Using genetic algorithms to find technical trading rules

• 成果类型: Article
• 署名作者: Allen, F; Karjalainen, R
• 署名单位: University of Pennsylvania
• 刊物名称: JOURNAL OF FINANCIAL ECONOMICS
• ISSN/ISSBN: 0304-405X
• DOI: 10.1016/S0304-405X(98)00052-X
• 发表日期: 1999
• 页码: 245-271
• 关键词: trading rules; GENETIC ALGORITHMS; EXCESS RETURNS

摘要

We use a genetic algorithm to learn technical trading rules for the S&P 500 index using daily prices from 1928 to 1995. After transaction costs, the rules do not earn consistent excess returns over a simple buy-and-hold strategy in the out-of-sample test periods. The rules are able to identify periods to be in the index when daily returns are positive and volatility is low and out when the reverse is true. These latter results can largely be explained by low-order serial correlation in stock index returns. (C) 1999 Elsevier Science S.A. All rights reserved.