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Automated trading with performance weighted random forests and seasonality
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“most of the studies compared between ensemble classifiers [12, 18-20, 22, 23, 28] or regressors [21,30] machine learning algorithms, but not both.”
From A comprehensive evaluation of ensemble learning for stock-market prediction (2020) · cites this paper as [30]
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK*
Malkiel, B. G., & Fama, E. F. (1970) · The Journal of Finance · doi:10.1111/j.1540-6261.1970.tb00518.x · Cited by 24,478
Classification and Regression by randomForest
Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Efficient capital markets: A review of theory and empirical work
Fama, E. F. (1970) · The Journal of Finance · doi:10.2307/2325486 · Cited by 13,635
An introduction to variable and feature selection
GuyonIsabelle, & ElisseeffAndré (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944968 · Cited by 8,354
An Introduction to Variable and Feature Selection.
Guyon, I., & Elisseeff, A. (2003) · Cited by 6,576
Ho, T. K. (2002) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.1995.598994 · Cited by 6,311
Hansen, L. K., & Salamon, P. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.58871 · Cited by 4,260
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Ballings, M., Van den Poel, D., Hespeels, N., & Gryp, R. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.05.013 · Cited by 611
Application of machine learning to construction injury prediction
Tixier, A. J. ‐., Hallowell, M. R., Rajagopalan, B., & Bowman, D. (2016) · Automation in Construction · doi:10.1016/j.autcon.2016.05.016 · Cited by 376
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Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020) · Journal of Big Data · doi:10.1186/s40537-020-00299-5 · Cited by 329
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Nikou, M., Mansourfar, G., & Bagherzadeh, J. (2019) · Intelligent systems in accounting, finance and management/Intelligent systems in accounting, finance & management · doi:10.1002/isaf.1459 · Cited by 312
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Ampomah, E. K., Qin, Z., & Nyame, G. (2020) · Information · doi:10.3390/info11060332 · Cited by 270
Hausberg, J. P., Liere-Netheler, K., Packmohr, S., Pakura, S., & Vogelsang, K. (2019) · Journal of Business Economics · doi:10.1007/s11573-019-00956-z · Cited by 288
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Zhang, C., Li, Y., Chen, X., Jin, Y., Tang, P., & Li, J. (2020) · Proceedings · doi:10.1109/icdm50108.2020.00087 · Cited by 28
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Monge, M. E. C., Pastor‐Sanz, I., & Zavala, P. A. H. (2017) · Sustainability · doi:10.3390/su9081419 · Cited by 21
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Su, C.-H., Tu, F., Zhang, X., Shia, B., & Lee, T. (2021) · Journal of Data Science · doi:10.6339/jds.201901_17(1).0004 · Cited by 8
Wang, X., Wang, Y., Weng, B., & Vinel, A. (2020) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.2010.01197 · Cited by 16
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Pandya, J. B., & Jaliya, U. K. (2021) · Indian Journal of Computer Science and Engineering · doi:10.21817/indjcse/2021/v12i6/211206166 · Cited by 6
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Mousavi, S., Esfahanipour, A., & Zarandi, M. H. F. (2019) · Scientia Iranica · doi:10.24200/sci.2019.52323.2661 · Cited by 6
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Jiang, J. (2021) · International Symposium on Computer and Information Processing Technology (ISCIPT) · doi:10.1109/iscipt53667.2021.00026 · Cited by 6
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Metadata
- Authors
- Booth, A., Gerding, E., & McGroarty, F.
- Affiliations
- University of Southampton (GB)
- Corresponding author
- Booth, Ash
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2014-06
- Volume
- 41
- Issue
- 8
- Pages
- 3651-3661
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2113822305
- Cited by
- 154
- References
- 64
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 6 · 2023: 12 · 2022: 13 · 2021: 21 · 2020: 26 · 2019: 15 · 2018: 12 · 2017: 10 · 2016: 11 · 2015: 7 · 2014: 4
- FWCI
- 10.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Expert Systems with Applications (publishedVersion)
- ePrints Soton (University of Southampton) (submittedVersion)
- 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
- algorithmic trading, seasonal effects, random forest, expert weighting, trading strategy profitability, drawdown, stock market prediction
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