Deep Learning for Stock Market Trading: A Superior Trading Strategy?
Abstract
Deep-learning initiatives have vastly changed the analysis of data. Complex networks became accessible to anyone in any research area. In this pa per 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 classi fiers. 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.
Description
The article of record as published may be found at https://doi.org/10.14311/NNW.2019.29.011
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.Collections
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