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Reinforcement Learning in Financial Markets

强化学习 盈利能力指数 外汇市场 证券交易所 交易成本 人工智能 交易数据 计算机科学 市场流动性 学习分类器系统 数据库事务 交易策略 机器学习 金融经济学 业务 财务 经济 汇率 数据库
作者
Terry Lingze Meng,Matloob Khushi
出处
期刊:Data [Multidisciplinary Digital Publishing Institute]
卷期号:4 (3): 110-110 被引量:76
标识
DOI:10.3390/data4030110
摘要

Recently there has been an exponential increase in the use of artificial intelligence for trading in financial markets such as stock and forex. Reinforcement learning has become of particular interest to financial traders ever since the program AlphaGo defeated the strongest human contemporary Go board game player Lee Sedol in 2016. We systematically reviewed all recent stock/forex prediction or trading articles that used reinforcement learning as their primary machine learning method. All reviewed articles had some unrealistic assumptions such as no transaction costs, no liquidity issues and no bid or ask spread issues. Transaction costs had significant impacts on the profitability of the reinforcement learning algorithms compared with the baseline algorithms tested. Despite showing statistically significant profitability when reinforcement learning was used in comparison with baseline models in many studies, some showed no meaningful level of profitability, in particular with large changes in the price pattern between the system training and testing data. Furthermore, few performance comparisons between reinforcement learning and other sophisticated machine/deep learning models were provided. The impact of transaction costs, including the bid/ask spread on profitability has also been assessed. In conclusion, reinforcement learning in stock/forex trading is still in its early development and further research is needed to make it a reliable method in this domain.
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