Predicting Stock Market Price Using Neural Network Model

The present article predicts the movement of daily Indian stock market (S&P CNX Nifty) price by using Feedforward Neural Network Model over a period of eight years from January 1st 2008 to April 8th 2016. The prediction accuracy of the model is accessed by normalized mean square error (NMSE) and...

Full description

Saved in:
Bibliographic Details
Published in:International journal of strategic decision sciences 2018-07, Vol.9 (3), p.84-94
Main Author: Tripathy, Naliniprava
Format: Article
Language:English
Subjects:
Artificial neural networks
Efficient markets
Error correction
Forecasts and trends
Market prices
Model accuracy
Neural networks
Securities industry
Securities markets
Stock markets
Stocks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The present article predicts the movement of daily Indian stock market (S&P CNX Nifty) price by using Feedforward Neural Network Model over a period of eight years from January 1st 2008 to April 8th 2016. The prediction accuracy of the model is accessed by normalized mean square error (NMSE) and sign correctness percentage (SCP) measure. The study indicates that the predicted output is very close to actual data since the normalized error of one-day lag is 0.02. The analysis further shows that 60 percent accuracy found in the prediction of the direction of daily movement of Indian stock market price after the financial crises period 2008. The study indicates that the predictive power of the feedforward neural network models reasonably influenced by one-day lag stock market price. Hence, the validity of an efficient market hypothesis does not hold in practice in the Indian stock market. This article is quite useful to the investors, professional traders and regulators for understanding the effectiveness of Indian stock market to take appropriate investment decision in the stock market.
ISSN:1947-8569
1947-8577
DOI:10.4018/IJSDS.2018070104