

1 - Department of Accounting, Ali.C., Islamic Azad University, Aliabad Katoul, Iran.
2 - Department of Accounting, Ali.C., Islamic Azad University, Aliabad Katoul, Iran.
3 - Department of Accounting, Ali.C., Islamic Azad University, Aliabad Katoul, Iran.
4 - Department of Accounting, Ali.C., Islamic Azad University, Aliabad Katoul, Iran.

Received: 2023-07-31 Accepted : 2025-05-22 Published : 2025-06-23

Keywords: Earnings Management, Accruals, Bayesian Averaging,

Abstract :

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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