1.
종목과 포트폴리오. 같은 다른 개념으로 이해합니다. 종목을 모으면 포트폴이오이지만 투자로 접근할 때는 전혀 다른 분석이 필요로 합니다. 이런 의미에서 나온 개념이 Protfolio Optimization이 아닐까 합니다. 포트폴리오 투자에서는 종목별 손익보다는 모듬의 손익이 중요하기 때문입니다. 로보어드바이저로 대중화된 최적화는 금융공학에 뿌리를 두지만 최근에는 기계학습기술을 도입하면서 확장중입니다.
아래 소개하는 논문을 포트폴리오최적화와 관련한 논문을 분석하여 어떤 기법을 적용했는지를 분석합니다.
Analysis of new approaches used in portfolio optimization: a systematic literature review
이상의 방법으로 41개의 논문을 선정하였습니다. 그리고 개별 논문이 채택한 최적화방법을 기준으로 분류하면 아래와 같습니다.
제가 위 논문을 보면서 제가 인상이 깊었던 부분은 2장입니다. 물론 포트폴리오 최적화와 관련한 수많은 논문을 정리한 것도 유용하지만 방법론에 관심이 갔습니다.
2. Research method
– Table 1 Protocol for Systematic Literature Review.
상상을 해보죠. 투자를 주업으로 하는 개인이나 팀에서 논문분석을 하려고 합니다. 어떤 방법을 만들어 연구를 할까요? 이 논문이 제시한 방법이 무척 훌륭합니다. Protocol로 정의한 방법을 변형하여 다른 주제를 연구하더라도 유의미한 결과를 얻을 수 있을 듯 합니다. 특히 요즘과 같이 구글을 통해 다양한 수준의 논문을 구할 수 있는 조건에서 방법론에 대한 아이디어를 제공합니다.
2.
그러면 41개의 논문은 무엇일까요? 아래입니다.
Ackermann, F., Pohl, W., & Schmedders, K. (2016). Optimal and naive diversification in currency markets. Management Science, 63(10), 3347-3360. http://dx.doi.org/10.1287/mnsc.2016.2497. [ Links ]
Al Janabi, M. A. M. (2014). Optimal and investable portfolios: an empirical analysis with scenario optimization algorithms under crisis market prospects. Economic Modelling, 40, 369-381. http://dx.doi.org/10.1016/j.econmod.2013.11.021. [ Links ]
Algarvio, H., Lopes, F., Sousa, J., & Lagarto, J. (2017). Multi-agent electricity markets: retailer portfolio optimization using Markowitz theory. Electric Power Systems Research, 148, 282-294. http://dx.doi.org/10.1016/j.epsr.2017.02.031. [ Links ]
Ali, Ö. G., Akçay, Y., Sayman, S., Yılmaz, E., & Özçelik, M. H. (2016). Cross-selling investment products with a Win-win perspective in portfolio optimization. Operations Research, 65(1), 55-74. http://dx.doi.org/10.1287/opre.2016.1556. [ Links ]
Ayub, U., Shah, S. Z. A., & Abbas, Q. (2015). Robust analysis for downside risk in portfolio management for a volatile stock market. Economic Modelling, 44, 86-96. http://dx.doi.org/10.1016/j.econmod.2014.10.001. [ Links ]
Babaei, S., Sepehri, M. M., & Babaei, E. (2015). Multi-objective portfolio optimization considering the dependence structure of asset returns. European Journal of Operational Research, 244(2), 525-539. http://dx.doi.org/10.1016/j.ejor.2015.01.025. [ Links ]
Ban, G.-Y., El Karoui, N., & Lim, A. E. B. (2016). Machine learning and portfolio optimization. Management Science, 64(3), 1136-1154. http://dx.doi.org/10.1287/mnsc.2016.2644. [ Links ]
Bastos, L. D. S. L., Mendes, M. L., Nunes, D. R. D. L., Melo, A. C. S., & Carneiro, M. P. (2017). A systematic literature review on the joint replenishment problem solutions: 2006-2015. Production, 27(0), 27. http://dx.doi.org/10.1590/0103-6513.222916. [ Links ]
Behr, P., Guettler, A., & Miebs, F. (2013). On portfolio optimization: imposing the right constraints. Journal of Banking & Finance, 37(4), 1232-1242. http://dx.doi.org/10.1016/j.jbankfin.2012.11.020. [ Links ]
Benati, S. (2015). Using medians in portfolio optimization. The Journal of the Operational Research Society, 66(5), 720-731. http://dx.doi.org/10.1057/jors.2014.57. [ Links ]
Bensaïda, A., Boubaker, S., & Nguyen, D. K. (2018). The shifting dependence dynamics between the G7 stock markets. Quantitative Finance, 18(5), 801-812. http://dx.doi.org/10.1080/14697688.2017.1419628. [ Links ]
Berutich, J. M., López, F., Luna, F., & Quintana, D. (2016). Robust technical trading strategies using GP for algorithmic portfolio selection. Expert Systems with Applications, 46, 307-315. http://dx.doi.org/10.1016/j.eswa.2015.10.040. [ Links ]
Brodie, J., Daubechies, I., De Mol, C., Giannone, D., & Loris, I. (2009). Sparse and stable Markowitz portfolios. Proceedings of the National Academy of Sciences of the United States of America, 106(30), 12267-12272. http://dx.doi.org/10.1073/pnas.0904287106. PMid:19617537. [ Links ]
