The Prediction of Stock Index Movements Based on Machine Learning

被引：1
|
作者：
Wang, Sanbo [1 ]
机构：
[1] Northeastern Univ, Boston, MA 02115 USA
来源：
PROCEEDINGS OF 2020 12TH INTERNATIONAL CONFERENCE ON COMPUTER AND AUTOMATION ENGINEERING (ICCAE 2020) | 2020年
关键词：
S&P 500 Index; Predication; Support Vector Machine; Random Forest; SUPPORT VECTOR MACHINES;
D O I：
10.1145/3384613.3384615
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
With the rapid development of Artificial Intelligence technology, different methods from other subjects have been used in predicting stock market movement and the stock prices. In this paper, 11 technical indicators were calculated to predict the stock index movements using two machine learning technics. S&P 500 is a stock market index that measures the stock performance in the United States, from Jan 2004 to Dec 2018. The results showed that Random Forest is superior to Support Vector Machine in both training and test sets. Several technical trend indicators we calculated here, such as WILLR, BBANDS, CCI, CMG and MACD, play a significant role in prediction of index movements based on Random Forest. This paper provides a fundamental framework in studying the prediction of stock movements by Artificial Intelligence.
引用
收藏
页码：1 / 6
页数：6
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