IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v9y2022i4d10.1007_s40745-019-00230-7.html
   My bibliography  Save this article

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

Author

Listed:
  • Ashwini Saini

    (Singhania University)

  • Anoop Sharma

    (Singhania University)

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.

Suggested Citation

  • Ashwini Saini & Anoop Sharma, 2022. "Predicting the Unpredictable: An Application of Machine Learning Algorithms in Indian Stock Market," Annals of Data Science, Springer, vol. 9(4), pages 791-799, August.
    • Handle: RePEc:spr:aodasc:v:9:y:2022:i:4:d:10.1007_s40745-019-00230-7
    DOI: 10.1007/s40745-019-00230-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-019-00230-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40745-019-00230-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rechenthin, Michael & Street, W. Nick & Srinivasan, Padmini, 2013. "Stock chatter: Using stock sentiment to predict price direction," Algorithmic Finance, IOS Press, vol. 2(3-4), pages 169-196.
    2. Xi Zhang & Yunjia Zhang & Senzhang Wang & Yuntao Yao & Binxing Fang & Philip S. Yu, 2018. "Improving Stock Market Prediction via Heterogeneous Information Fusion," Papers 1801.00588, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kamaladdin Fataliyev & Aneesh Chivukula & Mukesh Prasad & Wei Liu, 2021. "Stock Market Analysis with Text Data: A Review," Papers 2106.12985, arXiv.org, revised Jul 2021.
    2. Linyi Yang & Yingpeng Ma & Yue Zhang, 2023. "Measuring Consistency in Text-based Financial Forecasting Models," Papers 2305.08524, arXiv.org, revised Jun 2023.
    3. Zhou, Zhongbao & Gao, Meng & Liu, Qing & Xiao, Helu, 2020. "Forecasting stock price movements with multiple data sources: Evidence from stock market in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    4. Yardley, Ben, 2020. "The Effects of Donald Trump’s Tweets on The Stock Exchange," MPRA Paper 102578, University Library of Munich, Germany.
    5. Jia‐Yen Huang & Jin‐Hao Liu, 2020. "Using social media mining technology to improve stock price forecast accuracy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 104-116, January.
    6. Karolina Sowinska & Pranava Madhyastha, 2020. "A Tweet-based Dataset for Company-Level Stock Return Prediction," Papers 2006.09723, arXiv.org.
    7. Damien Challet & Ahmed Bel Hadj Ayed, 2014. "Do Google Trend data contain more predictability than price returns?," Papers 1403.1715, arXiv.org.
    8. Li-Chen Cheng & Wei-Ting Lu & Benjamin Yeo, 2023. "Predicting abnormal trading behavior from internet rumor propagation: a machine learning approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    9. Zhou, Zhongbao & Gao, Meng & Xiao, Helu & Wang, Rui & Liu, Wenbin, 2021. "Big data and portfolio optimization: A novel approach integrating DEA with multiple data sources," Omega, Elsevier, vol. 104(C).
    10. Omer Berat Sezer & Mehmet Ugur Gudelek & Ahmet Murat Ozbayoglu, 2019. "Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019," Papers 1911.13288, arXiv.org.
    11. Wen, Danyan & Ma, Chaoqun & Wang, Gang-Jin & Wang, Senzhang, 2018. "Investigating the features of pairs trading strategy: A network perspective on the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 903-918.
    12. Gusev, Maxim & Kroujiline, Dimitri & Govorkov, Boris & Sharov, Sergey V. & Ushanov, Dmitry & Zhilyaev, Maxim, 2014. "Predictable markets? A news-driven model of the stock market," MPRA Paper 58831, University Library of Munich, Germany.
    13. Rosdyana Mangir Irawan Kusuma & Trang-Thi Ho & Wei-Chun Kao & Yu-Yen Ou & Kai-Lung Hua, 2019. "Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market," Papers 1903.12258, arXiv.org.
    14. Qiao Zhou & Ningning Liu, 2020. "A Stock Prediction Model Based on DCNN," Papers 2009.03239, arXiv.org.
    15. Junran Wu & Ke Xu & Jichang Zhao, 2019. "Online reviews can predict long-term returns of individual stocks," Papers 1905.03189, arXiv.org.
    16. Plakandaras, Vasilios & Papadimitriou, Theophilos & Gogas, Periklis & Diamantaras, Konstantinos, 2015. "Market sentiment and exchange rate directional forecasting," Algorithmic Finance, IOS Press, vol. 4(1-2), pages 69-79.
    17. Amit Milstein & Haoran Deng & Guy Revach & Hai Morgenstern & Nir Shlezinger, 2022. "Neural Augmented Kalman Filtering with Bollinger Bands for Pairs Trading," Papers 2210.15448, arXiv.org, revised Sep 2023.
    18. Mohammad Sahabuddin & Md. Aminul Islam & Mosab I. Tabash & Suhaib Anagreh & Rozina Akter & Md. Mizanur Rahman, 2022. "Co-Movement, Portfolio Diversification, Investors’ Behavior and Psychology: Evidence from Developed and Emerging Countries’ Stock Markets," JRFM, MDPI, vol. 15(8), pages 1-15, July.
    19. Xi Zhang & Yixuan Li & Senzhang Wang & Binxing Fang & Philip S. Yu, 2018. "Enhancing Stock Market Prediction with Extended Coupled Hidden Markov Model over Multi-Sourced Data," Papers 1809.00306, arXiv.org.
    20. Theophilos Papadimitriou & Periklis Gogas & Athanasios Fotios Athanasiou, 2020. "Forecasting S&P 500 spikes: an SVM approach," Digital Finance, Springer, vol. 2(3), pages 241-258, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:aodasc:v:9:y:2022:i:4:d:10.1007_s40745-019-00230-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.