Hierarchical ranking of the Dow Jones index using the ELECTRE-III method
DOI:
https://doi.org/10.36792/rvu.v93i93.43Keywords:
multicriteria hierarchical process, FINANCIAL RATIOS, DOW JONESAbstract
The objective of the article is to present a multicriteria hierarchical process (MCHP) approach to decision making in the selection of stocks of the main companies of the Dow Jones index. One of the problems that investors often face is deciding which stocks should be included in an investment portfolio. The article allows investors to answer this question, through an MCHP approach and the ELECTRE III method using different criteria based on the financial relationships of profitability, liquidity, market, and efficiency. In this process, the investor generates a global ranking and a ranking of each subgroup of criteria regarding the investor’s preferences.
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Aldalou, E., & Perçin, S. (2018). Financial Performance Evaluation of Turkish Airline Companies Using Integrated Fuzzy AHP Fuzzy Topsis Model. Uluslararası İktisadi ve İdari İncelemeler Dergisi. DOI: https://doi.org/10.18092/ulikidince.347925
Albadvi, A., Chaharsooghi, S., & Esfahanipour, A. (2006). Decision making in stock trading: An application of PROMETHEE. European Journal of Operational Research, 177(2), 673–683. DOI: https://doi.org/10.1016/j.ejor.2005.11.022
Almeida J., Figueira, J. R., y Roy, B. (2006). The software ELECTRE III-IV: Methodology and user Manual, Paris, Francia: University Paris-Dauphine Lamsade.
Altınırmak, S., Gülcan2, B., & Çağlar, K. (2016). Analyzing securities investment trusts traded in BIST via AHP PROMETHEE methodology. Journal of International Scientific Publications, 10, 458–472.
Angilella, S., Catalfo, P., Corrente, S., Giarlotta, A., Greco, S., & Rizzo, M. (2018). Robust sustainable development assessment with composite indices aggregating interacting dimensions: The hierarchical-SMAA-Choquet integral approach. Knowledge-Based Systems, 158, 136–153. DOI: https://doi.org/10.1016/j.knosys.2018.05.041
Ariza, M., & Cadena, J. (2017). Selección de portafolios de renta variable: una propuesta a través de betas al alza y a la baja en el mercado colombiano. Criterio Libre. 11(19), 225-243. DOI: https://doi.org/10.18041/1900-0642/criteriolibre.2013v11n19.1109
Bahloul, S., & Abid, F. (2013). A combined analytic hierarchy process and goal programming approach to international portfolio selection in the presence of investment barriers. International Journal of Multicriteria Decision Making, 3(1), 1–20. https://doi.org/10.1504/IJMCDM.2013.052455 DOI: https://doi.org/10.1504/IJMCDM.2013.052455
Basilio, M., De Freitas, J., Kämpffe, M. G., & Rego, R. (2018). Investment portfolio formation via multicriteria decision aid: A Brazilian stock market study. Journal of Modelling in Management, 13(12), 394–417. https://doi.org/https://doi.org/10.1108/JM2-02-2017-0021 DOI: https://doi.org/10.1108/JM2-02-2017-0021
Bay, Y., Yudan, W., & Li Quian. (2017). an optimal trade-off model for portfolio selection with sensitivity of PARAMETERS Yanqin Bai ∗ , Yudan Wei and Qian Li. Journal of Industria l and Management Optimization, 13(2), 947–965. https://doi.org/10.3934/jimo.2016055 DOI: https://doi.org/10.3934/jimo.2016055
Bodie, Z. (2019). Merton and the Science of Finance. Annual Review of Financial Economics, 11(1), 1–20. DOI: https://doi.org/10.1146/annurev-financial-011019-040506
Bodie, Z., & Merton, R. (2003). Finanzas. Pearson Eduación.
Boonjing, V., & Boongasame, L. (2016). Combinatorial Portfolio Selection with the ELECTRE III method: Case study of the Stock Exchange of Thailand (SET). Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, 8(4), 719–724. DOI: https://doi.org/10.15439/2016F228
Cao, L. J., & Tay, F. E. H. (2003). Support vector machine with adaptive parameters in financial time series forecasting. IEEE Transactions on Neural Networks, 14(6), .1506–1518. DOI: https://doi.org/10.1109/TNN.2003.820556
Cervelló, R., Guijarro, F., & Michniuk, K. (2014). Estrategia de inversión bursátil y reconocimiento gráfico de patrones: Aplicación sobre datos intradía del índice Dow Jones. Cuadernos de Administración.
