股票市场交易的深度学习:一种优越的交易策略?

IF 0.7 4区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Network World Pub Date : 2019-01-01 DOI:10.14311/NNW.2019.29.011

D. Fister, Johnathan Mun, Vita Jagrič, Timotej Jagrič

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引用次数: 13

摘要

深度学习计划极大地改变了数据分析。任何研究领域的任何人都可以访问复杂的网络。在本文中，我们提出了一种深度学习长短期记忆网络(LSTM)用于自动股票交易。一个机械交易系统被用来评估其表现。将提出的解决方案与传统的交易策略(即被动和基于规则的交易策略)以及机器学习分类器进行比较。我们发现，在2010-2018年期间，深度学习长短期记忆网络在德国蓝筹股宝马(BMW)上的表现优于其他交易策略。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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DEEP LEARNING FOR STOCK MARKET TRADING: A SUPERIOR TRADING STRATEGY?

Deep-learning initiatives have vastly changed the analysis of data. Complex networks became accessible to anyone in any research area. In this paper we are proposing a deep-learning long short-term memory network (LSTM) for automated stock trading. A mechanical trading system is used to evaluate its performance. The proposed solution is compared to traditional trading strategies, i.e., passive and rule-based trading strategies, as well as machine learning classifiers. We have discovered that the deep-learning long short-term memory network has outperformed other trading strategies for the German blue-chip stock, BMW, during the 2010–2018 period.

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来源期刊

Neural Network World 工程技术-计算机：人工智能

CiteScore

1.80

自引率

0.00%

发文量

审稿时长

12 months

期刊介绍： Neural Network World is a bimonthly journal providing the latest developments in the field of informatics with attention mainly devoted to the problems of: brain science, theory and applications of neural networks (both artificial and natural), fuzzy-neural systems, methods and applications of evolutionary algorithms, methods of parallel and mass-parallel computing, problems of soft-computing, methods of artificial intelligence.