An Empirical Study of Machine Learning Algorithms for Stock Daily Trading Strategy
Joint Authors
Meizi, Li
Lv, Dongdong
Yuan, Shuhan
Xiang, Yang
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-30, 30 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-04-14
Country of Publication
Egypt
No. of Pages
30
Main Subjects
Abstract EN
According to the forecast of stock price trends, investors trade stocks.
In recent years, many researchers focus on adopting machine learning (ML) algorithms to predict stock price trends.
However, their studies were carried out on small stock datasets with limited features, short backtesting period, and no consideration of transaction cost.
And their experimental results lack statistical significance test.
In this paper, on large-scale stock datasets, we synthetically evaluate various ML algorithms and observe the daily trading performance of stocks under transaction cost and no transaction cost.
Particularly, we use two large datasets of 424 S&P 500 index component stocks (SPICS) and 185 CSI 300 index component stocks (CSICS) from 2010 to 2017 and compare six traditional ML algorithms and six advanced deep neural network (DNN) models on these two datasets, respectively.
The experimental results demonstrate that traditional ML algorithms have a better performance in most of the directional evaluation indicators.
Unexpectedly, the performance of some traditional ML algorithms is not much worse than that of the best DNN models without considering the transaction cost.
Moreover, the trading performance of all ML algorithms is sensitive to the changes of transaction cost.
Compared with the traditional ML algorithms, DNN models have better performance considering transaction cost.
Meanwhile, the impact of transparent transaction cost and implicit transaction cost on trading performance are different.
Our conclusions are significant to choose the best algorithm for stock trading in different markets.
American Psychological Association (APA)
Lv, Dongdong& Yuan, Shuhan& Meizi, Li& Xiang, Yang. 2019. An Empirical Study of Machine Learning Algorithms for Stock Daily Trading Strategy. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-30.
https://search.emarefa.net/detail/BIM-1197189
Modern Language Association (MLA)
Lv, Dongdong…[et al.]. An Empirical Study of Machine Learning Algorithms for Stock Daily Trading Strategy. Mathematical Problems in Engineering No. 2019 (2019), pp.1-30.
https://search.emarefa.net/detail/BIM-1197189
American Medical Association (AMA)
Lv, Dongdong& Yuan, Shuhan& Meizi, Li& Xiang, Yang. An Empirical Study of Machine Learning Algorithms for Stock Daily Trading Strategy. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-30.
https://search.emarefa.net/detail/BIM-1197189
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1197189