Dynamic Analysis of the Effects of Profitability Ratio Shocks on Debt, Liquidity, Activity, and Market Ratios
Keywords:
PSVAR models, Directed Acyclic Graphs (DAG), profitability ratios, debt ratios, market ratios, liquidity ratios, activity ratiosAbstract
This article examines the dynamic interactions of corporate financial behaviors using a nine-variable Panel Structural Vector Autoregression (PSVAR) framework. The purpose of the study is to investigate the mutual and dynamic effects of profitability ratios on debt ratios, liquidity ratios, activity ratios, and market ratios. A total of 219 companies were selected during the years 2001–2024. To analyze the causal behavior of the model variables and to impose the restrictions required for applying the PSVAR model, Directed Acyclic Graphs (DAG) were used, and the causal relationships were extracted using Tetrad4 software. The results indicate that a ROE shock strongly reinforces itself in the early stages and also increases ROA. Moreover, when a ROE shock occurs, market value grows more than book value, and companies facing a positive ROE shock reduce their need for debt financing for up to five periods. In the long run, companies can use debt to generate growth, yet in the short run, emphasis should be placed on improving profitability ratios.
Downloads
References
1. Rajabi A. System Dynamics: A Novel Approach to Modeling Accounting Events and Financial Decision-Making. Empirical Research. 2018;7(28):21-42.
2. Fallahpour S, Tehrani R, Tabatabai SJ. Designing a simultaneous dynamics model for the financial behaviors of companies listed on the Tehran Stock Exchange under uncertainty. Asset Management and Financing. 2015;4:11.
3. Kalkar H, Molaei Golnaji I. Examining the interplay between working capital management, financial leverage, and performance variables in companies listed on the Tehran Stock Exchange. Islamic Economics and Banking Scientific Journal. 2021;39:279-99.
4. Aghababaei ME, Aliyan E. The Impacts of Investor Sentiment on Liquidity and its Volatility: Evidence from Tehran Stock Exchange. Financial Research Journal. 2022;24(1):61-80. doi: 10.22059/frj.2021.328773.1007231.
5. Eyshi Ravandi M, Moeinaddin M, Taftiyan A, Rostami Bashmani M. Investigating the Impact of Investor Sentiment and Liquidity on Stock Returns of the Iranian Stock Exchange. Dynamic Management and Business Analysis. 2024;3(1):40-52. doi: 10.22034/dmbaj.2024.2038046.1068.
6. Ellington M. Financial market illiquidity shocks and macroeconomic dynamics: Evidence from the UK. Journal of Banking & Finance. 2018;89:225-36. doi: 10.1016/j.jbankfin.2018.02.013.
7. Messaoud D, Ben Amar A, Boujelbene Y. Investor sentiment and liquidity in emerging stock markets. Journal of Economic and Administrative Sciences. 2023;39(4):867-91. doi: 10.1108/JEAS-11-2020-0198.
8. Hasanzadeh I, Sheikh MJ, Arabzadeh M, Farzinfar AA. The Role of Economic Policy Uncertainty in Relation to Financial Market Instability and Stock Liquidity in Tehran Stock Exchange Companies. Dynamic Management and Business Analysis. 2023;2(3):163-78. doi: 10.22034/dmbaj.2024.2031971.2315.
9. Salisu AA, Demirer R, Gupta R, Sangeetha JM, Alfia KJ. Technological shocks and stock market volatility over a century Financial stock market forecast using evaluated linear regression based machine learning technique. Journal of Empirical Finance. 2024;79:101561. doi: 10.1016/j.measen.2023.100950.
10. Sohag K, Kalina I, Samargandi N. Oil market cyclical shocks and fiscal stance in OPEC+. Energy. 2024;296:130949. doi: 10.1016/j.energy.2024.130949.
11. Yousfani K, Iftikhar H, Rodrigues PC, Armas EAT, López-Gonzales JL. Global Shocks and Local Fragilities: A Financial Stress Index Approach to Pakistan's Monetary and Asset Market Dynamics. Economies. 2025;13(8):243. doi: 10.3390/economies13080243.
12. Han G, Zhang W. How strong are the linkages between real estate and other sectors in China? Research in International Business and Finance. 2016;36:52-72. doi: 10.1016/j.ribaf.2015.09.018.
13. Pishbahar E, Dashti Q, Khalili Malekshah S. Examining the impact of macroeconomic variables on agricultural product prices in Iran: A Structural Vector Autoregression (SVAR) approach and Directed Acyclic Graphs (DAG). Journal of Agricultural Economics and Development. 2018;24(95):25-47.
14. Khalili Malekshah S, Ghahramanzadeh M. Analyzing the relationship between exports and the growth of Iran's agricultural sector: Application of Structural Vector Autoregression (SVAR) and Directed Acyclic Graphs (DAG). Economic Journal. 2016;10(99):4-81.
15. Guerini M, Moneta A. A method for agent-based models validation. Journal of Economic Dynamics and Control. 2017;82:125-41. doi: 10.1016/j.jedc.2017.06.001.
16. Lyu Y, Yi H, Yang M, Zou Y, Li D, Qin Z. Financial uncertainty shocks and systemic risk: Revealing the risk spillover from the oil market to the stock market. Applied Energy. 2025;382:125311. doi: 10.1016/j.apenergy.2025.125311.
17. Habibi A, Kolahi M. Structural Equation Modeling and Factor Analysis: Tehran: Jihad University Press; 2017.
Downloads
Published
Submitted
Revised
Accepted
Issue
Section
License
Copyright (c) 2025 Hamta Bashirigoodarzi (Author); Alireza Ghiyasvand; Farid Sefaty, Mahmud Hematfar (Author)

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.