A Hybrid BWM-TOPSIS Approach for Evaluating Saudi Stocks'Financial Performance

Shangkun Haddad^*
*Correspondence: Shangkun Haddad, Department of Accounting and Finance, Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia, Email:

Introduction

Evaluating the financial performance of stocks is a critical task for investors, analysts, and financial institutions aiming to make informed investment decisions. Given the complexity of financial markets and the multiple factors influencing stock performance, Multi-Criteria Decision-Making (MCDM) methods have gained prominence in financial analysis. Among these, the Best-Worst Method (BWM) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are widely used for ranking and selecting optimal investment choices. The combination of these two methods in a hybrid MCDM approach provides a structured and objective evaluation framework, particularly in emerging markets like Saudi Arabia, where stock performance is influenced by factors such as economic diversification, oil price fluctuations, and regulatory reforms. The BWM is utilized to determine the relative importance of financial performance indicators based on expert judgment, while TOPSIS ranks stocks by measuring their relative closeness to an ideal financial performance scenario [1].

Description

The financial performance of stocks is assessed through a range of quantitative and qualitative criteria, including profitability ratios, liquidity ratios, market performance indicators, and financial stability metrics. Traditional evaluation methods often rely on single-dimensional analysis, such as fundamental analysis or technical indicators, which may not capture the multi-faceted nature of stock performance. The MCDM approach, on the other hand, allows for a more comprehensive evaluation by considering multiple financial criteria simultaneously. In this study, the Best-Worst Method (BWM) is employed to determine the relative importance of financial performance indicators. The BWM is a pairwise comparison-based method that requires decision-makers to identify the most important and least important criteria, followed by a comparative assessment of other factors relative to these two extremes. Compared to other weighting methods like the Analytic Hierarchy Process (AHP), BWM reduces inconsistency in decision-making and requires fewer comparisons, making it more efficient and reliable. By applying BWM, the study derives optimal weight coefficients for key financial indicators such as Return On Equity (ROE), Return On Assets (ROA), Price-To-Earnings Ratio (P/E), Earnings Per Share (EPS), and Debt-To-Equity Ratio (D/E), ensuring that the evaluation process reflects real-world financial priorities [2]. Once the weight coefficients are determined, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to rank Saudi stocks based on their financial performance. TOPSIS is a distance-based ranking method that evaluates alternatives by measuring their closeness to an ideal best and ideal worst solution. The ideal best solution represents the highest financial performance for all criteria, while the ideal worst solution reflects the poorest financial performance. Each stock is scored based on its Euclidean distance from these two reference points, with a higher score indicating better financial performance. The application of this approach to the Saudi stock market is particularly relevant given the marketâ??s evolving nature. Saudi Arabia, being a major emerging market and the largest economy in the Gulf Cooperation Council (GCC), has undergone significant financial and economic transformations, particularly with the introduction of Vision 2030 reforms aimed at diversifying the economy beyond oil dependency. The Saudi stock market, represented by the Tadawul All Share Index (TASI), has witnessed increased foreign investor participation, regulatory advancements, and the listing of major state-owned enterprises like Saudi Aramco, making financial performance evaluation more critical than ever [3]. The hybrid BWM-TOPSIS approach can help investors navigate this dynamic market by identifying top-performing stocks across different industry sectors, including banking, petrochemicals, real estate, and technology. One of the major strengths of the hybrid BWM-TOPSIS approach is its ability to integrate expert judgment with data-driven evaluation, thereby enhancing the reliability of stock ranking outcomes. However, challenges remain, particularly in ensuring that subjective judgments in the BWM weighting process do not introduce biases and that financial data used in TOPSIS analysis is comprehensive and up-to-date. Additionally, the effectiveness of this approach depends on the selection of appropriate financial indicators that reflect the true performance of stocks in the Saudi market context. The inclusion of macroeconomic factors, such as inflation rates, interest rates, and exchange rate fluctuations, can further refine the evaluation process and provide a more holistic view of stock performance. Looking ahead, the integration of big data analytics, AI, and real-time financial monitoring will further enhance the predictive power of multi-layer network models [4]. As financial markets become more complex and competitive, the use of hybrid MCDM techniques such as BWM-TOPSIS will play a crucial role in shaping investment strategies, ensuring that stock evaluations are both systematic and adaptable to changing market conditions. These technologies will enable dynamic risk tracking, early warning systems, and automated stress-testing, ensuring that financial institutions remain resilient in an increasingly complex economic environment. In the context of Saudi Arabiaâ??s financial market expansion, this approach provides a valuable decision-support tool for navigating stock performance in an evolving and dynamic investment landscape. This study employs a hybrid BWM-TOPSIS approach to evaluate the financial performance of Saudi stocks, offering a robust methodology for decision-makers to identify the best-performing investment opportunities based on multiple financial criteria. The hybrid BWM-TOPSIS approach ensures that the ranking of Saudi stocks is both objective and data-driven, allowing investors to make more reliable and informed investment choices [5].

Conclusion

The hybrid BWM-TOPSIS approach presents a powerful and systematic methodology for evaluating the financial performance of stocks in the Saudi stock market. By combining the expert-driven weighting of financial criteria with an objective ranking mechanism this approach ensures that investment decisions are based on a balanced and multi-dimensional assessment of stock performance. The findings of this study provide valuable insights for investors, portfolio managers, and financial analysts, enabling them to make more informed decisions in selecting high-performing stocks. The increasing integration of quantitative decision-making models in financial analysis highlights the importance of adopting data-driven and transparent investment strategies. Future research can explore the integration of machine learning and Artificial Intelligence (AI) with the BWM-TOPSIS framework to enhance predictive accuracy and automate the evaluation process. Moreover, expanding the methodology to include risk-based factors, sustainability criteria (ESG metrics), and macroeconomic indicators can further improve investment decision-making.

Acknowledgement

None

Conflict of Interest

None

References

1. Sacks, Rafael, Ioannis Brilakis, Ergo Pikas and Haiyan Sally Xie, et al. "Construction with digital twin information systems." DCE 1 (2020): e14.

Google Scholar  Cross Ref  Indexed at

2. Mahdjoubi, Lamine, C. A. Brebbia and Richard Laing, eds. “Building Information Modelling (BIM) in design, construction and operations.” WIT Press (2015): 149.

Google Scholar                                 

3. Lihtmaa, Lauri and Targo Kalamees. "Emerging renovation strategies and technical solutions for mass-construction of residential districts built after World War II in Europe." Energy Strategy Rev 51 (2025): 101282.

Google Scholar   Cross Ref Indexed at

4. Juarez, Maria G., Vicente J. Botti and Adriana S. Giret. "Digital twins: Review and challenges." J Comput Inf Sci Eng 21 (2021): 030802.

Google Scholar   Cross Ref Indexed at

5. Sharma, Angira, Edward Kosasih, Jie Zhang and Alexandra Brintrup, et al. "Digital twins: State of the art theory and practice, challenges and open research questions." J Ind Inf Integr 30 (2022): 100383.

Google Scholar  Cross Ref Indexed at