International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Modern Probabilistic Machine Learning and Control Methods for Portfolio Optimization

  • Park, Jooyoung (Department of Control & Instrumentation Engineering, Korea University) ;
  • Lim, Jungdong (Department of Control & Instrumentation Engineering, Korea University) ;
  • Lee, Wonbu (Department of Control & Instrumentation Engineering, Korea University) ;
  • Ji, Seunghyun (Department of Control & Instrumentation Engineering, Korea University) ;
  • Sung, Keehoon (Department of Control & Instrumentation Engineering, Korea University) ;
  • Park, Kyungwook (School of Business Administration, Korea University)
  • Received : 2014.05.21
  • Accepted : 2014.06.24
  • Published : 2014.06.25
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Abstract

Many recent theoretical developments in the field of machine learning and control have rapidly expanded its relevance to a wide variety of applications. In particular, a variety of portfolio optimization problems have recently been considered as a promising application domain for machine learning and control methods. In highly uncertain and stochastic environments, portfolio optimization can be formulated as optimal decision-making problems, and for these types of problems, approaches based on probabilistic machine learning and control methods are particularly pertinent. In this paper, we consider probabilistic machine learning and control based solutions to a couple of portfolio optimization problems. Simulation results show that these solutions work well when applied to real financial market data.

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References

  1. S. Boyd, M. T. Mueller, B. O'Donoghue, and Y. Wang, "Performance bounds and suboptimal policies for multiperiod investment," Foundations and Trends in Optimization, vol. 1 no. 1, pp. 1-72, 2014. http://dx.doi.org/10.1561/2400000001
  2. J. A. Primbs, "Portfolio optimization applications of stochastic receding horizon control," in Proceeding of the 2007 American Control Conference, New York, NY, July 9-13, 2007, pp. 1811-1816. http://dx.doi.org/10.1109/ACC.2007.4282251
  3. G. C. Calafiore, "Multi-period portfolio optimization with linear control policies," Automatica, vol. 44, no. 10, pp. 2463-2473, Oct. 2008. http://dx.doi.org/10.1016/j.automatica.2008.02.007
  4. S. Alenmyr and A. 'Ogren, "Model Predictive Control for Stock Portfolio Selection," M.S. Thesis, Department of Automatic Control, Lund University, Sweden, 2010.
  5. B. R. Barmish, "On performance limits of feedback controlbased stock trading strategies," in Proceedings of 2011 American Control Conference, San Francisco, CA, June 29-July 1, 2011, pp. 3874-3879.
  6. J. A. Primbs, and C. Sung, "A stochastic receding horizon control approach to constrained index tracking," Asia-Pacific Financial Markets, vol. 15, no. 1, pp. 3-24, Mar. 2008. http://dx.doi.org/10.1007/s10690-008-9073-1
  7. J. E. Beasley, N. Meade, and T. J. Chang, "An evolutionary heuristic for the index tracking problem," European Journal of Operational Research, vol. 148, no. 3, pp. 621-643, Aug. 2003. http://dx.doi.org/10.1016/S0377-2217(02)00425-3
  8. R. Jeurissen and J. van den Berg, "Index tracking using a hybrid genetic algorithm," in Proceedings of the ICSC Congress on Computational Intelligence Methods and Applications, , Istanbul, Turkey, 2005. http://dx.doi.org/10.1109/CIMA.2005.1662364
  9. J. A. Primbs, "Dynamic hedging of basket options under proportional transaction costs using receding horizon control," International Journal of Control, vol. 82, no. 10, pp. 1841-1855, Oct. 2009. http://dx.doi.org/10.1080/00207170902783341
  10. H. Markowitz, Portfolio Selection: Efficient Diversification of Investments (Cowles Foundation for Research in Economics at Yale University Monograph 16), New York, NY: Wiley, 1959.
  11. J. Park, D. Yang, and K. Park, "Approximate dynamic programming-based dynamic portfolio optimization for constrained index tracking,"International Journal of Fuzzy Logic and Intelligent Systems, vol. 13, no. 1, pp. 19-28, Mar. 2013. http://dx.doi.org/10.5391/IJFIS.2013.13.1.19
