Genetic Algorithms and Trading Strategies: New Evidences from Financially Interesting Time Series

Chueh-Inong Taso ()
Additional contact information
Chueh-Inong Taso: National Chengchi University

No 552, Computing in Economics and Finance 1999 from Society for Computational Economics

Abstract: In this paper, the performance of canonical GA-based trading strategies are evaluated under different time series. The time series considered include a variety of financial time series, ranging from linear and nonlinear stationary time series to chaotic time series. Unlike many existing applications of computational intelligence in financial engineering, for each performance criterion, we provide rigourous asymptotic statistical tests based on a Monte Carlo simulation. In addition, the criteria chosen are much more extensive than in the existing literature. These include the profit ratio, risk, the Sharpe ratio, maximum drawdown, and the luck coefficient. As a result, this study provides a thorough understanding of the effectiveness of canonical GAs for generating trading strategies under different financial time series.

Date: 1999-03-01
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf9:552

Access Statistics for this paper

More papers in Computing in Economics and Finance 1999 from Society for Computational Economics CEF99, Boston College, Department of Economics, Chestnut Hill MA 02467 USA. Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F. Baum ().