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Homogenous Ensemble of Time-Series Models for Indian Stock Market

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Big Data Analytics (BDA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11297))

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Abstract

In the present era, Stock Market has become the storyteller of all the financial activity of any country. Therefore, stock market has become the place of high risks, but even then it is attracting the mass because of its high return value. Stock market tells about the economy of any country and has become one of the biggest investment place for the general public. In this manuscript, we present the various forecasting approaches and linear regression algorithm to successfully predict the Bombay Stock Exchange (BSE) SENSEX value with high accuracy. Depending upon the analysis performed, it can be said successfully that Linear Regression in combination with different mathematical functions prepares the best model. This model gives the best output with BSE SENSEX values and Gross Domestic Product (GDP) values as it shows the least p-value as 5.382e−10 when compared with other model’s p-values.

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Author information

Authors and Affiliations

  1. Gautam Buddha University, Greater Noida, Uttar Pradesh, India

    Sourabh Yadav

  2. Dr B R Ambedkar National Institute of Technology, Jalandhar, Punjab, India

    Nonita Sharma

Authors
  1. Sourabh Yadav
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  2. Nonita Sharma
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Corresponding author

Correspondence to Sourabh Yadav .

Editor information

Editors and Affiliations

  1. Ashoka University, Sonepat, India

    Anirban Mondal

  2. IBM Research - India, New Delhi, India

    Himanshu Gupta

  3. University of Minnesota, Minneapolis, MN, USA

    Jaideep Srivastava

  4. IIIT, Hyderabad, India

    P. Krishna Reddy

  5. National Institute of Technology, Warangal, India

    D.V.L.N. Somayajulu

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Yadav, S., Sharma, N. (2018). Homogenous Ensemble of Time-Series Models for Indian Stock Market. In: Mondal, A., Gupta, H., Srivastava, J., Reddy, P., Somayajulu, D. (eds) Big Data Analytics. BDA 2018. Lecture Notes in Computer Science(), vol 11297. Springer, Cham. https://doi.org/10.1007/978-3-030-04780-1_7

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  • DOI: https://doi.org/10.1007/978-3-030-04780-1_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04779-5

  • Online ISBN: 978-3-030-04780-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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