Identification and ranking of variables influencing earnings forecasting in the capital markets of Iraq and the United Arab Emirates

Articles in Press

Document Type : Original Article

Authors
hayder huodur radhi al-sarray 1
meysam doaei 2
Ibrahim Abed Mousa Alsabary 3
mohamad alimoradi 1
1 Department of Accounting, Isf.C., Islamic Azad University, Isfahan, Iran.
2 Department of Finance, Esf.C., Islamic Azad University, Esfarayen, Iran.
3 Department of Accounting, College of Administrative Sciences, Al-Mustaqbal University, Babylon, Hillah, Iraq.
10.22034/jmaak.2026.79113.4715
Abstract
Purpose: The primary objective of this study is to identify and rank financial and macroeconomic variables influencing the earnings forecasting of firms in the capital markets of Iraq and the United Arab Emirates.
Research Methodology: This research is application-oriented in purpose and exploratory-analytical in nature, employing an exploratory sequential mixed-methods research design. The overarching research logic is based on the systematic integration of expert judgment with empirical evidence derived from real capital market data. In the qualitative phase, key variables were identified and structured through expert judgment using a two-round Delphi process. In the quantitative phase, firm-level financial data and macroeconomic indicators over the period 2019–2024 were empirically analyzed.
Findings: Firm-specific financial variables particularly net income, return on assets and operating cash flow exhibit the strongest predictive power for earnings and show a high degree of alignment with expert judgments. Macroeconomic variables such as inflation rate, gross domestic product growth, exchange rate volatility and oil prices primarily play a moderating and contextual role, with their direct impact on corporate earnings being limited and heterogeneous.
Originality / Value Added: The application of a composite index enabled the ranking of variables based on both professional expertise and empirical evidence. This integrated approach enhances the scientific validity and generalizability of the findings and provides a realistic framework for developing earnings forecasting models and machine learning algorithms.
Keywords
Capital Market
Earnings Forecasting
Identification
Ranking
1.       حمامی، زهرا.، قدرتی، حسن.، عرب زاده، میثم.، پناهیان، حسین.، علیپور، محمد. (1402). طراحی و تبیین الگوی ارزیابی قابلیت پیش‌بینی سود در شرکت‌های فعال در صنعت مالی. فصلنامه ارزش آفرینی در مدیریت کسب و کار، 3(3)، 84-65.
2.       رضاییان، شیوا.، طالقانی، محمد.، شرج شریفی، آزیتا. (1403). توسعه مدلی جامع جهت پیش‌بینی قیمت سهام در بازار بورس اوراق بهادار با رویکرد مدل‌سازی ساختاری تفسیری. مدیریت دارایی و تأمین مالی، 12(2)، 58-39.
3.       میرزائی، سید احمد.، دعائی، میثم. (1398). ارائه مدلی جدید با رویکرد هوش مصنوعی به منظور پیش‌بینی قیمت سهام. ششمین کنفرانس ملی پژوهش‌های کاربردی در مهندسی کامپیوتر و فناوری اطلاعات.
 