Ceren, T. Ş., & Köksalan, M. (2014). Effects of Multiple Criteria on Portfolio Optimization. International Journal of Information Technology & Decision Making, 13(01), 77-99. http://dx.doi.org/10.1142/S0219622014500047. [ Links ]
Chen, C., & Zhou, Y. (2018). Robust multiobjective portfolio with higher moments. Expert Systems with Applications, 100, 165-181. http://dx.doi.org/10.1016/j.eswa.2018.02.004. [ Links ]
Dai, Z., & Wen, F. (2018). Some improved sparse and stable portfolio optimization problems. Finance Research Letters, 27, 46-52. http://dx.doi.org/10.1016/j.frl.2018.02.026. [ Links ]
DeMiguel, V., Garlappi, L., & Uppal, R. (2009a). Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy? Review of Financial Studies, 22(5), 1915-1953. http://dx.doi.org/10.1093/rfs/hhm075. [ Links ]
DeMiguel, V., Garlappi, L., Nogales, F. J., & Uppal, R. (2009b). A generalized approach to portfolio optimization: improving performance by constraining portfolio norms. Management Science, 55(5), 798-812. http://dx.doi.org/10.1287/mnsc.1080.0986. [ Links ]
Ertenlice, O., & Kalayci, C. B. (2018). A survey of swarm intelligence for portfolio optimization: algorithms and applications. Swarm and Evolutionary Computation, 39, 36-52. http://dx.doi.org/10.1016/j.swevo.2018.01.009. [ Links ]
Fabozzi, F. J., Kolm, P. N., Pachamanova, D. A., & Focardi, S. M. (2007). Robust portfolio optimization. Journal of Portfolio Management, 33(3), 40-48. http://dx.doi.org/10.3905/jpm.2007.684751. [ Links ]
Fan, J., Zhang, J., & Yu, K. (2012). Vast portfolio selection with gross-exposure constraints. Journal of the American Statistical Association, 107(498), 592-606. http://dx.doi.org/10.1080/01621459.2012.682825. PMid:23293404. [ Links ]
Furlan, P. K., & Laurindo, F. J. B. (2017). Agrupamentos epistemológicos de artigos publicados sobre big data analytics. Transinformação, 29(1), 91-100. http://dx.doi.org/10.1590/2318-08892017000100009. [ Links ]
García, F., Guijarro, F., & Oliver, J. (2018). Index tracking optimization with cardinality constraint: a performance comparison of genetic algorithms and tabu search heuristics. Neural Computing & Applications, 30(8), 2625-2641. http://dx.doi.org/10.1007/s00521-017-2882-2. [ Links ]
Goodman, D., & Brette, R. (2008). Brian: a simulator for spiking neural networks in Python. Frontiers in Neuroinformatics, 2, 5. http://dx.doi.org/10.3389/neuro.11.005.2008. PMid:19115011. [ Links ]
Hu, Y., Liu, K., Zhang, X., Su, L., Ngai, E. W. T., & Liu, M. (2015). Application of evolutionary computation for rule discovery in stock algorithmic trading: a literature review. Applied Soft Computing, 36, 534-551. http://dx.doi.org/10.1016/j.asoc.2015.07.008. [ Links ]
Ji, R., Lejeune, M. A., & Prasad, S. Y. (2017). Properties, formulations, and algorithms for portfolio optimization using Mean-Gini criteria. Annals of Operations Research, 248(1-2), 305-343. http://dx.doi.org/10.1007/s10479-016-2230-4. [ Links ]
Karakalidis, A., & Sifaleras, A. (2017). Solving portfolio optimization problems using AMPL. In: E. Grigoroudis & M. Doumpos (Eds.), Operational research in business and economics (pp. 167-184). Cham: Springer. http://dx.doi.org/10.1007/978-3-319-33003-7_8. [ Links ]
Kumar, D., & Mishra, K. K. (2017). Portfolio optimization using novel co-variance guided Artificial Bee Colony algorithm. Swarm and Evolutionary Computation, 34(2), 353-369. http://dx.doi.org/10.1016/j.swevo.2016.11.003. [ Links ]
Le Thi, H. A., & Moeini, M. (2014). Long-short portfolio optimization under cardinality constraints by difference of convex functions algorithm. Journal of Optimization Theory and Applications, 161(1), 199-224. http://dx.doi.org/10.1007/s10957-012-0197-0. [ Links ]
Levy, M., & Kaplanski, G. (2015). Portfolio selection in a two-regime world. European Journal of Operational Research, 242(2), 514-524. http://dx.doi.org/10.1016/j.ejor.2014.10.012. [ Links ]
