Trading financial indices with reinforcement learning agents
| Autor: | Patrick J. Cusatis, Parag C. Pendharkar |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
050208 finance
Computer science Sharpe ratio Bond 05 social sciences General Engineering 02 engineering and technology computer.software_genre Bond market index Computer Science Applications Intelligent agent Artificial Intelligence 0502 economics and business 0202 electrical engineering electronic engineering information engineering Econometrics Portfolio Reinforcement learning 020201 artificial intelligence & image processing Adaptive learning Project portfolio management computer |
| Zdroj: | Expert Systems with Applications. 103:1-13 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2018.02.032 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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