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Feedback predictive control strategies for investment in the financial market with serially correlated returns subject to constraints and trading costs
Author(s) -
Dombrovskii Vladimir,
Obedko Tatiana
Publication year - 2017
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.2296
Subject(s) - portfolio , trading strategy , stochastic control , econometrics , currency , financial market , economics , leverage (statistics) , portfolio optimization , investment strategy , computer science , financial economics , finance , mathematical optimization , optimal control , mathematics , market liquidity , monetary economics , machine learning
Summary In this paper, we consider the optimal portfolio selection problem subject to hard constraints on trading amounts, trading costs, and different rates for borrowing and lending when the risky asset returns are serially correlated. We consider both explicit and implicit trading costs. No assumptions about the correlation structure between different time points or about the distribution of asset returns are needed. The problem is stated as a dynamic tracking problem of a reference portfolio with a desired return. We leverage the methodology of model predictive control (also known as receding horizon control) to design feedback portfolio optimization strategies and to provide a numerically tractable algorithm for practical applications. All expressions are presented in terms of first‐ and second‐order conditional moments. Our approach is tested on sets of real data from the Russian Stock Exchange Moscow Interbank Currency Exchange and New York Stock Exchange.