Please use this identifier to cite or link to this item:

Title: Pretests for Genetic-Programming Evolved Trading Programs:Zero-Intelligence Strategies and Lottery Trading
Authors: Chen,Shu-Heng;Navet,Nicolas
Date: 2006
Issue Date: 2010-11-24 22:06:03 (UTC+8)
Abstract: Over the last decade, numerous papers have investigated the use of GP for creating financial trading strategies. Typically in the literature results are inconclusive but the investigators always suggest the possibility of further improvements, leaving the conclusion regarding the effectiveness of GP undecided. In this paper, we discuss a series of pretests, based on several variants of random search, aiming at giving more clearcut answers on whether a GP scheme, or any other machine-learning technique, can be effective with the training data at hand. The analysis is illustrated with GP-evolved strategies for three stock exchanges exhibiting different trends.
Relation: Lecture Notes in Computer Science,4234,450-460
Data Type: article
DOI 連結:
Appears in Collections:[經濟學系] 期刊論文

Files in This Item:

File Description SizeFormat
iconip2006.pdf137KbAdobe PDF667View/Open

All items in 學術集成 are protected by copyright, with all rights reserved.

社群 sharing