Indian Stock Market Prediction Using Machine Learning and Sentiment Analysis

Ashish Pathak; Nisha P. Shetty

doi:10.1007/978-981-10-8055-5_53

Indian Stock Market Prediction Using Machine Learning and Sentiment Analysis

Ashish Pathak^*
, Nisha P. Shetty

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

41 Citations (Scopus)

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 language	English
Title of host publication	Computational Intelligence in Data Mining - Proceedings of the International Conference on CIDM 2017
Editors	Ajith Abraham, Himansu Sekhar Behera, Bighnaraj Naik, Janmenjoy Nayak
Publisher	Springer Verlag
Pages	595-603
Number of pages	9
ISBN (Print)	9789811080548
DOIs	https://doi.org/10.1007/978-981-10-8055-5_53
Publication status	Published - 01-01-2019
Event	4th International Conference on Computational Intelligence in Data Mining, ICCIDM 2017 - Sambalpur, India Duration: 11-11-2017 → 12-11-2017

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	711
ISSN (Print)	2194-5357

Conference

Conference	4th International Conference on Computational Intelligence in Data Mining, ICCIDM 2017
Country/Territory	India
City	Sambalpur
Period	11-11-17 → 12-11-17

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
General Computer Science

Access to Document

10.1007/978-981-10-8055-5_53

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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title = "Indian Stock Market Prediction Using Machine Learning and Sentiment Analysis",

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.",

author = "Ashish Pathak and Shetty, \{Nisha P.\}",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Singapore Pte Ltd.; 4th International Conference on Computational Intelligence in Data Mining, ICCIDM 2017 ; Conference date: 11-11-2017 Through 12-11-2017",

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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 proceeding › Conference contribution

TY - GEN

T1 - Indian Stock Market Prediction Using Machine Learning and Sentiment Analysis

AU - Pathak, Ashish

AU - Shetty, Nisha P.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

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ER -

Indian Stock Market Prediction Using Machine Learning and Sentiment Analysis

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