Abstract
Genetic programming is now a common research tool in financial applications. One classic line of exploration is their use to find effective trading rules for individual stocks or for groups of stocks (such as an index). The classic work in this area (Allen & Karjaleinen, 99) found profitable rules, but which did not outperform a straightforward "buy and hold" strategy. Several later works report similar outcomes, while a small number of works achieve out-performance of buy and hold, but prove difficult to replicate. We focus here on indicating clearly how the performance in one such study (Becker & Seshadri, 03) was replicated, and we carry out additional investigations which point towards guidelines for generating results that robustly outperform buy-and-hold. These guidelines relate to strategies for organizing the training dataset, and aspects of the fitness function. ©2009 IEEE.
Original language | English |
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Title of host publication | 2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009 - Proceedings |
Pages | 439-444 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 2009 |
Event | 2009 World Congress on Nature and Biologically Inspired Computing - Coimbatore, India Duration: 9 Dec 2009 → 11 Dec 2009 |
Conference
Conference | 2009 World Congress on Nature and Biologically Inspired Computing |
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Abbreviated title | NABIC 2009 |
Country/Territory | India |
City | Coimbatore |
Period | 9/12/09 → 11/12/09 |
Keywords
- Genetic programming
- Stock trading
- Technical trading rules