A genetic network programming with learning approach for enhanced stock trading model
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- @Article{Chen200912537,
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author = "Yan Chen and Shingo Mabu and Kaoru Shimada and
Kotaro Hirasawa",
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title = "A genetic network programming with learning approach
for enhanced stock trading model",
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journal = "Expert Systems with Applications",
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volume = "36",
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number = "10",
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pages = "12537--12546",
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year = "2009",
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ISSN = "0957-4174",
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DOI = "doi:10.1016/j.eswa.2009.05.054",
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URL = "http://www.sciencedirect.com/science/article/B6V03-4WC113D-2/2/a6c6277183e3b22cc3cc50ba71d7062f",
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keywords = "genetic algorithms, genetic programming, Genetic
Network Programming, Sarsa Learning, Stock trading
model, Technical Index, Candlestick Chart",
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abstract = "In this paper, an enhancement of stock trading model
using Genetic Network Programming (GNP) with Sarsa
Learning is described. There are three important points
in this paper: First, we use GNP with Sarsa Learning as
the basic algorithm while both Technical Indices and
Candlestick Charts are introduced for efficient stock
trading decision-making. In order to create more
efficient judgement functions to judge the current
stock price appropriately, Importance Index (IMX) has
been proposed to tell GNP the timing of buying and
selling stocks. Second, to improve the performance of
the proposed GNP-Sarsa algorithm, we proposed a new
method that can learn the appropriate function
describing the relation between the value of each
technical index and the value of the IMX. This is an
important point that devotes to the enhancement of the
GNP-Sarsa algorithm. The third point is that in order
to create more efficient judgment functions, sub-nodes
are introduced in each node to select appropriate stock
price information depending on the situations and to
determine appropriate actions (buying/selling). To
confirm the effectiveness of the proposed method, we
carried out the simulation and compared the results of
GNP-Sarsa with other methods like GNP with Actor
Critic, GNP with Candlestick Chart, GA and Buy&Hold
method. The results shows that the stock trading model
using GNP-Sarsa outperforms all the other methods.",
- }
Genetic Programming entries for
Yan Chen
Shingo Mabu
Kaoru Shimada
Kotaro Hirasawa
Citations