Skip to main content
Springer Nature Link
Log in

Prediction of Stock Price Direction Combining Volatility Indicators with Machine Learning Algorithms

  • Conference paper
  • First Online:
Intelligent Systems Design and Applications (ISDA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 646))

  • 283 Accesses

Abstract

Predicting close price direction for the next period is one of the most difficult tasks, for researchers and traders. There are two commonly approaches to predict stock markets, fundamental analysis and technical analysis. In this work we aim to predict Moroccan stock market direction, using standard deviation and Chaikin volatility indicators, combining with Support Vector Machine (SVM) and Random Forest (RF). The results of our work show that the two models might help traders to determine the direction of close price, and hence increase their profit. These results, are evaluated with accuracy metric and Area Under Curve (AUC) of Receiver Operating Characteristic (ROC), showed that both of models give a good accuracy in the different stocks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Marc Chaikin is a stock analyst and Founder and CEO of Chaikin Analytics. He is also the founder of Bomar Securities LP, which was sold to Instinet Corp, in 1992.

References

  1. Khaidem, L., Saha, S., Dey, S.R.: Predicting the direction of stock market prices using random forest (2016)

    Google Scholar 

  2. Alonso-Monsalve, S., Suárez-Cetrulo, A.L., Cervantes, A., Quintana, D.: Convolution on neural networks for high-frequency trend prediction of cryptocurrency exchange rates using technical indicators. Expert Syst. Appl. 149, 113250 (2020)

    Article  Google Scholar 

  3. Murphy, J.J.: Technical analysis of the financial markets. New York Institute of Finance (1999)

    Google Scholar 

  4. Agrawal, M., Khan, A.U., Shukla, P.K.: Stock price prediction using technical indicators: a predictive model using optimal deep learning. Int. J. Recent Technol. Eng. (IJRTE), vol. 8(2) (2019). ISSN: 2277–3878

    Google Scholar 

  5. Sezer, O.B., Ozbayoglu, M., Dogdu, E.: A deep neural-network based stock trading system based on evolutionary optimized technical analysis parameters. In: Complex Adaptive Systems Conference with Theme: Engineering Cyber Physical Systems, CAS 30 October–1 November 1, 2017, Chicago, Illinois, USA (2017)

    Google Scholar 

  6. Brasileiro, R.C., Souza, V.L., Fernandes, B.J., Oliveira, A.L.: Automatic method for stock trading combining technical analysis and the artificial bee colony algorithm. In: IEEE Congress on Evolutionary Computation 20 –23 June, Cancun, Mexico (2013)

    Google Scholar 

  7. Ou, P., Wang, H.: Prediction of stock market index movement by ten data mining techniques. Mod. Appl. Sci. 3, P28 (2009)

    Article  MATH  Google Scholar 

  8. Chang, P.C., Liao, T.W., Lin, J.J., Fan, C.Y.: A dynamic threshold decision system for stock trading signal detection. Appl. Soft Comput. 11, 3998–4010 (2011)

    Article  Google Scholar 

  9. Imandoust, S., Bolandraftar, M.: Forecasting the direction of stock market index movement using three data mining techniques: the case of Tehran stock exchange. J. Eng. Res. Appl. 4(6), 106–117 (2014). ISSN: 2248–9622, (Version 2)

    Google Scholar 

  10. Shen, S., Jiang, H., Zhang, T.: Stock market forecasting using machine learning algorithms (2012)

    Google Scholar 

  11. Patel, J., Shah, S., Thakkar, P., Kotecha, K.: Predicting stock market index using fusion of machine learning techniques (2014)

    Google Scholar 

  12. Zhang, Y., Wu, L.: Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network. Expert Syst. Appl. 36, 8849–8854 (2009). https://doi.org/10.1016/j.eswa.2008.11.028

    Article  Google Scholar 

  13. Pang, X., Zhou, Y., Wang, P., Lin, W., Chang, V.: An innovative neural network approach for stock market prediction. J. Supercomput. 76(3), 2098–2118 (2018). https://doi.org/10.1007/s11227-017-2228-y

    Article  Google Scholar 

  14. Hasan, S.S., Rahman, R., Mannan, N., Khan, H., Moni, J.N., Rahman, R.M.: Improved Stock Price Prediction by Integrating Data Mining Algorithms and Technical Indicators: A Case Study on Dhaka Stock Exchange. In: Nguyen, N.T., Papadopoulos, G.A., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds.) ICCCI 2017. LNCS (LNAI), vol. 10448, pp. 288–297. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67074-4_28

    Chapter  Google Scholar 

  15. Sethia, Akhil, Raut, Purva: Application of LSTM, GRU and ICA for stock price prediction. In: Satapathy, S. C., Joshi, A. (eds.) Information and Communication Technology for Intelligent Systems. SIST, vol. 107, pp. 479–487. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-1747-7_46

    Chapter  Google Scholar 

  16. Breiman, l., Friedman, J.H., Olshen, R.A. Stone, C.J.: Classification and Regression Trees (Monterey, CA: Wadsworth) (1984)

    Google Scholar 

  17. Wang, D., Zhao, Y.: using news to predict investor sentiment: based on SVM model. In: International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI2019)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Finance, Audit and Organizational, Governance Research Laboratory, National School of Commerce and Management Hassan First University of Settat, Settat, Morocco

    Azdine Bilal, Abdelhadi Ifleh & Mounime El Kabbouri

Authors
  1. Azdine Bilal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdelhadi Ifleh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mounime El Kabbouri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Azdine Bilal .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, FLAME University, Pune, Maharashtra, India

    Ajith Abraham

  2. Center for Smart Computing Continuum, Burgenland, Austria

    Sabri Pllana

  3. University of Bari, Bari, Italy

    Gabriella Casalino

  4. University of Jinan, Jinan, Shandong, China

    Kun Ma

  5. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Anu Bajaj

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bilal, A., Ifleh, A., El Kabbouri, M. (2023). Prediction of Stock Price Direction Combining Volatility Indicators with Machine Learning Algorithms. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 646. Springer, Cham. https://doi.org/10.1007/978-3-031-27440-4_31

Download citation

Publish with us

Policies and ethics