Indian Stock Market Prediction Using Machine Learning and Sentiment Analysis

Ashish Pathak, Nisha P. Shetty

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Stock market is a very volatile in-deterministic system with vast number of factors influencing the direction of trend on varying scales and multiple layers. Efficient Market Hypothesis (EMH) states that the market is unbeatable. This makes predicting the uptrend or downtrend a very challenging task. This research aims to combine multiple existing techniques into a much more robust prediction model which can handle various scenarios in which investment can be beneficial. Existing techniques like sentiment analysis or neural network techniques can be too narrow in their approach and can lead to erroneous outcomes for varying scenarios. By combing both techniques, this prediction model can provide more accurate and flexible recommendations. Embedding technical indicators will guide the investor to minimize the risk and reap better returns.

Original languageEnglish
Title of host publicationComputational Intelligence in Data Mining - Proceedings of the International Conference on CIDM 2017
EditorsAjith Abraham, Himansu Sekhar Behera, Bighnaraj Naik, Janmenjoy Nayak
PublisherSpringer Verlag
Pages595-603
Number of pages9
ISBN (Print)9789811080548
DOIs
Publication statusPublished - 01-01-2019
Externally publishedYes
Event4th International Conference on Computational Intelligence in Data Mining, ICCIDM 2017 - Sambalpur, India
Duration: 11-11-201712-11-2017

Publication series

NameAdvances in Intelligent Systems and Computing
Volume711
ISSN (Print)2194-5357

Conference

Conference4th International Conference on Computational Intelligence in Data Mining, ICCIDM 2017
CountryIndia
CitySambalpur
Period11-11-1712-11-17

Fingerprint

Learning systems
Neural networks
Financial markets

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Pathak, A., & Shetty, N. P. (2019). Indian Stock Market Prediction Using Machine Learning and Sentiment Analysis. In A. Abraham, H. S. Behera, B. Naik, & J. Nayak (Eds.), Computational Intelligence in Data Mining - Proceedings of the International Conference on CIDM 2017 (pp. 595-603). (Advances in Intelligent Systems and Computing; Vol. 711). Springer Verlag. https://doi.org/10.1007/978-981-10-8055-5_53
Pathak, Ashish ; Shetty, Nisha P. / Indian Stock Market Prediction Using Machine Learning and Sentiment Analysis. Computational Intelligence in Data Mining - Proceedings of the International Conference on CIDM 2017. editor / Ajith Abraham ; Himansu Sekhar Behera ; Bighnaraj Naik ; Janmenjoy Nayak. Springer Verlag, 2019. pp. 595-603 (Advances in Intelligent Systems and Computing).
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Pathak, A & Shetty, NP 2019, Indian Stock Market Prediction Using Machine Learning and Sentiment Analysis. in A Abraham, HS Behera, B Naik & J Nayak (eds), Computational Intelligence in Data Mining - Proceedings of the International Conference on CIDM 2017. Advances in Intelligent Systems and Computing, vol. 711, Springer Verlag, pp. 595-603, 4th International Conference on Computational Intelligence in Data Mining, ICCIDM 2017, Sambalpur, India, 11-11-17. https://doi.org/10.1007/978-981-10-8055-5_53

Indian Stock Market Prediction Using Machine Learning and Sentiment Analysis. / Pathak, Ashish; Shetty, Nisha P.

Computational Intelligence in Data Mining - Proceedings of the International Conference on CIDM 2017. ed. / Ajith Abraham; Himansu Sekhar Behera; Bighnaraj Naik; Janmenjoy Nayak. Springer Verlag, 2019. p. 595-603 (Advances in Intelligent Systems and Computing; Vol. 711).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Pathak A, Shetty NP. Indian Stock Market Prediction Using Machine Learning and Sentiment Analysis. In Abraham A, Behera HS, Naik B, Nayak J, editors, Computational Intelligence in Data Mining - Proceedings of the International Conference on CIDM 2017. Springer Verlag. 2019. p. 595-603. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-981-10-8055-5_53