Corrente, S., Figueira, J. R., Greco, S., & Słowiński, R. (2017). A robust ranking method extending ELECTRE III to hierarchy of interacting criteria. Omega (United Kingdom), 73, 1–17. DOI: https://doi.org/10.1016/j.omega.2016.11.008
Corrente, S., Greco, S., & Słowiński, R. (2012). Multiple criteria hierarchy process in robust ordinal regression. Decision Support Systems, 53(3), 660–674. https://doi.org/10.1016/j.dss.2012.03.004 DOI: https://doi.org/10.1016/j.dss.2012.03.004
Creamer, G. (2012). Model calibration and automated trading agent for euro futures. Quantitative Finance, 12(4), 531–545. DOI: https://doi.org/10.1080/14697688.2012.664921
Creamer, G., & Freund, Y. (2007). A boosting approach for automated. Journal of Trading, 2(3), 84–96. DOI: https://doi.org/10.3905/jot.2007.688953
Chahuán, K. (2018). Relación Dow Jones sustainability index Chile e ingresos, resultados y rentabilidad sobre patrimonio de empresas. Capic Review, 16. https://doi.org/10.35928/cr.vol16.2018.68 DOI: https://doi.org/10.35928/cr.vol16.2018.68
Dempster, M. A. H., Payne, T. W., Romahi, Y., & Thompson, G. W. T. (2001). Computational learning techniques for intraday FX trading using popular technical indicators. EEE Transactions on Neural Networks, 12(4), 744–754. DOI: https://doi.org/10.1109/72.935088
Ehrgott, M., Klamroth, K., & Schwehm, C. (2004). An MCDM approach to portfolio optimization. 155, 752–770. https://doi.org/10.1016/S0377-2217(02)00881-0 DOI: https://doi.org/10.1016/S0377-2217(02)00881-0
Elselmy, H., Ghoneim, A., & Elkhodary, I. (2019). Portfolio selection factors: Egypt equity market case study. ACM International Conference Proceeding Series, 212–216. DOI: https://doi.org/10.1145/3328833.3328858
Giannoulis, C. & Ishizaka, A. (2010). A web-based decision support system with ELECTREIII for a personalized ranking of British universities,” Decision Support Systems, 48(3), 488-497. DOI: https://doi.org/10.1016/j.dss.2009.06.008
Govindan, K., & Jepsen, M. B. (2016). ELECTRE: A comprehensive literature review on methodologies and applications. European Journal of Operational Research, 250(1), 1–29. DOI: https://doi.org/10.1016/j.ejor.2015.07.019
Guerrero-Baena, D. D., Gómez-Limón, J. A., & Fruet Cardozo, V. V. (2014). Are multi-criteria decision making techniques useful for solving corporate finance problems? A bibliometric analysis. Revista de Metodos Cuantitativos Para La Economia y La Empresa, 17(1), 60–79.
Kimoto, T., Asakawa, K., Yoda, M., & Takeoka, M. (1993). Stock market prediction system with modular neural networks. Neural Networks in Finance and Investing, 343–357.
Lima, A., & Soares, V. (2013). Financial ratios applied to portfolio selection : Electre III methodology in buy-and-hold strategy Indicadores financeiros aplicados à seleção de carteiras : Metodologia Electre III numa estratégia de buy-and-hold. Organizações Em Contexto, 9(17), 281–319. DOI: https://doi.org/10.15603/1982-8756/roc.v9n17p281-319
Lopez-Dumrauf, G. (2003). Finanzas corporativas. Buenos Aires: Grupo Guia.
Lu, C. J., Lee, T. S., & Chiu, C. C. (2009). Financial time series forecasting using independent component analysis and support vector regression. Decision Support Systems, 47(2), 115–125. DOI: https://doi.org/10.1016/j.dss.2009.02.001
Macharis, C., Brans, J. P., Mareschal, B., (1998) The GDSS ROMETHEE procedure: a PROMETHEE–GAIA based procedure for group decision support, Journal of Decision Systems, 7, pp. 283–307.
Mahfoud, S., & Mani, G. (1996). Financial forecasting using genetic algorithms. Applied Artificial Intelligence, 10(6), 543–565. DOI: https://doi.org/10.1080/088395196118425
Mandziuk, J., & Jaruszewicz, M. (2011). Neuro-genetic system for stock index prediction. Journal of Intelligent & Fuzzy Systems, 22(2–3), 93–123. DOI: https://doi.org/10.3233/IFS-2011-0479
Milanesi, G. (2016). Un modelo de opciones barreras para estimar las probabilidades de fracasos financieros de empresas. Barrier options model for estimate firm´s probabilities for financial distress. TEC Empresarial. https://doi.org/10.18845/te.v10i3.2936 DOI: https://doi.org/10.18845/te.v10i3.2936
Mansour, N., Cherif, M. S., & Abdelfattah, W. (2019). Multi-objective imprecise programming for financial portfolio selection with fuzzy returns. Expert Systems With Applications. DOI: https://doi.org/10.1016/j.eswa.2019.07.027
Mohammad, J., Mohammad, E., & Sanam, B. (2012). Selection of Portfolio by using Multi Attributed Decision Making. American Journal of Scientific Research, 1450-223X(44), 15–29.
