1. Introduction
Investors in the stock market are often driven by the objective of achieving high returns while minimizing risks. However, this goal becomes challenging due to various factors influencing stock market performance, such as the global economy, political events, and security concerns. To navigate these complexities, investors must consider a range of criteria to guide their decision-making process. When it comes to stock market investing, the use of multicriteria decision making (MCDM) becomes a significant challenge. Investors must evaluate and compare numerous stocks based on predetermined criteria to identify those with the most significant potential for high returns. This evaluation process involves analyzing both fundamental and stock market indicators. Fundamental indicators assess a company’s financial performance, including its earnings per share, return on investment, and price-to-earnings ratio. These indicators provide insights into the company’s profitability, efficiency, and valuation, which are vital considerations for investors. On the other hand, stock market indicators provide information on trading volumes and overall market risk, allowing investors to gauge market sentiment and potential volatility.
Given the complexity and diversity of the Saudi Stock Market, which encompasses more than 21 sectors and over 200 companies, selecting stocks for investment becomes an intricate task. Each sector has unique dynamics and potential opportunities, adding another layer of complexity to the decision-making process. In such situations, employing MCDM methods becomes crucial for structuring and analyzing complex problems, enabling investors to consider multiple criteria simultaneously.
The Saudi Stock Market is of significant importance globally. With over 210 listed companies distributed across various sectors, including banks and financial services, petrochemical industries, energy, and more, the Saudi Stock Market plays a vital role in the country’s economic landscape. Under the Vision 2030 initiative, the private sector’s role is also increasing, with the government aiming to raise the private sector’s contribution to the gross domestic product (GDP) from 40% to 65% by 2030. Given the significance of the Saudi Stock Market and the challenges associated with selecting optimal stocks for investment, there is a clear need for a hybrid model based on MCDM to guide investment decisions and maximize returns.
This study aims to address this need by utilizing MCDM methods to analyze and rank selected stocks from different sectors in the Saudi Stock Market. The objectives of this study encompass several key aspects. First, a comprehensive review of the existing literature on MCDM methods and stock market performance will be conducted. This review will provide valuable insights and establish a solid foundation for a subsequent analysis. Second, this study will identify the sectors with the most potential within the Saudi Stock Market, considering various factors such as market trends, growth prospects, and investor sentiment. Third, valuable stocks will be selected from each sector based on a thorough evaluation of their fundamental and stock market indicators. Fourth, MCDM methods will be employed to rank the selected stocks, considering the predetermined criteria and their relative importance. Finally, this study aims to rank the stocks that offer the highest return potential in the Saudi Stock Market.
This study aspires to contribute to the knowledge of MCDM methods and their application in the Saudi Stock Market. The findings will provide valuable insights and guidance for investors, enabling them to make more informed decisions and optimize their investment strategies in the dynamic and rapidly evolving Saudi Stock Market. This study offers a unique methodology tailored to the specific context of stock market evaluation, adding to the existing literature. This approach presents a fresh perspective on evaluating stock performance and assists decision-makers in making well-informed investment decisions.
The rest of this paper is structured as follows.
Section 2 provides a review of the literature on key topics related to the stock market, including applications of mathematical programming, MCDM methods, and other analytical approaches used in stock market research.
Section 3 outlines this study’s proposed methodology, including identifying the top five sectors and stocks for each sector and criteria, determining criteria weights, and evaluating stock performance as alternatives.
Section 4 presents the results of applying each stage of the methodology.
Section 5 discusses the findings and results of the analysis. Finally,
Section 6 summarizes the main conclusions and limitations of this study and provides recommendations for future work.
3. Materials and Methods
The structure of the suggested methodology for measuring and ranking stocks on the Saudi Stock Market is shown in
Figure 1. The proposed method consists of three consecutive stages. In the first stage, an evaluation was conducted to evaluate the top five sectors of the stock market by market cap. The number of listed companies varies across sectors, and the selection is based on the top 25% of stocks in each sector by market cap. The second stage employed the BWM to calculate the weights of the criteria collected from the expert subject matter. In the third stage, the chosen stocks were ranked using the TOPSIS approach. This involved identifying an alternative closest to the ideal solution and farthest from the negative ideal solution in a multidimensional computing space. These rankings were based on the overall performance of the stocks, as determined by the weighted criteria calculated in the previous stage.
