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

Authors

  • S Barik Dept. of Computer Science and Engineering -CUTM, BBSR, Odisha, India
  • S Das Dept. of Computer Science and Engineering -CUTM, BBSR, Odisha, India
  • SK Sahoo Dept. of Computer Science and Engineering -CUTM, BBSR, Odisha, India

Keywords:

data Mining, Prediction, Soft computing, Stock market

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%.

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Published

2025-11-11

How to Cite

[1]
S. Barik, S. Das, and S. Sahoo, “A Hybrid Forecasting Model for Stock Value Prediction using Soft Computing Skill”, Int. J. Comp. Sci. Eng., vol. 5, no. 4, pp. 40–45, Nov. 2025.

Issue

Vol. 5 No. 4 (2017): IJCSE April Edition

Section

Research Article

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