Skip to main content
SpringerLink
Log in

Predicting the Unpredictable: An Application of Machine Learning Algorithms in Indian Stock Market

  • Published:
Annals of Data Science Aims and scope Submit manuscript

Abstract

The stock market is a popular investment option for investors because of its expected high returns. Stock market prediction is a complex task to achieve with the help of artificial intelligence. Because stock prices depend on many factors, including trends and news in the market. However, in recent years, many creative techniques and models have been proposed and applied to efficiently and accurately forecast the behaviour of the stock market. This paper presents a comparative study of fundamental and technical analysis based on different parameters. We also discuss a comparative Analysis of various prediction techniques used to predict stock price. These strategies include technical analysis like time series analysis and machine learning algorithms such as the artificial neural network (ANN). Along with them, few researchers focused on the textual analysis of stock prices by continuous analysing the public sentiments from social media and other news sources. Various approaches are compared based on methodologies, datasets, and efficiency with the help of visualisation.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Liu B (2012) Sentiment analysis and opinion mining. Morgan and Claypool Publishers, San Rafael

    Book  Google Scholar 

  2. Attigeri GV, Manohara PMM, Pai RM, Nayak A (2015) Stock market prediction: a big data approach. In: Proceedings of the IEEE region 10th conference on TENCON 2015, Macao, China, pp 1–5

  3. Chen R, Lazer M (2013) Sentiment analysis of twitter feeds for the prediction of stock market movement, Stanford. edu. Retrieved January. 2013

  4. Zhang L (2013) Sentiment analysis on twitter with stock price and significant keyword correlation. The University of Texas at Austin, Austin

    Google Scholar 

  5. Adebiyi Ayodele A, Charles AK, Adebiyi MO, Otokiti SO (2012) Stock price prediction using neural network with hybridized market indicators. J Emerg Trends Comput Inf Sci 3(1):1–9

    Google Scholar 

  6. Bollen J, Mao J, Zeng X (2010) Twitter mood predicts the stock market, CoRR. vol. abs/1010.3003

  7. Rechenthin M, Street WN, Srinivasan P (2013) Stock chatter: using stock sentiment to predict price direction. Algorithm Finance 2(3–4):169–196

    Article  Google Scholar 

  8. Zabir HK, Tasnim SA, Md. Hussain MA (2011) Price prediction of share market using artificial neural network (ANN). Int J Comput Appl 22(2):42–47

    Google Scholar 

  9. Setty DV, Rangaswamy TM, Subramanya KN (2010) A review on data mining applications to the performance of stock marketing. Int J Comput Appl 1:33–43

    Google Scholar 

  10. Rechenthin MD (2014) Machine-learning classification techniques for the analysis and prediction of high-frequency stock direction. Ph.D. thesis, University of Iowa, USA

  11. Buche A, Chandak MB (2016) Stock market prediction using text opinion mining: a survey. Int J Adv Res Comput Sci Softw Eng 6:566–569

    Google Scholar 

  12. Twomey JM, Smith AE (1997) Validation and verification. In: Kartam N, Flood I, Garrett JH (eds) Artificial neural networks for civil engineers: fundamentals and applications. ASCE, New York, pp 44–64

    Google Scholar 

  13. Cheng CH, Chen YS (2007) Fundamental analysis of stock trading systems using classification techniques. In: International conference on machine learning and cybernetics, vol 3, Hong Kong, China, pp 1377–1382

  14. Gudelek, MU, Boluk SA, Ozbayoglu AM (2017) A deep learning based stock trading model with 2-D CNN trend detection. In: IEEE (SSCI), Honolulu, HI, USA, pp 1–8

  15. Dang M, Duong D (2016) Improvement methods for stock market prediction using financial news articles. In: Proceedings of 3rd national foundation for science and technology development conference on information and computer science (NICS), Danang, Vietnam, pp 125–129

  16. Ding X, Zhang Y, Liu T, Duan J (2015) Deep learning for event-driven stock prediction. In: International joint conference on artificial intelligence, AAAI Press, pp 2327–2333

  17. Zhang X, Zhang Y, Wang S, Yao Y, Fang B, Philip SY (2018) Improving stock market prediction via heterogeneous information fusion. Knowl-Based Syst 143:236–247

    Article  Google Scholar 

  18. Li JH, Bu, Wu J (2017) Sentiment-aware stock market prediction: a deep learning method. In: International conference on service systems and service management (ICSSSMʼ17), IEEE, Dalian, China, pp 1–6

  19. Yunus Y, Halid K, Jamshidi M (2014) Stock market prediction by using artificial neural network. In: World automation congress (WAC), pp 718–722

  20. Mehak U, Syed HA, Kamran R, Syed SAA (2016) Stock market prediction using machine learning techniques. In: International conference on computer and information sciences (ICCOINS), pp 322–327

Download references

Author information

Authors and Affiliations

  1. Singhania University, Pacheri Bari, Jhunjhunu, Rajasthan, India

    Ashwini Saini & Anoop Sharma

Authors
  1. Ashwini Saini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anoop Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ashwini Saini.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saini, A., Sharma, A. Predicting the Unpredictable: An Application of Machine Learning Algorithms in Indian Stock Market. Ann. Data. Sci. 9, 791–799 (2022). https://doi.org/10.1007/s40745-019-00230-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40745-019-00230-7

Keywords

Search

Navigation