Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques
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Directional accuracy in previous machine learning studies can be as high as 90 percent.
“In the former case, Table 1 also reports the directional accuracy, which can be as high as 90 percent (Patel et al., 2015).”
From Bridging the divide in financial market forecasting: machine learners vs. financial economists (2016) · cites this paper as Patel et al., 2015
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Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439 · see citing works
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A systematic review of fundamental and technical analysis of stock market predictions
Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09754-z · Cited by 547 · see citing works
Sonkavde, G., Dharrao, D. S., Bongale, A. M., Deokate, S. T., Doreswamy, D., & Bhat, S. K. (2023) · International Journal of Financial Studies · doi:10.3390/ijfs11030094 · Cited by 262 · see citing works
“Random forest, trained on multiple decision trees and Naïve Bayes, predicts stock movement based on negative or positive probability using Reliance and Infosys’ 10-year historical data (Patel et al. 2015).”
MethodBridging the divide in financial market forecasting: machine learners vs. financial economists
Hsu, M.-W., Lessmann, S., Sung, M.-C., Ma, T., & Johnson, J. E. V. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.05.033 · Cited by 195 · see citing works
Cervelló-Royo, R., Guijarro, F., & Michniuk, K. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.03.017 · Cited by 186 · see citing works
Stock Market Prediction with Gaussian Naïve Bayes Machine Learning Algorithm
Ampomah, E. K., Nyame, G., Qin, Z., Addo, P. C., Gyamfi, E. O., & Gyan, M. (2021) · Informatica · doi:10.31449/inf.v45i2.3407 · Cited by 64 · see citing works
- Authors
- Patel, J., Shah, S., Thakkar, P., & Kotecha, K.
- Affiliations
- Nirma University (IN)
- Corresponding author
- Thakkar, Priyank
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2015-01
- Volume
- 42
- Issue
- 1
- Pages
- 259-268
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2025053102
- Cited by
- 1,141
- References
- 1
- Citations by year
- 2026: 109 · 2025: 127 · 2024: 133 · 2023: 121 · 2022: 131 · 2021: 131 · 2020: 123 · 2019: 115 · 2018: 73 · 2017: 32 · 2016: 30 · 2015: 15 · 2014: 1
- FWCI
- 32.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Expert Systems with Applications (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Keywords
- stock price prediction, stock index forecasting, technical indicators, Indian stock market, CNX Nifty, BSE Sensex, Reliance Industries, Infosys, random forest, artificial neural networks, support vector machine, naive Bayes, forecasting model comparison
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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