Genetic Programming in Statistical Arbitrage

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{conf/evoW/SaksM08,

• title = "Genetic Programming in Statistical Arbitrage",
• author = "Philip Saks and Dietmar G. Maringer",
• bibdate = "2008-04-15",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoW2008.html#SaksM08",
• booktitle = "Proceedings of Evo{COMNET}, Evo{FIN}, Evo{HOT}, Evo{IASP}, Evo{MUSART}, Evo{NUM}, Evo{STOC}, and EvoTransLog, Applications of Evolutionary Computing, EvoWorkshops",
• publisher = "Springer",
• year = "2008",
• volume = "4974",
• editor = "Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni {Di Caro} and Rolf Drechsler and Anik{\'o} Ek{\'a}rt and Anna Esparcia-Alc{\'a}zar and Muddassar Farooq and Andreas Fink and Jon McCormack and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Sima Uyar and Shengxiang Yang",
• isbn13 = "978-3-540-78760-0",
• pages = "73--82",
• series = "Lecture Notes in Computer Science",
• DOI = "doi:10.1007/978-3-540-78761-7_8",
• address = "Naples",
• month = "26-28 " # mar,
• keywords = "genetic algorithms, genetic programming",
• 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.",

}

Genetic Programming entries for Philip Saks Dietmar G Maringer

Citations