Next Article in Journal
The Impact of Tax Preferences on the Investment Attractiveness of Bonds for Retail Investors: The Case of Russia
Next Article in Special Issue
Impact Analysis of Financial Regulation on Multi-Asset Markets Using Artificial Market Simulations
Previous Article in Journal
Dynamic Spillovers between Gulf Cooperation Council’s Stocks, VIX, Oil and Gold Volatility Indices
Previous Article in Special Issue
The Predictability of the Exchange Rate When Combining Machine Learning and Fundamental Models
Open AccessArticle

Deep Reinforcement Learning in Agent Based Financial Market Simulation

1
Department of Systems Innovation, School of Engineering, The University of Tokyo, Tokyo 113-8654, Japan
2
Daiwa Securities Co. Ltd., Tokyo 100-0005, Japan
3
Daiwa Institute of Research Ltd., Tokyo 135-8460, Japan
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2020, 13(4), 71; https://doi.org/10.3390/jrfm13040071
Received: 28 February 2020 / Revised: 7 April 2020 / Accepted: 8 April 2020 / Published: 11 April 2020
(This article belongs to the Special Issue AI and Financial Markets)
Prediction of financial market data with deep learning models has achieved some level of recent success. However, historical financial data suffer from an unknowable state space, limited observations, and the inability to model the impact of your own actions on the market can often be prohibitive when trying to find investment strategies using deep reinforcement learning. One way to overcome these limitations is to augment real market data with agent based artificial market simulation. Artificial market simulations designed to reproduce realistic market features may be used to create unobserved market states, to model the impact of your own investment actions on the market itself, and train models with as much data as necessary. In this study we propose a framework for training deep reinforcement learning models in agent based artificial price-order-book simulations that yield non-trivial policies under diverse conditions with market impact. Our simulations confirm that the proposed deep reinforcement learning model with unique task-specific reward function was able to learn a robust investment strategy with an attractive risk-return profile. View Full-Text
Keywords: deep reinforcement learning; financial market simulation; agent based simulation deep reinforcement learning; financial market simulation; agent based simulation
Show Figures

Figure 1

MDPI and ACS Style

Maeda, I.; deGraw, D.; Kitano, M.; Matsushima, H.; Sakaji, H.; Izumi, K.; Kato, A. Deep Reinforcement Learning in Agent Based Financial Market Simulation. J. Risk Financial Manag. 2020, 13, 71.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop