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Predictive Analysis of Stocks Using Data Mining

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Smart Intelligent Computing and Applications

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 105))

Abstract

There are 60 major stock exchanges around the world with a total value of $69 trillion. Stocks are traded almost daily. Stock data is available on the Internet right from the beginning. Prediction of stock market is an attractive topic for researchers of different fields. Before the advent of machine learning and data science, stock market movement was primarily analyzed using statistical and technical factors. Now with the help of machine learning techniques, it is possible to accurately identify the stock market movement. Various machine learning techniques like support machine vectors, random forests, gradient boosted trees, etc. have been successfully used in the past to predict stock prices.

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Author information

Authors and Affiliations

  1. School of Information Technology and Engineering, VIT University, Vellore, India

    G. Magesh

  2. School of Computer Science and Engineering, VIT University, Vellore, India

    P. Swarnalatha

Authors
  1. G. Magesh
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  2. P. Swarnalatha
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Corresponding author

Correspondence to G. Magesh .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

  2. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

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Magesh, G., Swarnalatha, P. (2019). Predictive Analysis of Stocks Using Data Mining. In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 105. Springer, Singapore. https://doi.org/10.1007/978-981-13-1927-3_30

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  • DOI: https://doi.org/10.1007/978-981-13-1927-3_30

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1926-6

  • Online ISBN: 978-981-13-1927-3

  • eBook Packages: EngineeringEngineering (R0)

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