Investigating algorithmic stock market trading using ensemble machine learning methods

被引：0
作者：
Saifan R. [1 ]
Sharif K. [1 ]
Abu-Ghazaleh M. [1 ]
Abdel-Majeed M. [1 ]
机构：
[1] Computer Engineering Department, School of Engineering, University of Jordan, Queen Rania Street, Amman
来源：
Informatica (Slovenia) | 2020年 / 44卷 / 03期
关键词：
Algorithmic trading; Ensemble methods; Extremely randomized trees; Financial forecast; Forecasting returns; Gradient boosting; Machine learning; Random forest; Risk analysis; Stock market simulation; Stock price prediction; Volatility forecasting;
D O I：
10.31449/INF.V44I3.2904
中图分类号：
学科分类号：
摘要：
Recent advances in the machine learning field have given rise to efficient ensemble methods that accurately forecast time-series. In this paper, we use the Quantopian algorithmic stock market trading simulator to assess ensemble methods performance in daily prediction and trading. The ensemble methods used are Extremely Randomized Trees, Random Forest, and Gradient Boosting. All methods are trained using multiple technical indicators and automatic stock selection is used. Simulation results show significant returns relative to the benchmark and large values of alpha are produced from all methods. These results strengthen the role of ensemble method based machine learning in automated stock market trading. © 2020 Slovene Society Informatika. All rights reserved.
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页码：311 / 325
页数：14
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