A Survey on Machine Learning for Stock Price Prediction: Algorithms and Techniques

被引：41

作者：

Obthong, Mehtabhorn ^{[1
]}

Tantisantiwong, Nongnuch ^{[2
]}

Jeamwatthanachai, Watthanasak ^{[1
]}

Wills, Gary ^{[1
]}

机构：

[1] Univ Southampton, Sch Elect & Comp Sci, Southampton, Hants, England

[2] Nottingham Trent Univ, Nottingham Business Sch, Nottingham, England

来源：

FEMIB: PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON FINANCE, ECONOMICS, MANAGEMENT AND IT BUSINESS | 2020年

关键词：

Machine Learning; Deep Learning; Finance; Stock Price Prediction; Time Series Analysis; ARTIFICIAL NEURAL-NETWORKS; RECOGNITION;

D O I：

10.5220/0009340700630071

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

Stock market trading is an activity in which investors need fast and accurate information to make effective decisions. Since many stocks are traded on a stock exchange, numerous factors influence the decision-making process. Moreover, the behaviour of stock prices is uncertain and hard to predict. For these reasons, stock price prediction is an important process and a challenging one. This leads to the research of finding the most effective prediction model that generates the most accurate prediction with the lowest error percentage. This paper reviews studies on machine learning techniques and algorithm employed to improve the accuracy of stock price prediction.

引用

页码：63 / 71

页数：9

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