A hybrid procedure for stock price prediction by integrating self-organizing map and genetic programming
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- @Article{Hsu:2011:ESA,
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author = "Chih-Ming Hsu",
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title = "A hybrid procedure for stock price prediction by
integrating self-organizing map and genetic
programming",
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journal = "Expert Systems with Applications",
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year = "2011",
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volume = "38",
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number = "11",
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pages = "14026--14036",
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ISSN = "0957-4174",
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DOI = "doi:10.1016/j.eswa.2011.04.210",
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URL = "http://www.sciencedirect.com/science/article/B6V03-52T13T7-7/2/c2626c201c0da6cbc20628185936eaf3",
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keywords = "genetic algorithms, genetic programming, Stock price
prediction, Self-organising map",
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size = "11 pages",
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abstract = "Stock price prediction is a very important financial
topic, and is considered a challenging task and worthy
of the considerable attention received from both
researchers and practitioners. Stock price series have
properties of high volatility, complexity, dynamics and
turbulence, thus the implicit relationship between the
stock price and predictors is quite dynamic. Hence, it
is difficult to tackle the stock price prediction
problems effectively by using only single soft
computing technique. This study hybridises a
self-organizing map (SOM) neural network and genetic
programming (GP) to develop an integrated procedure,
namely, the SOM-GP procedure, in order to resolve
problems inherent in stock price predictions. The SOM
neural network is used to divide the sample data into
several clusters, in such a manner that the objects
within each cluster possess similar properties to each
other, but differ from the objects in other clusters.
The GP technique is applied to construct a mathematical
prediction model that describes the functional
relationship between technical indicators and the
closing price of each cluster formed in the SOM neural
network. The feasibility and effectiveness of the
proposed hybrid SOM-GP prediction procedure are
demonstrated through experiments aimed at predicting
the finance and insurance sub-index of TAIEX (Taiwan
stock exchange capitalisation weighted stock index).
Experimental results show that the proposed SOM-GP
prediction procedure can be considered a feasible and
effective tool for stock price predictions, as based on
the overall prediction performance indices.
Furthermore, it is found that the frequent and
alternating rise and fall, as well as the range of
daily closing prices during the period, significantly
increase the difficulties of predicting.",
- }
Genetic Programming entries for
Chih-Ming Hsu
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