The proposed methodology enhances the understanding of the research question by providing a clear and structured approach to evaluating the stock market. It contributes by providing methodological clarity, considering multiple criteria, integrating subjective and objective factors, offering decision support, and providing a novel application in the stock market context. It ensures transparency and replicability by offering a step-by-step process. Considering multiple criteria enables a comprehensive evaluation of stock market performance, including fundamental and stock market indicators.
The integration of subjective and objective factors captures qualitative and quantitative aspects. The BWM allows decision-makers to express preferences while TOPSIS incorporates objective data, resulting in a balanced evaluation that enhances decision making. The methodology provides actionable insights for investors through stock rankings and the identification of top-performing sectors. This guidance supports investment strategies and portfolio management. It introduces a novel application of the hybrid MCDM approach in the context of the stock market. Adopting the BWM and TOPSIS brings a unique methodology tailored to stock market evaluation, expanding the knowledge base and offering new research opportunities.
3.2. Determining Criteria Weights
Step 5 focuses on assigning weights to the stock performance criteria. The BWM is the appropriate method for MCDM problems. A survey was conducted involving 10 finance experts who were actively involved in investment consulting in the Saudi Stock Market. The purpose was to gather insights on how these experts assessed the importance of each performance criterion. The participants had varying levels of experience and education, which can be found in
Table A1 in
Appendix A. The data utilized in this study were obtained from the main market website, which is the official website of the Saudi Market Exchange (Tadawul) [
92]. According to Munim et al. [
93], the following steps explain the calculation of weight criteria using the BWM.
Step 5.1: Formulate the problem; the decision-maker determines the evaluation criteria.
Step 5.2: Identify the best and worst criteria. The decision-maker determines the best (most important) criterion and the worst (least important) criterion for use in the comparison process to determine the vectors used in finding the criteria weights.
Step 5.3: Determine the preference for the best criterion over all other criteria. The decision-maker ranks the best criterion’s importance over all other criteria using a 1–9 scale, where 1 indicates the same importance of the two criteria and 9 indicates the extreme importance of one criterion over another. The resulting best-to-others vector will be
where
abj expresses the preference for the best criterion
b over criterion
j, and the decision-maker identifies the best criterion (
b).
Step 5.4: Determine the preference for all other criteria over the worst criterion.
Step 5.3: The decision-maker ranks the importance of all other criteria over the worst criterion using a scale from 1 to 9. This results in the other worst vector.
where
ajw expresses the preference for criterion (
j) over the worst criterion (
w); the decision-maker identifies the least important criterion (
w).
Step 5.5: Estimate the optimal weight. This step aims to minimize the absolute differences:
For all
j to reach the optimal criteria weights, observe the following linear programming model:
≥ 0, for all j.
abj: represents the preference for the best criterion b over criterion j.
ajw: represents the preference for the criterion j over the worst criterion w.
wb: indicates the optimal weights of the best criteria.
ww: indicates the optimal weights of the worst criteria.
wj: indicates the optimal weights of the other criteria.
δL: indicates the consistency ratio of the comparison procedure in the BWM.
Step 5.6 Find the scores of alternatives. To find the final rank of the alternatives, decision-makers rank the alternatives using a 1–9 score, where 1 indicates that the alternative was not implemented at all, and 9 indicates that it was the most implemented alternative. Then, normalize these scored values by dividing each value by the maximum value in its column. After that, multiply the normalized values by their respective weights. Finally, taking the row-wise averages gives us the final ranking of the alternatives. The following equation can represent this step:
Fi: the final score of the alternative i.
: the normalized score of criterion j under alternative i.
Step 6: In this step, calculate the overall weight of each performance criterion.
