Bank Nifty Index Trend Prediction using Historical Data, Wave Analysis, Market Sentiment, and Fibonacci Retracement for Dynamic Decision Making

##plugins.themes.academic_pro.article.sidebar##

Published Nov 26, 2021
https://doi.org/10.47164/ijngc.v12i5.426
Download
Requires Subscription PDF
Statistic

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...
Volume 12, Special Issue 5, November 2021

##plugins.themes.academic_pro.article.main##

MANISH BHARDWAJ
a:1:{s:5:"en_US";s:36:"PhD Scholar RCOEM Nagpur Maharashtra";}
Dattatraya Adane
Professor, Information Technology Department, Shri Ramdeobaba College of Engineering and Management, Nagpur (M.S.)

Abstract

Bank Nifty Index trend prediction is an important topic in finance, Banking Sector, and economics. Be a trader,
investor, or researcher interested in developing a better prediction model over the year. This paper presents the
process of building Bank Nifty Index Trend Prediction using Wave Analysis, Market Sentiment, and Fibonacci
Retracements for Dynamic Decision making. Published Stock data is obtained from The National Stock Exchange
of India Limited (NSE) are used. Result obtained revealed that this model has a strong potential for short term
BANK NIFTY INDEX Trend Prediction and compete the similar model by taking the advantage called Fibonacci
Retracement and Market Sentiment.

##plugins.themes.academic_pro.article.details##

Creative Commons License

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

Author Biography

Dattatraya Adane, Professor, Information Technology Department, Shri Ramdeobaba College of Engineering and Management, Nagpur (M.S.)

Second Author

How to Cite
BHARDWAJ, M., & Adane, D. (2021). Bank Nifty Index Trend Prediction using Historical Data, Wave Analysis, Market Sentiment, and Fibonacci Retracement for Dynamic Decision Making. International Journal of Next-Generation Computing, 12(5). https://doi.org/10.47164/ijngc.v12i5.426
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. Ariyo, A. A., Adewumi, A. O., and Ayo, C. K. 2014. Stock price prediction using the Arima model. In 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation. IEEE, 106–112. DOI: https://doi.org/10.1109/UKSim.2014.67
  2. Bathla, G. 2020. Stock price prediction using lstm and svr. In 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, 211–214. DOI: https://doi.org/10.1109/PDGC50313.2020.9315800
  3. Bhat, A. A. and Kamath, S. S. 2013. Automated stock price prediction and trading framework for nifty intraday trading. In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT). IEEE, 1–6. DOI: https://doi.org/10.1109/ICCCNT.2013.6726675
  4. Krishnamurthy, P., Balasubramanian, P., and Mohan, D. 2017. Study on relationship between exchange rate return and various stock indices returns. In 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI). IEEE, 316–320. DOI: https://doi.org/10.1109/ICDMAI.2017.8073533
  5. Kruthika, R., Balasubramanian, P., and Sureshkumar, V. 2018. Relationship between google trends data and index returns. In 2018 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC). IEEE, 042–045. DOI: https://doi.org/10.1109/ICCPEIC.2018.8525211
  6. Maurya, R. K., Rai, P., and Yadav, H. 2021. Decision making in intraday index options trading: A parametric approach. In 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 1067–1071. DOI: https://doi.org/10.1109/Confluence51648.2021.9377182
  7. Mehta, Y., Malhar, A., and Shankarmani, R. 2021. Stock price prediction using machine learning and sentiment analysis. In 2021 2nd International Conference for Emerging Technology (INCET). IEEE, 1–4. DOI: https://doi.org/10.1109/INCET51464.2021.9456376
  8. Qin, J., Tao, Z., Huang, S., and Gupta, G. 2021. Stock price forecast based on arima model and bp neural network model. In 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE). IEEE, 426–430. DOI: https://doi.org/10.1109/ICBAIE52039.2021.9389917
  9. Sayavong, L., Wu, Z., and Chalita, S. 2019. Research on stock price prediction method based on convolutional neural network. In 2019 International Conference on Virtual Reality and Intelligent Systems (ICVRIS). IEEE, 173–176. DOI: https://doi.org/10.1109/ICVRIS.2019.00050
  10. Seals, E. and Price, S. R. 2020. Preliminary investigation in the use of sentiment analysis in prediction of stock forecasting using machine learning. In 2020 SoutheastCon. Vol. 2. IEEE, 1–2. DOI: https://doi.org/10.1109/SoutheastCon44009.2020.9368258
  11. Wei, D. 2019. Prediction of stock price based on lstm neural network. In 2019 International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM). IEEE, 544–547. DOI: https://doi.org/10.1109/AIAM48774.2019.00113
  12. Xiong, L. and Lu, Y. 2017. Hybrid arima-bpnn model for time series prediction of the chinese stock market. In 2017 3rd International Conference on Information Management (ICIM). IEEE, 93–97. DOI: https://doi.org/10.1109/INFOMAN.2017.7950353