New evidence about the profitability of small and large stocks and the role of volume obtained using Strongly Typed Genetic Programming
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- @Article{Manahov:2014:JIFMIM2,
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author = "Viktor Manahov and Robert Hudson and Philip Linsley",
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title = "New evidence about the profitability of small and
large stocks and the role of volume obtained using
Strongly Typed Genetic Programming",
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journal = "Journal of International Financial Markets,
Institutions and Money",
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volume = "33",
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pages = "299--316",
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year = "2014",
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ISSN = "1042-4431",
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DOI = "doi:10.1016/j.intfin.2014.08.007",
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URL = "http://www.sciencedirect.com/science/article/pii/S1042443114001115",
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abstract = "We employ a special adaptive form of the Strongly
Typed Genetic Programming (STGP)-based learning
algorithm to develop trading rules based on a survival
of the fittest principle. Employing returns data for
the Russell 1000, Russell 2000 and Russell 3000 indices
the STGP method produces greater returns compared to
random walk benchmark forecasts, and the forecasting
models are statistically significant in respect of
their predictive effectiveness for all three indices
both in- and out-of-sample. Using one-step-ahead STGP
models to investigate the differences in return
patterns between small and large stocks we demonstrate
the superiority of models developed for small-cap
stocks over those developed for large-cap stocks,
indicating that small stocks are more predictable. We
also investigate the relationship between trading
volume and returns, and find that trading volume has
negligible predictive strength, implying it is not
advantageous to develop volume-based trading
strategies.",
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keywords = "genetic algorithms, genetic programming, Forecasting
and simulation, Small Stocks, Agent-based modelling,
Artificial stock market, Capital asset pricing model,
Efficiency",
- }
Genetic Programming entries for
Viktor Manahov
Robert Hudson
Philip Linsley
Citations