Li, Q., & Bao, L. (2014). Enhanced index tracking with multiple time-scale analysis. Economic Modelling, 39, 282-292. http://dx.doi.org/10.1016/j.econmod.2014.03.009. [ Links ]
Liu, Y.-J., & Zhang, W.-G. (2015). A multi-period fuzzy portfolio optimization model with minimum transaction lots. European Journal of Operational Research, 10(2), 143-164. http://dx.doi.org/10.1016/j.ejor.2014.10.061. [ Links ]
Macedo, L. L., Godinho, P., & Alves, M. J. (2017). Mean-semivariance portfolio optimization with multiobjective evolutionary algorithms and technical analysis rules. Expert Systems with Applications, 79, 33-43. http://dx.doi.org/10.1016/j.eswa.2017.02.033. [ Links ]
Mansini, R., Ogryczak, W., & Speranza, M. G. (2014). Twenty years of linear programming based portfolio optimization. European Journal of Operational Research, 234(2), 518-535. http://dx.doi.org/10.1016/j.ejor.2013.08.035. [ Links ]
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91. http://dx.doi.org/10.1111/j.1540-6261.1952.tb01525.x. [ Links ]
Marzban, S., Mahootchi, M., & Arshadi Khamseh, A. (2015). Developing a multi-period robust optimization model considering American style options. Annals of Operations Research, 233(1), 305-320. http://dx.doi.org/10.1007/s10479-013-1461-x. [ Links ]
McKinney, W. (2010). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference, 1697900(Scipy), 50-59. Retrieved in 2019, November 27, from http://conference.scipy.org/proceedings/scipy2010/pdfs/proceedings.pdf [ Links ]
Meghwani, S. S., & Thakur, M. (2018). Multi-objective heuristic algorithms for practical portfolio optimization and rebalancing with transaction cost. Applied Soft Computing, 67, 865-894. http://dx.doi.org/10.1016/j.asoc.2017.09.025. [ Links ]
Merton, R. C. (1971). Optimum consumption and portfolio rules in a continuous-time model. In Stochastic optimization models in finance (pp. 621-661). New York: Academic Press. [ Links ]
Mishra, S. K., Panda, G., & Majhi, B. (2016). Prediction based mean-variance model for constrained portfolio assets selection using multiobjective evolutionary algorithms. Swarm and Evolutionary Computation, 28, 117-130. http://dx.doi.org/10.1016/j.swevo.2016.01.007. [ Links ]
Mitchell, S., O’Sullivan, M., & Dunning, I. (2011). PuLP: a linear programming toolkit for Python. Auckland: The University of Auckland. Retrieved in 2019, November 27, from http://www.optimization-online.org/DB_FILE/2011/09/3178.pdf [ Links ]
Morandi, M. I. W. M., & Camargo, L. F. R. (2015). Revisão sistemática da literatura. In A. Dresch, D. P. Lacerda & J. A. V. Antunes Jr. (Eds.), (pp. 141-175). Design science research: método de pesquisa para avanço da ciência e tecnologia. Porto Alegre: The Bookman. [ Links ]
Pai, G. A. V. (2017). Fuzzy decision theory based metaheuristic portfolio optimization and active rebalancing using interval type-2 fuzzy sets. IEEE Transactions on Fuzzy Systems, 25(2), 377-391. http://dx.doi.org/10.1109/TFUZZ.2016.2633972. [ Links ]
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12(1), 2825-2830. [ Links ]
Pekár, J., Čičková, Z., & Brezina, I. (2016). Portfolio performance measurement using differential evolution. Central European Journal of Operations Research, 24(2), 421-433. http://dx.doi.org/10.1007/s10100-015-0393-8. [ Links ]
Pflug, G. C., Pichler, A., & Wozabal, D. (2012). The 1/N investment strategy is optimal under high model ambiguity. Journal of Banking & Finance, 36(2), 410-417. http://dx.doi.org/10.1016/j.jbankfin.2011.07.018. [ Links ]
Pflug, G., & Wozabal, D. (2007). Ambiguity in portfolio selection. Quantitative Finance, 7(4), 435-442. http://dx.doi.org/10.1080/14697680701455410. [ Links ]
Qu, B. Y., Zhou, Q., Xiao, J. M., Liang, J. J., & Suganthan, P. N. (2017). Large-scale portfolio optimization using multiobjective evolutionary algorithms and preselection methods. Mathematical Problems in Engineering, 2017, 1-14. http://dx.doi.org/10.1155/2017/4197914. [ Links ]
