Surveying various genetic programming (GP) approaches to forecast real-time trends prices in the stock market
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Gite:2017:ieeeCC,
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author = "Balasaheb Gite and Khalid Sayed and Navin Mutha and
Saurabhkumar Marpadge and Kshitij Patil",
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booktitle = "2017 Computing Conference",
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title = "Surveying various genetic programming (GP) approaches
to forecast real-time trends prices in the stock
market",
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year = "2017",
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pages = "131--134",
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abstract = "The share prices in the stock market are known for
their extreme unpredictability and attempts to identify
any familiar patterns in the prices poses a confounding
problem for both fundamental & technical analysts. This
article attempts to use symbolic regression
capabilities of GP and a market trend indicator (RSI)
to predict the price and trend of the particular stock
as accurately as possible. The use of a market
indicator to independently forecast the trend without
any role of GP serves as a verification mechanism to
the price predicted by GP for the next day to further
validate the authenticity of the price of the stock in
the context of the real-time stock market. Extensive
testing has been done on the various evolution
parameters and functions of GP to customize the GP
approach as much as possible to suit the current
application and optimise the results. Though obtained
results can never be fully relied on by real technical
analysts of the stock market, it could definitely be
used as a decision making support.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/SAI.2017.8252093",
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month = jul,
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notes = "Also known as \cite{8252093}",
- }
Genetic Programming entries for
Balasaheb Gite
Khalid Sayed
Navin Mutha
Saurabhkumar Marpadge
Kshitij Patil
Citations