Moody, J., & Saffell, M. (2001). Learning to trade via direct reinforcement. IEEE Transactions on Neural Networks, 12(4), 875–889. DOI: https://doi.org/10.1109/72.935097
Moody, J., Wu, L., Liao, Y., & Saffell, M. (1998). Performance functions and reinforcement learning for trading systems and portfolios. Journal of Forecasting, 17(5), 441–471. DOI: https://doi.org/10.1002/(SICI)1099-131X(1998090)17:5/6<441::AID-FOR707>3.0.CO;2-#
OJ., L., J.W., & Zhang, B. T. (2002). Stock trading system using reinforcement learning with cooperative agents. Proceedings of the 19th International Conference on Machine Learning, 451–458.
Pätäri, E., Karell, V., Luukka, P., & Yeomans, J. S. (2017). Comparison of the multicriteria decision-making methods for equity portfolio selection: The U.S. evidence. European Journal of Operational Research, 265(2), 655–672. https://doi.org/10.1016/j.ejor.2017.08.001 DOI: https://doi.org/10.1016/j.ejor.2017.08.001
Shabani Vezmelai, A., Lashgari, Z., & Keyghobadi, A. (2015). Portfolio selection using ELECTRE III: Evidence from Tehran Stock Exchange. Decision Science Letters, 4(2), 227–236. DOI: https://doi.org/10.5267/j.dsl.2014.11.003
Shah, P., Mallory, M. L., Ando, A. W., & Guntenspergen, G. R. (2017). Fine-resolution conservation planning with limited climate-change information. Conservation Biology, 31(2). DOI: https://doi.org/10.1111/cobi.12793
Sharpe, W. (1964). Capital Asset Prices. The Journal of Finance, 19(3). DOI: https://doi.org/10.2307/2977928
Spronk, J., Steuer, R. E., & Zopounidis, C. (2016). Multicrieria Decision Aid/Analysis in Finance. In International Series In operations Research & Management Science (pp. 1011–1065). DOI: https://doi.org/10.1007/978-1-4939-3094-4_24
Suárez, L., Pimiento, N., & Duarte, J. (2018). Selección de portafolios de inversión socialmente responsables usando el método de las restricciones y la técnica multicriterio Proceso Analítico Jerárquico. Revista EIA. https://doi.org/10.24050/reia.v0i0.634 DOI: https://doi.org/10.24050/reia.v0i0.634
Sun, Y. F., Grace, A., Teo, K. L., & Zhou, G. L. (2015). Portfolio optimization using a new probabilistic risk measure. Journal of Industrial and Management Optimization, 11, 1275–1283. DOI: https://doi.org/10.3934/jimo.2015.11.1275
Sun, X., Zheng, X. & Li, D. (2013). Recent advances in mathematical programming with semicontinuous variables and cardinality constraint. Journal Operations Research Society of China, 1, 55–77. DOI: https://doi.org/10.1007/s40305-013-0004-0
Tay, F. E. H., & Cao, L. J. (2002). Modified support vector machines in financial time series forecasting. Neurocomputing, 48(1–4), 559–565. DOI: https://doi.org/10.1016/S0925-2312(01)00676-2
Teo, K. & Yang, X. (2001). Portfolio selection problem with minimax type risk function. Annals of Operations Research, 101, 333–349. DOI: https://doi.org/10.1023/A:1010909632198
Tsang, E., Yung, P., & Li, J. (2004). ‘EDDIE-automation’, A Decision Support Tool for Financial Forecasting. Decision Support Systems, Periodical Style, 37, 559–565. DOI: https://doi.org/10.1016/S0167-9236(03)00087-3
Tian, Y., Fang, S., Deng, Z. & Jin, Q. (2016). Cardinality constrained portfolio selection problem: A completely positive programming approach. Journal of Industrial and Management Optimization, 12, 1041–1056. DOI: https://doi.org/10.3934/jimo.2016.12.1041
Useche, A. J. (2015). Construcción de portafolios de inversión desde las finanzas del comportamiento: una revisión crítica. Cuadernos de Administración. https://doi.org/10.11144/javeriana.cao28-51.cpif DOI: https://doi.org/10.11144/Javeriana.cao28-51.cpif
Zhu, S.S., Li, D., & Sun, X. L. (2010). Portfolio selection with marginal risk control. The Journal of Computational Finance, 14(1), 3–28. https://doi.org/doi:10.21314/JCF.2010.213 DOI: https://doi.org/10.21314/JCF.2010.213
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