산업경영시스템학회지 (Journal of Korean Society of Industrial and Systems Engineering)

  • 제46권4호
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  • Pages.63-73
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  • 2023
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  • 2005-0461(pISSN)
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  • 2287-7975(eISSN)
한국산업경영시스템학회 (Society of Korea Industrial and System Engineering)
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K-shape 군집화 기반 블랙-리터만 포트폴리오 구성

Black-Litterman Portfolio with K-shape Clustering

  • 김예지 (가천대학교 AI소프트웨어학부) ;
  • 조풍진 (가천대학교 AI소프트웨어학부)
  • 투고 : 2023.09.23
  • 심사 : 2023.11.09
  • 발행 : 2023.12.31
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초록

This study explores modern portfolio theory by integrating the Black-Litterman portfolio with time-series clustering, specificially emphasizing K-shape clustering methodology. K-shape clustering enables grouping time-series data effectively, enhancing the ability to plan and manage investments in stock markets when combined with the Black-Litterman portfolio. Based on the patterns of stock markets, the objective is to understand the relationship between past market data and planning future investment strategies through backtesting. Additionally, by examining diverse learning and investment periods, it is identified optimal strategies to boost portfolio returns while efficiently managing associated risks. For comparative analysis, traditional Markowitz portfolio is also assessed in conjunction with clustering techniques utilizing K-Means and K-Means with Dynamic Time Warping. It is suggested that the combination of K-shape and the Black-Litterman model significantly enhances portfolio optimization in the stock market, providing valuable insights for making stable portfolio investment decisions. The achieved sharpe ratio of 0.722 indicates a significantly higher performance when compared to other benchmarks, underlining the effectiveness of the K-shape and Black-Litterman integration in portfolio optimization.

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과제정보

This work has been supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1F1A1062917). This work was supported by the Gachon University research fund of 2023.(GCU-202300670001)

참고문헌

  1. Aghabozorgi, S. and The, Y. W., Stock Market Co-movement Assessment Using a Three-phase Clustering Method, Expert Systems with Applications, 2014, Vol. 41, No. 4, pp. 1301-1304. https://doi.org/10.1016/j.eswa.2013.08.028
  2. Barua, R. and Sharma, A. K., Dynamic Black Litterman Portfolios with Views Derived Via CNN-bilstm Predictions, Finance Research Letters, 2022, Vol. 49, pp. 103111.
  3. Barua, R. and Sharma, A. K., Using Fear, Greed and Machine Learning for Optimizing Global Portfolios: A Black-Litterman Approach, Finance Research Letters, 2023, Vol. 58, p. 104515.
  4. Berndt, D. J. and Clifford, J., Using Dynamic Time Warping to Find Patterns in Time Series, in Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, 1994, ACM, pp. 359-370.
  5. Black, F. and Litterman, R., Global Portfolio Optimization, Financial Analysis Journal, 1992, Vol. 48, No. 5, pp. 28-43. https://doi.org/10.2469/faj.v48.n5.28
  6. Cho, P., Lee, M., and Song, J. W., Clustering-driven Pair Trading Portfolio Investment in Korean Stock Market, Journal of Korean Society of Industrial and Systems Engineering, 2022, Vol. 45, pp. 123-130. https://doi.org/10.11627/jksie.2022.45.3.123
  7. Choi, Y. M., Liu, W., and Jhang, H., A Study on Strategic Asset Allocation using the Black-Litermann Model of Global Investors, The Korean Journal of Financial Management, 2021, Vol. 38, No. 4, pp. 293-314.
  8. Hartigan, J. A. and Wong, M. A., Algorithm as 136: A k-means Clustering Algorithm, Journal of the Royal Statistical Society. Series C, 1979, Vol. 28, No. 1, pp. 100-108. https://doi.org/10.2307/2346830
  9. Markowitz, H., Portfolio Selection, The Journal of Finance, 1952, Vol. 7, No. 1, pp.77-91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x
  10. Moon, K. S. and Kim, H., Efficient Asset Allocation Based on Prediction with Adaptive Data Selection, Economic Computation & Economic Cybernetics Studies & Research, 2023, Vol. 57, No. 1, pp. 57-72.
  11. Niennattrakul, V., Ratanamahatana, C. A., On Clustering Multimedia Time Series Data Using k-means and Dynamic Time Warping, In 2007 International Conference on Multimedia and Ubiquitous Engineering, 2007, IEEE, pp. 733-738.
  12. Pang, T. and Karan, C., A Closed-form Solution of the Black-Litterman Model with Conditional Value at Risk, Operations Research Letters, 2018, Vol. 46, No. 1, pp. 103-108. https://doi.org/10.1016/j.orl.2017.11.014
  13. Paparrizos, J. and Gravano, L., k-shape: Efficient and Accurate Clustering of Time Series, In Proceedings of 2015 ACM SIGMOD International Conference on Management of Data, 2015, ACM, pp. 1855-1870.
  14. Putra, Y. E., Saepudin, D., and Aditsania, A., Portfolio Selection of Kompas100 Stocks Index Using b-spline Based Clustering, Procedia Computer Science, 2021, Vol. 179, pp. 375-382. https://doi.org/10.1016/j.procs.2021.01.019
  15. Rezaei, H., Faaljou, H., and Mansourfar, G., Intelligent Asset Allocation Using Predictions of Deep Frequency Decomposition, Expert Systems with Applications, 2021, Vol. 186, p. 115715.
  16. Sahamkhadam, M., Stephan, A., and Ostermark, R., Copula-based Black-litterman Portfolio Optimization, European Journal of Operational Research, 2022, Vol. 297, No. 3, pp. 1055-1070. https://doi.org/10.1016/j.ejor.2021.06.015
  17. Tayali, S. T., A Novel Backtesting Methodology for Clustering in Mean-variance Portfolio Optimization, Knowledge-Based Systems, 2020, Vol. 209, p. 106454.
  18. Wang, Y. and Aste, T., Dynamic Portfolio Optimization with Inverse Covariance Clustering, Expert Systems with Applications, 2023, Vol. 213, pp. 118739.
  19. Wu, D., Wang, X., and Wu, S., Construction of Stock Portfolios Based on k-means Clustering of Continuous Trend Features, Knowledge-Based Systems, 2022, Vol. 252, pp. 109358.