Co-evolved genetic programs for stock market trading

Q1 Economics, Econometrics and Finance

Intelligent Systems in Accounting, Finance and Management Pub Date : 2019-11-15 DOI:10.1002/isaf.1458

Jason F. Nicholls, Andries P. Engelbrecht

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引用次数: 1

Abstract

The profitability of trading rules evolved by three different optimised genetic programs, namely a single population genetic program (GP), a co-operative co-evolved GP, and a competitive co-evolved GP is compared. Profitability is determined by trading thirteen listed shares on the Johannesburg Stock Exchange (JSE) over a period of April 2003 to June 2008. An empirical study presented here shows that GPs can generate profitable trading rules across a variety of industries and market conditions. The results show that the co-operative co-evolved GP generates trading rules perform significantly worse than a single population GP and a competitively co-evolved GP. The results also show that a competitive co-evolved GP and the single population GP produce similar trading rules. The profits returned by the evolved trading rules are compared to the profit returned by the buy-and-hold trading strategy. The evolved trading rules significantly outperform the buy-and-hold strategy when the market trends downwards. No significant difference is identified among the buy-and-hold strategy, the competitive co-evolved GP, and single population GP when the market trends upwards.

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共同进化的股票市场交易基因程序

比较了单种群遗传方案、合作型遗传方案和竞争型遗传方案三种不同优化遗传方案演化出的交易规则的盈利能力。盈利能力是由2003年4月至2008年6月期间在约翰内斯堡证券交易所(JSE)交易的13只上市股票确定的。本文提出的一项实证研究表明，普通合伙人可以在各种行业和市场条件下产生有利可图的交易规则。结果表明，合作型协同进化GP生成交易规则的性能显著低于单种群GP和竞争型协同进化GP。结果还表明，竞争性共同进化GP和单种群GP产生了相似的交易规则。将改进后的交易规则所带来的利润与买入并持有交易策略所带来的利润进行比较。当市场趋向下行时，演化出的交易规则明显优于买入并持有策略。当市场呈上升趋势时，买入持有策略、竞争性共同进化GP和单一种群GP之间没有显著差异。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Intelligent Systems in Accounting, Finance and Management Economics, Econometrics and Finance-Finance

CiteScore

6.00

自引率

0.00%

发文量

期刊介绍： Intelligent Systems in Accounting, Finance and Management is a quarterly international journal which publishes original, high quality material dealing with all aspects of intelligent systems as they relate to the fields of accounting, economics, finance, marketing and management. In addition, the journal also is concerned with related emerging technologies, including big data, business intelligence, social media and other technologies. It encourages the development of novel technologies, and the embedding of new and existing technologies into applications of real, practical value. Therefore, implementation issues are of as much concern as development issues. The journal is designed to appeal to academics in the intelligent systems, emerging technologies and business fields, as well as to advanced practitioners who wish to improve the effectiveness, efficiency, or economy of their working practices. A special feature of the journal is the use of two groups of reviewers, those who specialize in intelligent systems work, and also those who specialize in applications areas. Reviewers are asked to address issues of originality and actual or potential impact on research, teaching, or practice in the accounting, finance, or management fields. Authors working on conceptual developments or on laboratory-based explorations of data sets therefore need to address the issue of potential impact at some level in submissions to the journal.