Analysis and prediction of Indian stock market: a machine-learning approach
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DOI: 10.1007/s13198-023-01934-z
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- Sharma, Chandradew & Banerjee, Kinjal, 2015. "A study of correlations in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 321-330.
- Renault, Thomas, 2017.
"Intraday online investor sentiment and return patterns in the U.S. stock market,"
Journal of Banking & Finance, Elsevier, vol. 84(C), pages 25-40.
- Thomas Renault, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Post-Print hal-03205113, HAL.
- Thomas Renault, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03205113, HAL.
- Liu, Qingbai & Wang, Chuanjie & Zhang, Ping & Zheng, Kaixin, 2021. "Detecting stock market manipulation via machine learning: Evidence from China Securities Regulatory Commission punishment cases," International Review of Financial Analysis, Elsevier, vol. 78(C).
- Chandradew Sharma & Kinjal Banerjee, 2015. "A Study of Correlations in the Stock Market," Papers 1504.05844, arXiv.org.
- Sun, Andrew & Lachanski, Michael & Fabozzi, Frank J., 2016. "Trade the tweet: Social media text mining and sparse matrix factorization for stock market prediction," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 272-281.
- Lee, Hsiu-Chuan & Lee, Yun-Huan & Lu, Yang-Cheng & Wang, Yu-Chun, 2020. "States of psychological anchors and price behavior of Japanese yen futures," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
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Keywords
Stock market prediction; Sentiment analysis; RMSE error (root mean square error); Support vector machine; Artificial intelligence; Random forest; Gradient boosting regressor; Machine learning; LSTM; KNearest neighbour; Normalization;All these keywords.
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