Portfolio Optimization using Artificial Intelligence: A Systematic Literature Review

Gustavo Carvalho Santos, Flavio Barboza, Antônio Cláudio Paschoarelli Veiga, Kamyr Gomes

Resumo


Artificial intelligence (AI) models can help investors find portfolios in which the focus is to optimize the risk-return relationship. There are several algorithms and techniques in the literature that allow the application of tests to a set of historical data for the selection and validation of investment portfolios. Based on this, this research intends to examine the contribution of the main machine learning techniques used in portfolio management through a systematic literature review. By using the Methodi Ordinatio for selection and ranking of articles, we classified papers considering object of study, type of AI used, period of analysis, data frequency, balance and cardinality. In addition, we detail the main contributions and trends conceived until the year 2020. Therefore, our findings reveal gaps and suggest future works on the topic.



Palavras-chave


Artificial Intelligence, Portfolio Management, Literature Review

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Referências


Aboussalah, A. M., & Lee, C.-G. (2020). Continuous control with stacked deep dynamic recurrent reinforcement learning for portfolio optimization. Expert Systems with Applications, 140, 112891.

Afonso, M. H., Souza, J. de, Ensslin, S. R., & Ensslin, L. (2011). Como construir conhecimento sobre o tema de pesquisa? Aplicação do processo Proknow-C na busca de literatura sobre avaliação do desenvolvimento sustentável. Revista de Gestão Social e Ambiental, 5(2), 47–62.

Almahdi, S., & Yang, S. Y. (2017). An adaptive portfolio trading system: A risk-return portfolio optimization using recurrent reinforcement learning with expected maximum drawdown. Expert Systems with Applications, 87, 267–279.

Bahrammirzaee, A. (2010). A comparative survey of artificial intelligence applications in finance: Artificial neural networks, expert system and hybrid intelligent systems. Neural Computing and Applications, 19(8), 1165–1195. doi: 10.1007/s00521-010-0362-z

Bustos, O., & Pomares-Quimbaya, A. (2020). Stock market movement forecast: A Systematic review. Expert Systems with Applications, 156, 113464. doi: 10.1016/j.eswa.2020.113464

Cavalcante, R. C., Brasileiro, R. C., Souza, V. L. F., Nobrega, J. P., & Oliveira, A. L. I. (2016). Computational Intelligence and Financial Markets: A Survey and Future Directions. Expert Systems with Applications, 55, 194–211. doi: https://doi.org/10.1016/j.eswa.2016.02.006

Chang, T.-J., Meade, N., Beasley, J. E., & Sharaiha, Y. M. (2000). Heuristics for cardinality constrained portfolio optimisation. Computers & Operations Research, 27(13), 1271–1302.

Fama, E. F. (1995). Random walks in stock market prices. Financial Analysts Journal, 51(1), 75–80.

Henriques, I. C., Sobreiro, V. A., Kimura, H., & Mariano, E. B. (2020). Two-stage DEA in banks: Terminological controversies and future directions. Expert Systems with Applications, 161, 113632. doi: 10.1016/j.eswa.2020.113632

Huang, C.-F. (2012). A hybrid stock selection model using genetic algorithms and support vector regression. Applied Soft Computing, 12(2), 807–818.

Inuiguchi, M., & Ramík, J. (2000). Possibilistic linear programming: A brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem. Fuzzy Sets and Systems, 111(1), 3–28. doi: https://doi.org/10.1016/S0165-0114(98)00449-7

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.

Li, X., Qin, Z., & Kar, S. (2010). Mean-variance-skewness model for portfolio selection with fuzzy returns. European Journal of Operational Research, 202(1), 239–247.

Lwin, K., Qu, R., & Kendall, G. (2014). A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization. Applied Soft Computing, 24, 757–772. doi: 10.1016/j.asoc.2014.08.026

Mammeri, Z. (2019). Reinforcement Learning Based Routing in Networks: Review and Classification of Approaches. IEEE Access, 7, 55916–55950. doi: 10.1109/ACCESS.2019.2913776

Mangram, M. E. (2013). A simplified perspective of the Markowitz portfolio theory. Global Journal of Business Research, 7(1), 59–70.

Markowitz, H. (1952). PORTFOLIO SELECTION*. The Journal of Finance, 7(1), 77–91. doi: https://doi.org/10.1111/j.1540-6261.1952.tb01525.x

Mencarelli, L., & D’Ambrosio, C. (2019). Complex portfolio selection via convex mixed-integer quadratic programming: A survey. International Transactions in Operational Research, 26(2), 389–414. doi: https://doi.org/10.1111/itor.12541

Nobre, J., & Neves, R. F. (2019). Combining principal component analysis, discrete wavelet transform and XGBoost to trade in the financial markets. Expert Systems with Applications, 125, 181–194.

Pagani, R. N., Kovaleski, J. L., & Resende, L. M. (2015). Methodi Ordinatio: A proposed methodology to select and rank relevant scientific papers encompassing the impact factor, number of citation, and year of publication. Scientometrics, 105(3), 2109–2135.

Rubinstein, M. (2002). Markowitz’s" portfolio selection": A fifty-year retrospective. The Journal of Finance, 57(3), 1041–1045.

Skolpadungket, P., Dahal, K., & Harnpornchai, N. (2007). Portfolio optimization using multi-obj ective genetic algorithms. 2007 IEEE Congress on Evolutionary Computation, 516–523. doi: 10.1109/CEC.2007.4424514

Soleymani, F., & Paquet, E. (2020). Financial portfolio optimization with online deep reinforcement learning and restricted stacked autoencoder—DeepBreath. Expert Systems with Applications, 156, 113456.

Vo, N. N., He, X., Liu, S., & Xu, G. (2019). Deep learning for decision making and the optimization of socially responsible investments and portfolio. Decision Support Systems, 124, 113097.

Weng, L., Sun, X., Xia, M., Liu, J., & Xu, Y. (2020). Portfolio trading system of digital currencies: A deep reinforcement learning with multidimensional attention gating mechanism. Neurocomputing, 402, 171–182.




DOI: https://doi.org/10.5585/exactaep.2022.21882

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