Ren, F., Lu, Y. N., Li, S. P., Jiang, X. F., Zhong, L. X., & Qiu, T. (2017). Dynamic portfolio strategy using clustering approach. PLoS One, 12(1), e0169299. http://dx.doi.org/10.1371/journal.pone.0169299. PMid:28129333. [ Links ]
Reveiz-Herault, A. (2016). An active asset management investment process for drawdown-averse investors. Intelligent Systems in Accounting, Finance & Management, 23(1-2), 85-96. http://dx.doi.org/10.1002/isaf.1375. [ Links ]
Rezaei Pouya, A., Solimanpur, M., & Jahangoshai Rezaee, M. (2016). Solving multi-objective portfolio optimization problem using invasive weed optimization. Swarm and Evolutionary Computation, 28, 42-57. http://dx.doi.org/10.1016/j.swevo.2016.01.001. [ Links ]
Rubio, A., Bermúdez, J. D., & Vercher, E. (2016). Forecasting portfolio returns using weighted fuzzy time series methods. International Journal of Approximate Reasoning, 75, 1-12. http://dx.doi.org/10.1016/j.ijar.2016.03.007. [ Links ]
Rubio, A., Bermúdez, J. D., & Vercher, E. (2017). Improving stock index forecasts by using a new weighted fuzzy-trend time series method. Expert Systems with Applications, 76, 12-20. http://dx.doi.org/10.1016/j.eswa.2017.01.049. [ Links ]
Saborido, R., Ruiz, A. B., Bermúdez, J. D., Vercher, E., & Luque, M. (2016). Evolutionary multi-objective optimization algorithms for fuzzy portfolio selection. Applied Soft Computing, 39, 48-63. http://dx.doi.org/10.1016/j.asoc.2015.11.005. [ Links ]
Sharma, C., & Banerjee, K. (2015). A study of correlations in the stock market. Physica A, 432, 321-330. http://dx.doi.org/10.1016/j.physa.2015.03.061. [ Links ]
Silva, A., Neves, R., & Horta, N. (2015). A hybrid approach to portfolio composition based on fundamental and technical indicators. Expert Systems with Applications, 42(4), 2036-2048. http://dx.doi.org/10.1016/j.eswa.2014.09.050. [ Links ]
Sun, X., & Liu, Z. (2016). Optimal portfolio strategy with cross-correlation matrix composed by DCCA coefficients: evidence from the Chinese stock market. Physica A, 444, 667-679. http://dx.doi.org/10.1016/j.physa.2015.10.065. [ Links ]
Uryasev, S. (2000). Conditional value-at-risk: Optimization algorithms and applications. In Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520) (pp. 49-57). New York: IEEE. http://dx.doi.org/10.1109/CIFER.2000.844598. [ Links ]
Vercher, E., & Bermúdez, J. D. (2015). Portfolio optimization using a credibility mean-absolute semi-deviation model. Expert Systems with Applications, 42(20), 7121-7131. http://dx.doi.org/10.1016/j.eswa.2015.05.020. [ Links ]
Yu, D., Wang, W., Zhang, W., & Zhang, S. (2018). A bibliometric analysis of research on multiple criteria decision making. Current Science, 114(4), 747. http://dx.doi.org/10.18520/cs/v114/i04/747-758. [ Links ]
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. [ Links ]
Zhang, W. G., & Liu, Y. J. (2014). Credibilitic mean-variance model for multi-period portfolio selection problem with risk control. OR-Spektrum, 36(1), 113-132. http://dx.doi.org/10.1007/s00291-013-0335-6. [ Links ]
Zhang, Y., Li, X., & Guo, S. (2018). Portfolio selection problems with Markowitz’s mean-variance framework: a review of literature. Fuzzy Optimization and Decision Making, 17(2), 1-34. http://dx.doi.org/10.1007/s10700-017-9266-z. [ Links ]
Zhao, L., Li, W., Fenu, A., Podobnik, B., Wang, Y., & Stanley, H. E. (2018a). The q-dependent detrended cross-correlation analysis of stock market. Journal of Statistical Mechanics, 2018(2), 1-28. http://dx.doi.org/10.1088/1742-5468/aa9db0. [ Links ]
Zhao, L., Wang, G. J., Wang, M., Bao, W., Li, W., & Stanley, H. E. (2018b). Stock market as temporal network. Physica A, 506, 1104-1112. http://dx.doi.org/10.1016/j.physa.2018.05.039. [ Links ]
