Sciweavers

Free Online Productivity Tools i2Speak i2Symbol i2OCR iTex2Img iWeb2Print iWeb2Shot i2Type iPdf2Split iPdf2Merge i2Bopomofo i2Arabic i2Style i2Image i2PDF iLatex2Rtf Sci2ools

12

EVOW
2008
Springer

favoriteEmaildiscussreport

132views Artificial Intelligence» more EVOW 2008»

Genetic Programming in Statistical Arbitrage

13 years 6 months ago

Genetic Programming in Statistical Arbitrage

Download sci2s.ugr.es

Abstract. This paper employs genetic programming to discover statistical arbitrage strategies on the banking sector in the Euro Stoxx universe. Binary decision rules are evolved using two different representations. The first is the classical single tree approach, while the second is a dual tree structure where evaluation is contingent on the current market position. Hence, buy and sell rules are co-evolved. Both methods are capable of discovering significant statistical arbitrage strategies.

Philip Saks, Dietmar G. Maringer

Real-time Traffic

Artificial Intelligence | Binary Decision Rules | EVOW 2008 | Significant Statistical Arbitrage | Statistical Arbitrage Strategies |

claim paper

Related Content

» Application of the Linux cluster for exhaustive window haplotype analysis using the FBAT a...

» Using Factorial Experiments to Evaluate the Effect of Genetic Programming Parameters

» Genetic Programming Neural Networks as a Bioinformatics Tool for Human Genetics

» Pixel Statistics and False Alarm Area in Genetic Programming for Object Detection

» Success effort and other statistics for performance comparisons in genetic programming

» A Statistical Learning Perspective of Genetic Programming

» Model selection in genetic programming

» Evolving distributed algorithms with genetic programming election

» On the genetic programming of timeseries predictors for supply chain management

Post Info
More Details (n/a)

Added	19 Oct 2010
Updated	19 Oct 2010
Type	Conference
Year	2008
Where	EVOW
Authors	Philip Saks, Dietmar G. Maringer

Comments (0)