Deep Reinforcement Learning Agent for S&P 500 Stock Selection

被引：9

作者：

Huotari, Tommi ^{[1
]}

Savolainen, Jyrki ^{[1
]}

Collan, Mikael ^{[1
]}

机构：

[1] LUT Univ, Sch Business & Management, Lappeenranta 53850, Finland

来源：

AXIOMS | 2020年 / 9卷 / 04期

基金：

芬兰科学院;

关键词：

deep reinforcement learning; portfolio selection; convolutional neural network; feature selection; trading agent; NEURAL-NETWORKS; MARKET VALUE; PREDICTION; RETURN; PERFORMANCE;

D O I：

10.3390/axioms9040130

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This study investigated the performance of a trading agent based on a convolutional neural network model in portfolio management. The results showed that with real-world data the agent could produce relevant trading results, while the agent's behavior corresponded to that of a high-risk taker. The data used were wide in comparison with earlier reported research and was based on the full set of the S&P 500 stock data for twenty-one years supplemented with selected financial ratios. The results presented are new in terms of the size of the data set used and with regards to the model used. The results provide direction and offer insight into how deep learning methods may be used in constructing automatic trading systems.

引用

页码：1 / 15

页数：15

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