  12. J. Park, J. Jeong, and K. Park, "An investigation on dynamic portfolio selection problems utilizing stochastic receding horizon approach," Journal of Korean Institute of Intelligent Systems, vol. 22, no. 3, pp. 386-393, Jun. 2012. http://dx.doi.org/10.5391/JKIIS.2012.22.3.386
  13. J. Park, D. Yang, and K. Park, "Investigations on dynamic trading strategy utilizing stochastic optimal control and machine learning," Journal of Korean Institute of Intelligent Systems, vol. 23, no. 4, pp. 348-353, Aug. 2013. http://dx.doi.org/10.5391/JKIIS.2013.23.4.348
  14. M. Dai, Q. Zhang, and Q. J. Zhu, "Trend following trading under a regime switching model," SIAM Journal on Financial Mathematics, vol. 1, no. 1, pp. 780-810, 2010. http://dx.doi.org/10.1137/090770552
  15. M. Dai, Q. Zhang, and Q. J. Zhu, "Optimal trend following trading rules," Social Science Research Network Paper, July 19, 2011. http://dx.doi.org/10.2139/ssrn.1762118
  16. H. T. Kong, Q. Zhang, and G. G. Yin, "A trend-following strategy: conditions for optimality," Automatica, vol. 47, no. 4, pp. 661-667, Apr. 2011. http://dx.doi.org/10.1016/j.automatica.2011.01.039
  17. J. Yu and Q. Zhang, "Optimal trend-following trading rules under a three-state regime switching model," Mathematical Control and Related Fields, vol. 2, no. 1, pp. 81-100, 2012. http://dx.doi.org/10.3934/mcrf.2012.2.81
  18. S. J. Kim, J. Primbs, and S. Boyd, "Dynamic spread trading," 2008 manuscript submitted.
  19. J. A. Primbs, "A control systems based look at financial engineering," 2009 manuscript submitted.
  20. S. Mudchanatongsuk, J. A. Primbs, and W. Wong, "Optimal pairs trading: a stochastic control approach," in Proceedings of the American Control Conference, Seattle, WA, June 11-13, 2008, pp. 1035-1039. http://dx.doi.org/10.1109/ACC.2008.4586628
  21. D. Wierstra, T. Schaul, J. Peters, and J. Schmidhuber, "Natural evolution strategies," in Proceedings of the IEEE World Congress on Evolutionary Computation, Hong Kong, June 1-6, 2008, pp. 3381-3387. http://dx.doi.org/10.1109/CEC.2008.4631255
  22. D. Wierstra, T. Schaul, T. Glasmachers, Y. Sun, and J. Schmidhuber, "Natural evolution strategies," June 22, 2011. http://arxiv.org/abs/1106.4487
  23. T. Glasmachers, T. Schaul, S. Yi, D. Wierstra, and J. Schmidhuber, "Exponential natural evolution strategies," in Proceedings of the 12th Genetic and Evolutionary Computation Conference, Portland, OR, July 7-11, 2010.
  24. T. Schaul, "Benchmarking exponential natural evolution strategies on the noiseless and noisy black-box optimization testbeds," in Proceedings of the 14th Genetic and Evolutionary Computation Conference, Philadelphia, PA, July 7-11, 2012. http://dx.doi.org/10.1145/2330784.2330816
  25. Y. Wang, B. O'Donoghue, and S. Boyd, "Approximate dynamic programming via iterated Bellman inequalities," International Journal of Robust and Nonlinear Control, February 19, 2014, in press. http://dx.doi.org/10.1002/rnc.315
  26. B. O'Donoghue, W. Yang, and S. Boyd, "Min-max approximate dynamic programming," in Proceedings of the IEEE International Symposium on Computer-Aided Control System Design, Denver, CO, September 28-30, 2011, pp. 424-431. http://dx.doi.org/10.1109/CACSD.2011.6044538
  27. B. O'Donoghue, Y. Wang, and S. Boyd, "Iterated approximate value functions," in Proceedings European Control Conference, Zurich, Switzerland, July 17-19, 2013, pp. 3882-3888.
  28. A. Keshavarz and S. Boyd, "Quadratic approximate dynamic programming for input-affine systems," International Journal of Robust and Nonlinear Control, vol. 24, no. 3, pp. 432-449, Feb. 2014. http://dx.doi.org/10.1002/rnc.2894
  29. J. Peters and S. Schaal, "Natural actor-critic," Neurocomputing, vol. 71, no. 7-9, pp. 1180-1190, Mar. 2008. http://dx.doi.org/10.1016/j.neucom.2007.11.026
  30. D. P. Bertsekas, Dynamic Programming and Optimal Control, Belmont, MA: Athena Scientific, 1995.
  31. W. B. Powell, Approximate Dynamic Programming : Solving the Curses of Dimensionality, Hoboken, NJ: Wiley-Interscience, 2007.
  32. W. M. Wonham, "Some applications of stochastic differential equations to optimal non-linear filtering," SIAM Journal on Control, vol. 2, pp. 347-369, 1965.
  33. S. P. Boyd, Linear Matrix Inequalities in System and Control Theory, Philadelphia, PA: Society for Industrial and Applied Mathematics, 1994

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