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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

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.

Acknowledgement

Supported by : National Research Foundation of Korea (NRF)

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