Paper Info
Title
Evolving decision rules to predict investment opportunities
Abstract
This paper is motivated by the interest in finding significant movements in financial stock prices. However, when the number of profitable opportunities is scarce, the prediction of these cases is difficult. In a previous work, we have introduced evolving decision rules (EDR) to detect financial opportunities. The objective of EDR is to classify the minority class (positive cases) in imbalanced environments. EDR provides a range of classifications to find the best balance between not making mistakes and not missing opportunities. The goals of this paper are: 1) to show that EDR produces a range of solutions to suit the investor’s preferences and 2) to analyze the factors that benefit the performance of EDR. A series of experiments was performed. EDR was tested using a data set from the London Financial Market. To analyze the EDR behaviour, another experiment was carried out using three artificial data sets, whose solutions have different levels of complexity. Finally, an illustrative example was provided to show how a bigger collection of rules is able to classify more positive cases in imbalanced data sets. Experimental results show that: 1) EDR offers a range of solutions to fit the risk guidelines of different types of investors, and 2) a bigger collection of rules is able to classify more positive cases in imbalanced environments.
Year
DOI
Venue
2008
10.1007/s11633-008-0022-2
International Journal of Automation and Computing
Keywords
DocType
Volume
genetic programming(gp),evolution of rules.,imbalanced classes,classification,machine learning,genetic programming gp,financial market,decision rule,profitability
Journal
5
Issue
ISSN
Citations 
1
1751-8520
12
PageRank 
References 
Authors
0.62
18
5
Name
Order
Citations
PageRank
alma1120.62
lilia2120.62
Alma Lilia Garcia-Almanza3292.56
Edward P. K. Tsang489987.77
Edward P. K. Tsang589987.77