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Stock Direction Forecasting Techniques: An Empirical Study Combining Machine Learning System with Market Indicators in the Indian Context

by Deepika Chandwani, Manminder Singh Saluja
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 92 - Number 11
Year of Publication: 2014
Authors: Deepika Chandwani, Manminder Singh Saluja
10.5120/16051-5202

Deepika Chandwani, Manminder Singh Saluja . Stock Direction Forecasting Techniques: An Empirical Study Combining Machine Learning System with Market Indicators in the Indian Context. International Journal of Computer Applications. 92, 11 ( April 2014), 8-17. DOI=10.5120/16051-5202

@article{ 10.5120/16051-5202,
author = { Deepika Chandwani, Manminder Singh Saluja },
title = { Stock Direction Forecasting Techniques: An Empirical Study Combining Machine Learning System with Market Indicators in the Indian Context },
journal = { International Journal of Computer Applications },
issue_date = { April 2014 },
volume = { 92 },
number = { 11 },
month = { April },
year = { 2014 },
issn = { 0975-8887 },
pages = { 8-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume92/number11/16051-5202/ },
doi = { 10.5120/16051-5202 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:14:01.257897+05:30
%A Deepika Chandwani
%A Manminder Singh Saluja
%T Stock Direction Forecasting Techniques: An Empirical Study Combining Machine Learning System with Market Indicators in the Indian Context
%J International Journal of Computer Applications
%@ 0975-8887
%V 92
%N 11
%P 8-17
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Stock price movement prediction has been one of the most challenging issues in finance since the time immemorial. Many researchers in past have carried out extensive studies with the intention of investigating the approaches that uncover the hidden information in stock market data. As a result of which, Artificial Intelligence and data mining techniques have come to the forefront because of their ability to map non-linear data. The study encapsulates market indicators with AI techniques to generate useful extracts to improve decisions under conditions of uncertainty. Three approaches (fundamental model, technical indicators model and hybrid model) have been tested using the standalone and integrated machine learning algorithms viz. SVM, ANN, GA-SVM, and GA-ANN and the results of all the three approaches have been compared in the four above mentioned methods. The core objective of this paper is to identify an approach from the above mentioned algorithms that best predicts the Indian stocks price movement. It is observed from the results that the use of GA significantly increases the accuracy of ANN and that the use of technical analysis with SVM and ANN is well suited for Indian stocks and can help investors and traders maximize their quarterly profits.

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Index Terms

Computer Science
Information Sciences

Keywords

Support Vector Machine Artificial Neural Network Genetic Algorithm Financial Ratios Technical Indicators

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