Comparison of the Optimal Method in Bayesian, Dynamic and Selective Averaging Models to Identify the Influencing Variables on Capital Structure

Document Type : Original Article

Authors
1 PhD student of Financial Economics, Department of Economical science, Urmia Branch, Islamic Azad University, Urmia, Iran
2 Department of Economics, Urmia Branch, Islamic Azad University, Urmia, Iran
3 Department of Economy, Urmia Branch, Islamic Azad University, Urmia, Iran
10.22034/jik.2025.72488.4247
Abstract
The purpose of this research is to Compare the optimal method in Bayesian, Dynamic and Selective Averaging models to identify the influencing variables on capital structure forecasting.This research is practical in terms of purpose and correlational in terms of nature. In order to achieve the goal of the research, 175 companies from among the companies listed to the Tehran Stock Exchange during the years 2012 to 2022 were selected by systematic elimination method and considered as the main sample. Based on the results Among the BMA, TVP-DMA, TVP-DMS, BVAR and OLS models, the BMA model had the highest efficiency in identifying the most important variables affecting the capital structure. Based on this, 61 identified variables affecting the capital structure were included in the Bayesian averaging model. These variables were divided into two categories of internal and external factors. Based on previous probabilities, 17 variables were identified as important variables on the capital structure. Among these variables, 10 internal variables (ownership type, net operating profit, current ratio, asset turnover ratio) total; interest rate coverage ratio; debt to equity ratio; beta of all three M; accrued interest management; financial helplessness and taxes) and 7 external variables (inflation; exchange rate; budget deficit; business climate index; economic resilience index; sanctions index; capital market depth) were effective on the capital structure.
Keywords

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