NDLI: Repository method to suit different investment strategies

Content Provider	IEEE Xplore Digital Library
Author	Garcia-Almanza, A.L. Tsang, E.P.K.
Copyright Year	2007
Description	Author affiliation: Univ. of Essex, Colchester (Garcia-Almanza, A.L.; Tsang, E.P.K.)
Abstract	This work is motivated by the interest in finding significant movements in financial stock prices. The detection of such movements is important because these could represent good opportunities for invest. However, when the number of profitable opportunities is very small the prediction of these cases is very difficult. In previous works, we have introduced the repository method (RM). The aim of this approach is to classify financial data sets in extreme imbalanced environments. When opportunities are extremely rare, the investor needs a sharper balance between not making mistakes and not missing opportunities. RM offers a range of solutions to suit the risk guidelines of the investor. The aims of this paper are 1) to show that RM can produce a range of solutions to suit the investor's preferences and 2) to analyze the impact of the evolutionary process to RM's performance. Three series of experiments were performed, RM was tested using two artificial data sets whose solutions have different level of complexity. Finally RM was tested in a data set from the London stock market. Experimental results show that: 1) RM offers a range of solutions to fit the risk guidelines of the investor and 2) the contribution of the evolutionary process is very valuable to the performance of RM and 3) RM is able to extract predictive rules even from earliest stages of the evolutionary process.
Starting Page	790
Ending Page	797
File Size	205544
Page Count	8
File Format	PDF
ISBN	9781424413393
DOI	10.1109/CEC.2007.4424551
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-25
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Investments Guidelines Testing Decision trees Performance analysis Data mining Genetic programming Machine learning Computer science Performance evaluation
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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