A Dynamic Business Analysis System for Indian Stock Market using Artificial Neural Network

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-35 Number-3
Year of Publication : 2016
Authors : Rutuj Rashinkar, Sayali Shende, Akshay Rasane
  10.14445/22312803/IJCTT-V35P128

MLA

Rutuj Rashinkar, Sayali Shende, Akshay Rasane "A Dynamic Business Analysis System for Indian Stock Market using Artificial Neural Network". International Journal of Computer Trends and Technology (IJCTT) V35(3):150-153, May 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Stock price prediction has always been an area of interest for researchers due to the dynamic and chaotic nature of stock market. Various techniques have been applied for stock price forecasting. None of the techniques give an accurate prediction due to the high uncertainty of the stock market. Hence the proposed system will be using four different algorithms for prediction with more accuracy and certainty. The use of Artificial Neural Networks is made and Machine learning is applied.

References
[1] “Using Neural Networks to Forecast Stock Market Prices”. Ramon Lawrence Department of Computer Science University of Manitoba.
[2] “A Stock Market Prediction Model using Artificial Neural Network”. Kumar Abhishek, Anshul Khairwa, Tej Pratap, Surya Prakash
[3] “Stock Market Prediction Using Artificial Neural Networks”. Bhagwant Chauhan, Umesh Bidave, Ajit Gangathade, Sachin Kale, Department of Computer Engineering Universal College of Engineering and Research, University of Pune
[4] “Introduction to Time Series Forecasting”. Titus Awokuse and Tom Ilvento, University of Delaware, College of Agriculture and Natural Resources, Food and Resource Economics.
[5] “Price Prediction of Share Market using Artificial Neural Network”. Zabir Haider Khan, Tasnim Sharmin Alin, Md. Akter Hussain, Dept of CSE, SUST, Sylhet, Bangladesh.

Keywords
Artificial Neural Networks, Time Series, ARIMA, Backpropagation, Feed Forward Networks.