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A Hybrid Forecasting Model for Stock Value Prediction using Soft Computing skill

S. Barik1 , S. Das2 , SK. Sahoo3

Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-4 , Page no. 40-45, Apr-2017

Online published on Apr 30, 2017

Copyright © S. Barik, S. Das, SK. Sahoo . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: S. Barik, S. Das, SK. Sahoo, “A Hybrid Forecasting Model for Stock Value Prediction using Soft Computing skill,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.4, pp.40-45, 2017.

MLA Style Citation: S. Barik, S. Das, SK. Sahoo "A Hybrid Forecasting Model for Stock Value Prediction using Soft Computing skill." International Journal of Computer Sciences and Engineering 5.4 (2017): 40-45.

APA Style Citation: S. Barik, S. Das, SK. Sahoo, (2017). A Hybrid Forecasting Model for Stock Value Prediction using Soft Computing skill. International Journal of Computer Sciences and Engineering, 5(4), 40-45.

BibTex Style Citation:
@article{Barik_2017,
author = {S. Barik, S. Das, SK. Sahoo},
title = {A Hybrid Forecasting Model for Stock Value Prediction using Soft Computing skill},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {4 2017},
volume = {5},
Issue = {4},
month = {4},
year = {2017},
issn = {2347-2693},
pages = {40-45},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1238},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1238
TI - A Hybrid Forecasting Model for Stock Value Prediction using Soft Computing skill
T2 - International Journal of Computer Sciences and Engineering
AU - S. Barik, S. Das, SK. Sahoo
PY - 2017
DA - 2017/04/30
PB - IJCSE, Indore, INDIA
SP - 40-45
IS - 4
VL - 5
SN - 2347-2693
ER -

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Abstract

This paper aims to present a hybrid model to forecast stock price by analyzing different trends of stock market. As the stock price are time series but they are not static and highly noise due to the fact that stock market is not stable as it depends on various factors. In this paper we have propose a new approach to forecast stock price using ANFIS model optimized by particle swam optimization (PSO) this model is consisting of an effective algorithm for predicting next day high price of Yahoo stock value and Microsoft stock value. To present this algorithm we have taken real dataset of Yahoo Company and Microsoft Company. This new approach is compared with existing models with real data set and gives more accurate results which give more accuracy result with MAPE of 1%.

Key-Words / Index Term

data Mining, Prediction, Soft computing, Stock market

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