4.       Abate, M. T., & Kaur, R. (2023). The evolution of modern capital structure theory: A review. Evolution, 31(2), 958-974.
5.       Abdulhadi, K. H., & Dasht Bayaz, M. L. (2023). Cross-country Analysis: Exploring the Impact of Intangible Assets and Macroeconomic Factors on Stock Prices in Iran, Saudi Arabia, and Iraq. International Journal of Economics and Finance Studies, 15(3), 22-56.
6.       Ahmed, P. F. A. A., & Ali, S. H. A. (2025). Analysis of Factors Affecting the Performance of the Iraq Stock Exchange: A Study for the Period (2004-2023) with a Focus on Government Policies, Oil Prices, and Security and Political Stability: Analysis of Factors Affecting the Performance of the Iraq Stock Exchange. Academic Journal of International University of Erbil, 2(02), 202-213.
7.       Alabdullah, T. T. Y. (2023). Capital market companies in the UAE: Determinants and factors affecting the performance of listed UAE companies. Current Advanced Research On Sharia Finance And Economic Worldwide, 3(1), 1-18.
8.       Ball, R., & Nikolaev, V. V. (2022). On earnings and cash flows as predictors of future cash flows. Journal of Accounting and Economics, 73(1), 101430.
9.       Borges de Araújo, F., & Marques dos Anjos, L. C. (2024). Cognitive and motivational bias in Budgetary Decision Making: Experimental Evidence in the Public Sector. Advances in Scientific & Applied Accounting, 17(3).
10.    Chen, S., & Ren, S. (2025). AI-enabled Forecasting, Risk Assessment, and Strategic Decision Making in Finance. Frontiers in Business and Finance, 2(02), 274-295.
11.    Chen, W., Zhang, L., Jiang, P., Meng, F., & Sun, Q. (2022). Can digital transformation improve the information environment of the capital market? Evidence from the analysts' prediction behaviour. Accounting & Finance, 62(2), 2543-2578.
12.    Cho, K. H., & Patil, B. (2025). Does macroeconomic uncertainty (really) influence managers’ earnings management? Journal of Corporate Accounting & Finance, 36(1), 185-197.
13.    Duarte, A. F., Lisboa, I., & Carreira, P. (2024). Does earnings quality impact firms’ performance? The case of Portuguese SMEs from the mold sector. Journal of Financial Reporting and Accounting, 22(4), 894-916.
14.    Ebaid, I. E. S. (2023). Nexus between sustainability reporting and corporate financial performance: evidence from an emerging market. International Journal of Law and Management, 65(2), 152-171.
15.    Farooq, U., Wen, J., Aldawsari, S. H., Tabash, M. I., & Khudoykulov, K. (2025). How does oil policy uncertainty influence resource rents? New empirical evidence from Organization of the Petroleum Exporting Countries. Economics & Politics, 37(1), 146-168.
16.    Fridson, M. S., & Alvarez, F. (2022). Financial statement analysis: a practitioner's guide. John Wiley & Sons.
17.    Hou, K., Qiao, F., & Zhang, X. (2023). Finding anomalies in China. Fisher College of Business Working Paper.
18.    Ismail, W. A. W., Saad, S. M., Lode, N. A., & Kustiningsih, N. (2022). Corporate sustainability reporting and firm’s financial performance in emerging markets. International Journal of Academic Research in Business and Social Sciences, 12(1), 396-407.
19.    Kabir, M. H., Sobur, A., & Amin, M. R. (2023). Stock Price Prediction Using The Machine Learning. International Journal of Computer Research and Technology (IJCRT), 11(7), 946-950.
20.    Khalfaoui, R., Ben Jabeur, S., Hammoudeh, S., & Ben Arfi, W. (2025). The role of political risk, uncertainty, and crude oil in predicting stock markets: Evidence from the UAE economy. Annals of Operations Research, 345(2), 1105-1135.
21.    Ni, H., Meng, S., Chen, X., Zhao, Z., Chen, A., Li, P., ... & Chan, Y. (2024, August). Harnessing earnings reports for stock predictions: A qlora-enhanced llm approach. 6th International Conference on Data-driven Optimization of Complex Systems (DOCS), 909-915.
22.    Organisation for Economic Co-operation and Development. (2023). Business Insights on Emerging Markets. OECD Publishing.
23.    Sadeeq, U. (2023). Noise: A flaw in human judgment. Sagepub.
24.    Sizan, M. M. H., Das, B. C., Shawon, R. E. R., Rana, M. S., Al Montaser, M. A., Chouksey, A., & Pant, L. (2023). AI-Enhanced Stock Market Prediction: Evaluating Machine Learning Models for Financial Forecasting in the USA. Journal of Business and Management Studies, 5(4), 152-166.
25.    Survilė, E. (2025). The impact of business cycle fluctuations and working capital strategies on profitability: retail and manufacturing sectors' case (Doctoral dissertation, Vilniaus universitetas).
26.    Wen, Y., Wang, J., & Chen, X. (2025). Trust and AI weight: human-AI collaboration in organizational management decision-making. Frontiers in Organizational Psychology, 3, 1419403.