Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/44169
Title: Application of evolutionary computation for rule discovery in stock algorithmic trading : a literature review
Authors: Hu, Y
Liu, K
Zhang, X
Su, L
Ngai, EWT 
Liu, M
Keywords: Algorithmic trading
Classification framework
Evolutionary computation
Literature review
Rule discovery
Stock trading rule
Issue Date: 2015
Publisher: Elsevier
Source: Applied soft computing, 2015, v. 36, p. 534-551 How to cite?
Journal: Applied soft computing 
Abstract: Despite the wide application of evolutionary computation (EC) techniques to rule discovery in stock algorithmic trading (AT), a comprehensive literature review on this topic is unavailable. Therefore, this paper aims to provide the first systematic literature review on the state-of-the-art application of EC techniques for rule discovery in stock AT. Out of 650 articles published before 2013 (inclusive), 51 relevant articles from 24 journals were confirmed. These papers were reviewed and grouped into three analytical method categories (fundamental analysis, technical analysis, and blending analysis) and three EC technique categories (evolutionary algorithm, swarm intelligence, and hybrid EC techniques). A significant bias toward the applications of genetic algorithm-based (GA) and genetic programming-based (GP) techniques in technical trading rule discovery is observed. Other EC techniques and fundamental analysis lack sufficient study. Furthermore, we summarize the information on the evaluation scheme of selected papers and particularly analyze the researches which compare their models with buy and hold strategy (B&H). We observe an interesting phenomenon where most of the existing techniques perform effectively in the downtrend and poorly in the uptrend, and considering the distribution of research in the classification framework, we suggest that this phenomenon can be attributed to the inclination of factor selections and problem in transaction cost selections. We also observe the significant influence of the transaction cost change on the margins of excess return. Other influenced factors are also presented in detail. The absence of ways for market trend prediction and the selection of transaction cost are two major limitations of the studies reviewed. In addition, the combination of trading rule discovery techniques and portfolio selection is a major research gap. Our review reveals the research focus and gaps in applying EC techniques for rule discovery in stock AT and suggests a roadmap for future research.
URI: http://hdl.handle.net/10397/44169
ISSN: 1568-4946
EISSN: 1872-9681
DOI: 10.1016/j.asoc.2015.07.008
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