5. Discussion
In the past five years, the Saudi Stock Market has undergone significant transformation, with the top five sectors in market capitalization demonstrating notable growth compared to other sectors. This growth has made these sectors attractive to investors seeking to assess and analyze the constituent companies within each sector. Various criteria were considered to evaluate and rank the value of these companies. An extensive review of published papers emphasized the importance of stock selection criteria in the investment process. These criteria can be broadly categorized into three main groups: profitability, market conditions, and validation. To gain insights from experts in the field, seven academics from the Faculty of Economics, Engineering, and Business who specialized in finance and were actively involved in investment consulting in the Saudi Stock Market were consulted. The findings revealed that profitability was deemed the most critical criterion, carrying a weight of 56.5%. Market conditions and validation were weighted at 32.5% and 11%, respectively.
Hybrid MCDM methods were utilized in this study to evaluate seven criteria for the Saudi Stock Market. The aim was to establish a comprehensive performance evaluation framework by employing the BWM to assign weights to the criteria and use the TOPSIS method to rank the alternatives. According to the TOPSIS results, A8 emerged as the top-ranking company. When considering the weighted criteria for profitability, A8 showcased exceptional performance in terms of its ROE, ROA, and NPM, with returns of 47%, 37%, and 52%, respectively. Next in the ranking were A13 and A3. A13 exhibited strong performance by the end of 2023, with a 39% ROE and an 18.6% NPM. A3 boasted the highest NPM at 64.5%, but its lower ROA of 2.2% affected its overall ranking.
In contrast, A12 ranked the lowest in both ROE (2.6%) and ROA (2.5%). Notably, the bank sector displayed the highest NPM ratios compared to other sectors, with A3, A4, and A5 recording percentages of 64.5%, 59%, and 57%, respectively. Similarities and differences can be observed by comparing the findings of this study with previous research in the field. Like other studies, this research underscores the importance of financial indicators such as the ROE, ROA, NPM, EPS, P/E, P/B, and ATO in evaluating stock performance. However, it is essential to note that some disparities may arise due to variations in sample size, period, the inclusion of other criteria, or methodological differences.
Our evaluation of stocks on the Saudi Stock Market using an MCDM approach underscores the importance of providing robust policy implications that can guide stakeholders, policymakers, and regulatory bodies in making informed decisions. Our study’s primary objective was to offer investors a reliable evaluation methodology for informed investment decisions, and we acknowledge the significance of extending its policy implications.
One key policy implication of our research is the identification of sectors and stocks with strong financial performance. By considering multiple financial criteria such as the ROE, ROA, NPM, and ATO, we identified sectors that exhibit high return potential. Policymakers can utilize this information to develop targeted policies and initiatives to foster growth and investment in these sectors. Such interventions can contribute to the country’s overall economic development by creating a favorable environment for these sectors to thrive.
Moreover, our findings shed light on the performance of the bank sector, which displayed the highest NPM ratios compared to other sectors. Policymakers and regulators can utilize this insight to enhance the banking industry’s stability and efficiency. This may involve implementing regulatory measures to ensure sound financial practices and mitigate risks. Additionally, policymakers can consider introducing incentives and initiatives that encourage further investments in the banking sector, fostering its growth and contribution to the economy.
This study aligns with the Vision 2030 initiative, which aims to promote economic diversification and increase the private sector’s contribution to the GDP. By identifying and evaluating stocks with high return potential, our research provides valuable guidance for policymakers and investors seeking to strengthen the private sector. Policymakers can leverage our findings to develop targeted strategies and policies that facilitate investment, entrepreneurship, and innovation in sectors with significant growth prospects. This can contribute to realizing the Vision 2030 goals by fostering a dynamic and competitive private sector.
Similarly, applying the TOPSIS method to rank alternatives based on the established criteria weights provided a robust evaluation framework. The effectiveness of TOPSIS in decision making, including stock market analysis, is supported by the work of Hwang and Yoon [
95], in which incorporating actual market data into the analysis improved the validity and practical applicability of the results. This is consistent with the findings of previous studies, such as [
96,
97,
98], which underscored the importance of using actual data to enhance the reliability of decision-making methods in stock market analysis.