Stock Selection of Main Board Companies Based on Financial Ratios and Expected Shortfall Using Promethee with Sharpe Ratio Evaluation
DOI:
https://doi.org/10.33603/jka.v9i1.11881Keywords:
Multi-Criteria decision making, Decision usefulness, Tail risk, Portfolio performance, Investment evaluationAbstract
The development of the Indonesian capital market requires stock selection methods that can comprehensively integrate financial statement information and market risk. Conventional approaches that rely on a single indicator are considered insufficient to capture the complexity of stock performance and risk. This study aims to select stocks in the Indonesian Main Board Index using the PROMETHEE method based on financial ratios and Expected Shortfall, and to evaluate risk–return efficiency using the Sharpe Ratio. This research adopts a quantitative approach using financial statement data for 2024 and daily stock price data from January 1 to December 31, 2025. The variables include liquidity, profitability, solvency, bankruptcy risk, stock return, and 95% Expected Shortfall. The results show that PROMETHEE is able to generate systematic stock rankings based on multi-criteria dominance, where stocks with the highest net flow exhibit better fundamental performance and lower extreme risk. However, further analysis reveals that stocks with the highest PROMETHEE rankings do not necessarily have the highest Sharpe Ratios, indicating differences in evaluation dimensions between the two methods. This study concludes that a two-stage approach PROMETHEE as an initial screening tool and the Sharpe Ratio as a subsequent evaluation provides more comprehensive insights for investment decision-making.References
Alsanousi, A. T., Alqahtani, A. Y., Makki, A. A., & Baghdadi, M. A. (2024). A Hybrid MCDM Approach Using the BWM and the TOPSIS for a Financial Performance-Based Evaluation of Saudi Stocks. Information, 15(5), 258. https://doi.org/10.3390/info15050258.
Am, T. & Yaqin, A. (2021). Decision Support System of Stock Selection Using Promethee Method. Conference Proceedings of The 4th International Conference on Information and Communications Technology (ICOIACT), 227–232. https://doi.org/10.1109/ICOIACT53268.2021.9564001.
Basilio, M. P., de Freitas, J. G., Kämpffe, M. G. F., & Bordeaux-Rego, R. (2018). Investment portfolio formation via multicriteria decision aid: A Brazilian stock market study. Journal of Modelling in Management, 13(2), 394–417. https://doi.org/10.1108/JM2-02-2017-0021.
Bayer, S. & Dimitriadis, T. (2022). Regression-based expected shortfall backtesting. Journal of Financial Econometrics, 20(3), 437–471, https://doi.org/10.1093/jjfinec/nbaa013.
Broda, S. A., Krause, J., & Paolella, M. S. (2018). Approximating expected shortfall for heavy-tailed distributions. Econometrics and Statistics, 8, 184–203. https://doi.org/10.1016/j.ecosta.2017.07.003.
Bursa Efek Indonesia. (2024). Indeks saham. https://www.idx.co.id.
Chang, C.-L., Jimenez-Martin, J.-A., Maasoumi, E., McAleer, M., & Pérez-Amaral, T. (2019). Choosing Expected Shortfall over VaR in Basel III using stochastic dominance. International Review of Economics & Finance, 60, 95–113. https://doi.org/10.1016/j.iref.2018.12.016.
Christou, E., & Grabchak, M. (2022). Estimation of Expected Shortfall using Quantile Regression: a Comparison Study Computational Economics, 60(2), 725–753. https://doi.org/10.1007/s10614-021-10164-z.
Du, Z., & Escanciano, J. C. (2017). Backtesting Expected Shortfall: Accounting for tail risk. Management Science, 63(4), 940–958. https://doi.org/10.1287/mnsc.2015.2342.
Ghahtarani, A., Saif, A., & Ghasemi, A. (2021). Robust portfolio selection: A comprehensive review. Expert Systems with Applications, 22(3), 3203–3264 https://doi.org/10.1007/s12351-022-00690-5.
Gerlach, R., & Wang, C. (2018). Semi-parametric Dynamic Asymmetric Laplace Models for Tail Risk Forecasting, Incorporating Realized Measures. Quantitative Risk Management, arXiv:1805.08653. https://doi.org/10.48550/arXiv.1805.08653.
Han, X., Wang, B., Wang, R., & Wu, Q. (2024). Risk concentration and the mean–expected shortfall criterion. Mathematical Finance, 34(3), 819-846. https://doi.org/10.1111/mafi.12417.
Hué, S., Christophe, H., Yang, L. (2024). Backtesting Expected Shortfall: Accounting for Both Duration and Severity with Bivariate Orthogonal Polynomials. Quantitative Risk Management, arXiv: 2405.02012. https://doi.org/10.48550/arXiv.2405.02012.
