Research Area:  Machine Learning

Abstract:

Intelligent agents are often used in professional portfolio management. The use of intelligent agents in personal retirement portfolio management is not investigated in the past. In this research, we consider a two-asset personal retirement portfolio and propose several reinforcement learning agents for trading portfolio assets. In particular, we design an on-policy SARSA (λ) and an off-policy Q(λ) discrete state and discrete action agents that maximize either portfolio returns or differential Sharpe ratios. Additionally, we design a temporal-difference learning, TD(λ), agent that uses a linear valuation function in discrete state and continuous action settings. Using two different two-asset portfolios, the first asset being the S&P 500 Index and the second asset being either a broad bond market index or a 10-year U.S. Treasury note (T-note), we test the performance of different agents on different holdout (test) samples. The results of our experiments indicate that the high-learning frequency (i.e., adaptive learning) TD(λ) agent consistently beats both the single asset stock and bond cumulative returns by a significant margin.

Keywords:  
Financial Indices
Reinforcement Learning
Machine Learning
Deep Learning

Author(s) Name:  Parag C.Pendharkar and Patrick Cusatis

Publisher name:  ELSEVIER

DOI:  10.1016/j.eswa.2018.02.032

Volume Information:  Volume 103, 1 August 2018, Pages 1-13

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0957417418301209