Skip to main content

Predict Stock Market Behavior: Role of Machine Learning Algorithms

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 673)

Abstract

The prediction of a dynamic, volatile, and unpredictable stock market has been a challenging issue for the researchers over the past few years. This paper discusses stock market-related technical indicators, mathematical models, most preferred algorithms used in data science industries, analysis of various types of machine learning algorithms, and an overall summary of solutions. This paper is an attempt to perform the analysis of various issues pertaining to dynamic stock market prediction, based on the fact that minimization of stock market investment risk is strongly correlated to minimization of forecasting errors.

Keywords

  • Machine learning algorithms
  • Stock market prediction
  • Efficient market hypothesis
  • Ensemble machine learning

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-981-10-7245-1_38
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   269.00
Price excludes VAT (USA)
  • ISBN: 978-981-10-7245-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   349.00
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3

References

  1. www.StocksNeural.net.

  2. www.vatsals.com.

  3. Robi Polikar, “Ensemble Learning, Methods and applications”, Date: 19 January 2012, Springer Verlag.

    Google Scholar 

  4. Machine learning algorithms, http://www-formal.stanford.edu/jmc/whatisai/node2.html.

  5. Robert Nau, http:blog.optiontradingpedia.com, besttradingplatformfordaytraders.blogspot.com.

    Google Scholar 

  6. Rupinder kaur, Ms. Vidhu Kiran, “Time Series based Accuracy Stock Market Forecasting using Artificial Neural Network”, IJARCCE. doi:https://doi.org/10.17148/IJARCCE, 2015.

  7. AI Stock Market Forum: http://www.ai-stockmarketforum.com/.

  8. How to Value Stocks using DCF and the Dangers of Doing, Do Fundamentals Really Drive The stock market? www.mckinsey.com, A Mckinsey report.

  9. Ben McClure, Discounted Cash Flow Analysis, Investopedia, Investopedia.com, 2010 http://www.investopedia.com/university/dcf/.

  10. https://www.udacity.com/course/machine-learning-for-trading-ud501.

  11. http://www.angoss.com, Key Performance Indicators Six Sigma and Data Mining.pdf, 2011.

  12. Forecasting home page (Introduction to ARIMA models), price Prediction Using the ARIMA Model-IEEE Xplore, UKSim-AMSS, 16th ICCMS, 2014.

    Google Scholar 

  13. www.kdnuggets.com.

  14. www.dezyre.com.

  15. Paliouras, Georgios, Karkaletsis, Vangelis, Spyropoulos, Machine Learning and Its Applications: advanced lectures, Springer-Verlag New York, Inc. New York, NY, USA 2001, table of contents ISBN-978-3-540-44673-6.

    Google Scholar 

  16. Alexandra L’Heureux; Katarina Grolinger, Hany F. El Yamany; Miriam Capretz, Machine Learning with Big Data: Challenges and Approaches, IEEE Access, 2017, doi:https://doi.org/10.1109/ACCESS.2017.2696365.

  17. J. Huang, J. Lu and C. X. Ling, “Comparing naive Bayes, decision trees, and SVM with AUC and accuracy,” Third IEEE International Conference on Data Mining, 2003, pp. 553–556. doi:https://doi.org/10.1109/ICDM.2003.1250975.

  18. Neelima Budhani, Dr. C.K. Jha, Sandeep K. Budhani, Application of Neural Network in Analysis of Stock Market Prediction, www.ijcset.com, IJCSET, 2012.

  19. Maryam M. Najafabadi, Flavio Villanustre, Taghi M. Khoshgoftaar, Naeem Seliya, Randall Wald and Edin Muharemagic, Deep learning applications and challenges in big data analytics, Journal of Big Data, SpringerOpen Journal, 2015.

    Google Scholar 

  20. L. K. Hansen and P. Salamon, Neural network ensembles, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 10, pp. 9931001, 1990.

    Google Scholar 

  21. W. N. Street and Y. Kim, A streaming ensemble algorithm (SEA) for large-scale classification, Seventh ACM SIGKDD International Conference on Knowledge Discovery Data Mining (KDD-01), pp. 377382, 2001.

    Google Scholar 

  22. blog.cloudera.com.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Uma Gurav .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Gurav, U., Sidnal, N. (2018). Predict Stock Market Behavior: Role of Machine Learning Algorithms. In: Bhalla, S., Bhateja, V., Chandavale, A., Hiwale, A., Satapathy, S. (eds) Intelligent Computing and Information and Communication. Advances in Intelligent Systems and Computing, vol 673. Springer, Singapore. https://doi.org/10.1007/978-981-10-7245-1_38

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-7245-1_38

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7244-4

  • Online ISBN: 978-981-10-7245-1

  • eBook Packages: EngineeringEngineering (R0)