Jing, D., Imeni, M., Edalatpanah, S. A., Alburaikan, A., & Khalifa, H. A. E.-W. (2023). Optimal Selection of Stock Portfolios Using Multi-Criteria Decision-Making Methods. Mathematics, 11(2), 415. https://doi.org/10.3390/math11020415.
Kan, R., Wang, X., & Zheng, X. (2024). In-sample and out-of-sample Sharpe ratios of multi-factor asset pricing models. Journal of Financial Economics, 155, Article 103837. https://doi.org/10.1016/j.jfineco.2024.103837.
Lazar, E. & Zhang, N. (2019). Model Risk of Expected Shortfall. Journal of Banking & Finance, 100, 279–302105. https://doi.org/10.1016/j.jbankfin.2019.05.017.
Mappadang, A., Nugroho, B. A., Lestari, S. D., Elizabeth, & Lestari, T. K. (2024). Measuring Value-at-risk and Expected Shortfall of Newer Cryptocurrencies: New Insights. Cogent Business & Management, 11(1). https://doi.org/10.1080/23311975.2024.2416096.
Merlo, L., Petrella, L., & Raponi, V. (2021). Forecasting Value-at-Risk and Expected Shortfall using multivariate quantile regression and its implications in portfolio allocation. Journal of Banking & Finance Elsevier, vol. 133(C): 106248 https://doi.org/10.1016/j.jbankfin.2021.106248.
Narang, M., Joshi, M. C., Bisht, K., & Pal, A. (2022). Stock Portfolio Selection Using A New Decisionmaking Approach Based On The Integration Of Fuzzy Cocoso With Heronian Mean Operator. Decision Making: Applications in Management and Engineering. Vol. 5, Issue 1, 2022, pp. 90-112. https://doi.org/10.31181/dmame0310022022n.
Papapostolou, A., Karakosta, C., Mexis, F.-D., Andreoulaki, I., & Psarras, J. (2024). A Fuzzy PROMETHEE Method for Evaluating Strategies towards a Cross-Country Renewable Energy Cooperation: The Cases of Egypt and Morocco. Energies, 17(19), 4904. https://doi.org/10.3390/en17194904.
Rockafellar, R. T., & Uryasev, S. (2000). Optimization of Conditional Value-at-Risk
Journal of Risk. 2. 21-42. https://doi.org/10.21314/JOR.2019.395.
Saleh, D. Hanif, A., Fitriyah, H., & Biduri, S. (2023). Analisis Prediktor Rasio Keuangan Terbaik Terhadap Harga Saham Dengan Metode Best Subset Selection (Studi pada Perusahaan yang Terindeks di IDXHIDIV20 Bursa Efek Indonesia Periode 2018-2020). Akuntansi: Jurnal Akuntansi Integratif. 8(2). 213-235. https://doi.org/10.29080/jai.v8i2.1009.
Salo, A. Doumpos, M., Liesiö, J., & Zopounidis, C. (2024). Fifty years of portfolio optimization. European Journal of Operational Research Volume 318, Issue 1, Pages 1-18. https://doi.org/10.1016/j.ejor.2023.12.031.
Sikalo, M., Arnaut-Berilo, A., & Delalić, A. (2023). A combined AHP-PROMETHEE approach for portfolio performance comparison. International Journal of Financial Studies, 11(1), 46. https://doi.org/10.3390/ijfs11010046.
Subastyan, G. M. (2024). The effect of financial ratios on stock prices. Eduvest: Journal of Universal Studies, 4(3), 1245-1257. https://doi.org/10.59188/eduvest.v4i3.1098.
Vuković, M., Pivac, S., & Babić, Z. (2020). Comparative analysis of stock selection using a hybrid MCDM approach and modern portfolio theory, Croatian Review of Economic, Business and Social Statistics (CREBSS), Vol. 6, Iss. 2, pp. 58-68, https://doi.org/10.2478/crebss-2020-0011.
Wątróbski, J. (2023). Temporal PROMETHEE II New Multi-Criteria Approach to Sustainable Management of Alternative Fuels Consumption. Journal of Cleaner Production, 413, 137445. https://doi.org/10.1016/j.jclepro.2023.137445.
Zaevski, T. S., & Nedeltchev, D. C. (2023). From BASEL III to BASEL IV and Beyond: Expected Shortfall and Expectile Risk Measures. International Review of Financial Analysis, 87, 102645. https://doi.org/10.1016/j.irfa.2023.102645.
Zolfani, S. H., Taheri, H. M., Gharehgozlou, M., & Farahani, A. (2022). An asymmetric PROMETHEE II for Cryptocurrency Portfolio Allocation Based on Return Prediction. Applied Soft Computing, 131, 109829. https://doi.org/10.1016/j.asoc.2022.109829.
Zopounidis, C., & Doumpos, M. (2013). Multicriteria decision systems for financial problems. TOP, 21, 241–261. https://doi.org/10.1007/s11750-013-0279-7.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Jurnal Kajian Akuntansi

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike (CC-BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.