An in-depth investigation of genetic programming and nine other machine learning algorithms in a financial forecasting problem
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- @InProceedings{Long:2022:CEC,
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author = "Xinpeng Long and Michael Kampouridis and
Delaram Jarchi",
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booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)",
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title = "An in-depth investigation of genetic programming and
nine other machine learning algorithms in a financial
forecasting problem",
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year = "2022",
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editor = "Carlos A. Coello Coello and Sanaz Mostaghim",
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address = "Padua, Italy",
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month = "18-23 " # jul,
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isbn13 = "978-1-6654-6708-7",
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abstract = "Machine learning (ML) techniques have shown to be
useful in the field of financial forecasting. In
particular, genetic programming has been a popular ML
algorithm with proven success in improving financial
forecasting. Meanwhile, the performance of such ML
algorithms depends on a number of factors including
data analysis from different markets, data periods,
forecasting days ahead, and the transaction cost which
have been neglected in most previous studies.
Therefore, the focus of this paper is on investigating
the effect of such factors. We perform an extensive
evaluation of a financial genetic programming-based
approach and compare its performance against 9 popular
machine learning algorithms and the buy and hold
trading strategy. Experiments take place over daily
data from 220 datasets from 10 international markets.
Results show that genetic programming not only provides
profitable results but also outperforms the 9 machine
learning algorithms in terms of risk and Sharpe
ratio.",
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keywords = "genetic algorithms, genetic programming, Machine
learning algorithms, Data analysis, Costs, Machine
learning, Evolutionary computation, Benchmark testing,
Machine learning, Financial forecasting, Algorithmic
trading",
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DOI = "doi:10.1109/CEC55065.2022.9870351",
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notes = "Also known as \cite{9870351}",
- }
Genetic Programming entries for
Xinpeng Long
Michael Kampouridis
Delaram Jarchi
Citations