A Survey : Analysis and Estimation of Share Market Scenario

Authors

  • Devansh Dhote   Student, Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegoan, Savitribai Phule Pune University, Pune, Maharashtra, India
  • Piyush Rai   Student, Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegoan, Savitribai Phule Pune University, Pune, Maharashtra, India
  • Sunil Deshmukh  Student, Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegoan, Savitribai Phule Pune University, Pune, Maharashtra, India
  • Adarsh Jaiswal  Student, Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegoan, Savitribai Phule Pune University, Pune, Maharashtra, India
  • Prof. Yogesh Mali  Professor, Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegoan, Savitribai Phule Pune University, Pune, Maharashtra, India

Keywords:

Share Market, Analysis, Machine Learning, Supervised Learning Algorithm, Linear Regression, Data Analysis.

Abstract

Share market is a public market for the transaction of business share. It is an organized set-up with a monitoring body and the members who trade in shares are registered with the share market and regulatory body Security Exchange of Board India. Since share market data are highly time-variant and are normally in a nonlinear pattern, analyzing the future price of a share is highly challenging. Analysis provides sophisticated information regarding the current status of the share price movement. Thus this can be developed in decision making for customers in finalizing whether to buy or sell the particular shares of a given share. Many investigators have been carried out for analyzing share market price using various data mining techniques. This work aims at using of Artificial Neural Network techniques to predict the share price of companies listed under index of National Share Exchange (NSE). The past data of the selected share will be used for building and training the models. The results from the model will be used for comparison with the real data to determine the accuracy of the model.

References

  1. Dr. P. K. Sahoo, Mr. Krishna charlapally ,“Share Price Analysis Using Regression Analysis”, International Journal of Scientific & Engineering Research, Volume 6, Issue 3, March-2015 ISSN 2229-5518.
  2. Farhad Soleimanian Gharehchopogh, Tahmineh Haddadi Bonab and Seyyed Reza Khaze ,“A Linear Regression approach to analysis of share market trading volume: A case study”, International Journal of Managing Value and Supply Chains (IJMVSC) Vol.4, No. 3, September 2013.
  3. Sachin Kamley , Shailesh Jaloree & R. S. Thakur , “Multiple Regression: A Data Mining Approach fir analyzing the share market” trends based on open, close and high price of the month”, International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR) ISSN 2249-6831 Vol. 3, Issue 4, Oct 2013, 173-180 © TJPRC Pvt. Ltd.
  4. Hiransha Ma, Gopalakrishnan E. Ab, Vijay Krishna Menonab, Soman K.P*,” NSE Share Market Analysis Using Deep-Learning Models” International Conference on Computational Intelligence and Data Science (ICCIDS 2018).
  5. Neha Bhardwaj1, MD Akil Ansari1” Analysis of Share Market using Machine Learning Algorithms” International Research Journal of Engineering and Technology (IRJET) Volume: 06 Issue: 05 | May 2019.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2019-10-30

Issue

Volume 4, Issue 8, September-October-2019

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Devansh Dhote , Piyush Rai , Sunil Deshmukh, Adarsh Jaiswal, Prof. Yogesh Mali, " A Survey : Analysis and Estimation of Share Market Scenario , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 8, pp.77-80, September-October-2019.