Enhance the performance of neural networks for stock market prediction: An analytical study

Author

Boonpeng, Sabaithip ; Jeatrakul, Piyasak

Author_Institution

Sch. of Inf. Technol., Mae Fah Luang Univ., Chiang Rai, Thailand

fYear

2014

fDate

Sept. 29 2014-Oct. 1 2014

Firstpage

1

Lastpage

6

Abstract

Stock market prediction is a challenging task in the machine learning research. The challenge is how to guide the investors when is the right time to buy or to sell. In the present day, there are numbers of machine learning techniques applied to predict the stock market such as Genetic Algorithm (GA), Support Vector Machines (SVM) and Artificial Neural Network (ANN). ANN is a major technique which is employed widely in this area. Therefore, in order to understand the trend of using ANN in the stock market prediction, the techniques to enhance the performance of ANN are reviewed. The period of the study is in the year between 2006 and 2013.

Keywords

learning (artificial intelligence); neural nets; stock markets; machine learning; neural networks; stock market prediction; Artificial neural networks; Cleaning; Genetic algorithms; Market research; Predictive models; Stock markets; Training; artificial neural network; data mining; financial prediction; prediction model;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Information Management (ICDIM), 2014 Ninth International Conference on

Conference_Location

Phitsanulok

Type

conf

DOI

10.1109/ICDIM.2014.6991352

Filename

6991352