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

Author

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  • 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
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    References listed on IDEAS

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    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.
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    Cited by:

    1. Arijit Das & Tanmoy Nandi & Prasanta Saha & Suman Das & Saronyo Mukherjee & Sudip Kumar Naskar & Diganta Saha, 2024. "Effect of Leaders Voice on Financial Market: An Empirical Deep Learning Expedition on NASDAQ, NSE, and Beyond," Papers 2403.12161, arXiv.org.

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