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

共 50 条

[1] Forecasting with Deep Learning: S&P 500 index
Kamalov, Firuz
Smail, Linda
Gurrib, Ikhlaas
[J]. 2020 13TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID 2020), 2020, : 422 - 425
[2] S&P 500 inclusions and stock supply
Schnitzler, Jan
[J]. JOURNAL OF EMPIRICAL FINANCE, 2018, 48 : 341 - 356
[3] S&P 500 stock selection using machine learning classifiers: A look into the changing role of factors
Caparrini, Antonio
Arroyo, Javier
Mansilla, Jordi Escayola
[J]. RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE, 2024, 70
[4] S&P 500 Affiliation and Stock Price Informativeness
Liu, Shinhua
[J]. JOURNAL OF BEHAVIORAL FINANCE, 2020, 21 (03) : 219 - 232
[5] On Survivor Stocks in the S&P 500 Stock Index
Grobys, Klaus
[J]. JOURNAL OF RISK AND FINANCIAL MANAGEMENT, 2022, 15 (02)
[6] Changes of Network Structure of the Stock Indices of S&P 500
Chakraborty, Sree Narayan
Alam, Shafiqul
Milu, Sharmin Akter
Nobi, Ashadun
[J]. THIRD INTERNATIONAL CONFERENCE ON MATERIAL SCIENCE, SMART STRUCTURES AND APPLICATIONS (ICMSS 2020), 2021, 2327
[7] Harnischfeger's comeback plan - The worst stock on the S&P 500
Siegel, M
[J]. FORTUNE, 1999, 139 (01) : 196 - +
[8] Forecasting Crude Oil Prices with Major S&P 500 Stock Prices: Deep Learning, Gaussian Process, and Vine Copula
Kim, Jong-Min
Han, Hope H.
Kim, Sangjin
[J]. AXIOMS, 2022, 11 (08)
[9] Predicting Stock Movement Direction with Machine Learning: an Extensive Study on S&P 500 Stocks
Jiao, Yang
Jakubowicz, Jeremie
[J]. 2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2017, : 4705 - 4713
[10] A comparison of neural network model selection strategies for the pricing of S&P 500 stock index options
Thomaidis, N. S.
Tzastoudis, V. S.
Dounias, G. D.
[J]. INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS, 2007, 16 (06) : 1093 - 1113

← 1 2 3 4 5 →