Automated trading with performance weighted random forests and seasonality
Integrity signals
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Most existing studies compared ensemble classifiers or ensemble regressors, but not both.
“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]
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- 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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