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A Novel Model for Stock Price Prediction Using Hybrid Neural Network

  • Manas Ranjan SenapatiEmail author
  • Sumanjit Das
  • Sarojananda Mishra
Original Contribution
  • 270 Downloads

Abstract

The foremost challenge for investors is to select stock price by analyzing financial data which is a menial task as of distort associated and massive pattern. Thereby, selecting stock poses one of the greatest difficulties for investors. Nowadays, prediction of financial market like stock market, exchange rate and share value are very challenging field of research. The prediction and scrutinization of stock price is also a potential area of research due to its vital significance in decision making by financial investors. This paper presents an intelligent and an optimal model for prophecy of stock market price using hybridization of Adaline Neural Network (ANN) and modified Particle Swarm Optimization (PSO). The connoted model hybrid of Adaline and PSO uses fluctuations of stock market as a factor and employs PSO to optimize and update weights of Adaline representation to depict open price of Bombay stock exchange. The prediction performance of the proposed model is compared with different representations like interval measurements, CMS-PSO and Bayesian-ANN. The result indicates that proposed scheme has an edge over all the juxtaposed schemes in terms of mean absolute percentage error.

Keywords

Prediction Data mining Stock market Soft computing Neural network Artificial Intelligence 

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Copyright information

© The Institution of Engineers (India) 2018

Authors and Affiliations

  • Manas Ranjan Senapati
    • 1
    Email author
  • Sumanjit Das
    • 2
  • Sarojananda Mishra
    • 3
  1. 1.Department of Information TechnologyVeer Surendra Sai University of TechnologySambalpurIndia
  2. 2.Department of Computer Science and EngineeringCenturion University of TechnologyKhordhaIndia
  3. 3.Department of Computer Science & EngineeringIndira Gandhi Institute of TechnologyDhenkanalIndia

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