Abstract
Instance selection is a way to remove unnecessary data that can adversely affect the prediction model, thereby selecting representative and relevant data from the original data set that is expected to improve predictive performance. Instance selection plays an important role in improving the scalability of data mining algorithms and has also proven to be successful over a wide range of classification problems. However, instance selection using an evolutionary approach, as proposed in this study, is different from previous methods that have focused on improving accuracy performance in the stock market (i.e., Up or Down forecast). In fact, we propose a new approach to instance selection that uses genetic algorithms (GAs) to define a set of target labels that can identify the buying and selling signals and then select instances according to three performance measures of the trading system (i.e., the winning ratio, the payoff ratio, and the profit factor). An intelligent ensemble trading system with instance selection using GAs is then developed for investors in the stock market. An empirical study of the proposed model is conducted using 35 companies from the Dow Jones Industrial Average, the New York Stock Exchange, and the Nasdaq Stock Market from January, 2006 to December, 2016.
Recommended Citation
Y. Kim and D. L. Enke, "Instance Selection using Genetic Algorithms for an Intelligent Ensemble Trading System," Procedia Computer Science, vol. 114, pp. 465 - 472, Elsevier, Oct 2017.
The definitive version is available at https://doi.org/10.1016/j.procs.2017.09.030
Meeting Name
Complex Adaptive Systems Conference with Theme: Engineering Cyber Physical Systems, CAS (2017: Oct. 30-Nov. 1, Chicago, IL)
Department(s)
Engineering Management and Systems Engineering
Research Center/Lab(s)
Intelligent Systems Center
Keywords and Phrases
Adaptive systems; Commerce; Data mining; Electronic trading; Embedded systems; Finance; Genetic algorithms; Investments; Data mining algorithm; Dow Jones Industrial averages; Evolutionary approach; Genetic algorithm (GAs); Instance selection; New York Stock Exchange; Predictive performance; Trading systems; Financial markets; Ensemble trading system; Instance selection; Intelligent trading system
International Standard Serial Number (ISSN)
1877-0509
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2017 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Publication Date
01 Oct 2017
