Investigations on Dynamic Trading Strategy Utilizing Stochastic Optimal Control and Machine Learning

Abstract

Recently, control theory including stochastic optimal control and various machine-learning-based artificial intelligence methods have become major tools in the field of financial engineering. In this paper, we briefly review some recent papers utilizing stochastic optimal control theory in the fields of the pair trading for mean-reverting markets and the trend-following strategy, and consider a couple of strategies utilizing both stochastic optimal control theory and machine learning methods to acquire more flexible and accessible tools. Illustrative simulations show that the considered strategies can yield encouraging results when applied to a set of real financial market data.

Modern Probabilistic Machine Learning and Control Methods for Portfolio Optimization

“…Note that in the exponential NES approach, the performance index may be chosen with more flexibility. This paper extends our previous work on this topic [13] in two ways. First, we utilize a more advanced version of the NES-the exponential NES [23,25]-that is now the state-of-the-art method in the field.…”

“…2, the total number of position changes in the long-flat-short type trading strategy (2nd panel) is 345. Note that this value differs from the corresponding number of position changes in the long-flat type trading strategy (3rd panel, 100 changes) obtained by the NES approach [13]. Simulation results in Fig.…”

“…Figure 2 shows the NASDAQ index values together with the long-flat-short trading signals resulting from a policy obtained by the exponential NES method over the entire period. For comparison purposes, we also show the long-flat trading signals obtained by the NES approach [13]. According to Fig.…”

“…In particular, since portfolio optimization problems are essentially optimal decision-making problems that rely on actual data observed in a stochastic environment, theoretical and practical solutions can be formulated in light of recent advancements. These problems include the traditional mean-variance efficient portfolio problem [10], index tracking portfolio formulation [6][7][8]11], risk-adjusted expected return maximizing strategy [1,2,12], trend following strategy [13][14][15][16][17], long-short trading strategy (including the pairs trading strategy) [13,[18][19][20], and behavioral portfolio management.…”

Some Observations for Portfolio Management Applications of Modern Machine Learning Methods

“…We apply a simplified version of the sparse Gaussian process (GP) classification method, which is a direct result of two recent remarkable Gaussian process papers [25,26], for performing risk sensitivity classification in dealing with financial portfolio management. Since portfolio management problems are optimal decision-making problems that rely on actual empirical data, theoretical and practical solutions can be formulated via many of recent machine learning and control advancements: the traditional mean-variance efficient portfolio problem [11]; index tracking portfolio formulation [12][13][14][15]; risk-adjusted expected return maximizing strategy [16][17][18]; trend following strategy [19][20][21][22][23]; long-short trading strategy (including the pairs trading strategy) [20,24], etc. In this paper, we also raise two important portfolio management issues in which the GP application results can be useful.…”