TY - GEN
T1 - Outperforming buy-and-hold with evolved technical trading rules
T2 - EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC, EvoApplicatons 2010
AU - Lohpetch, Dome
AU - Corne, David
PY - 2010
Y1 - 2010
N2 - Genetic programming (GP) is increasingly popular as a research tool for applications in finance and economics. One thread in this area is the use of GP to discover effective technical trading rules. In a seminal article, Allen & Karjalainen (1999) used GP to find rules that were profitable, but were nevertheless outperformed by the simple "buy and hold" trading strategy. Many succeeding attempts have reported similar findings. There are a small handful of cases in which such work has managed to find rules that outperform buy-and-hold, but these have tended to be difficult to replicate. Recently, however, Lohpetch & Corne (2009) investigated work by Becker & Seshadri (2003), which showed outperformance of buy-and-hold. In turn, Becker & Seshadri's work had made several modifications to Allen & Karjalainen's work, including the adoption of monthly rather than daily trading. Lohpetch et al (2009) provided a replicable account of this, and also showed how further modifications enabled fairly reliable outperformance of buy-and-hold. It remained unclear, however, whether adoption of monthly trading is necessary to achieve robust outperformance of buy-and-hold. Here we investigate and compare each of daily, weekly and monthly trading; we find that outperformance of buy-and-hold can be achieved even for daily trading, but as we move from monthly to daily trading the performance of evolved rules becomes increasingly dependent on prevailing market conditions. © 2010 Springer-Verlag Berlin Heidelberg.
AB - Genetic programming (GP) is increasingly popular as a research tool for applications in finance and economics. One thread in this area is the use of GP to discover effective technical trading rules. In a seminal article, Allen & Karjalainen (1999) used GP to find rules that were profitable, but were nevertheless outperformed by the simple "buy and hold" trading strategy. Many succeeding attempts have reported similar findings. There are a small handful of cases in which such work has managed to find rules that outperform buy-and-hold, but these have tended to be difficult to replicate. Recently, however, Lohpetch & Corne (2009) investigated work by Becker & Seshadri (2003), which showed outperformance of buy-and-hold. In turn, Becker & Seshadri's work had made several modifications to Allen & Karjalainen's work, including the adoption of monthly rather than daily trading. Lohpetch et al (2009) provided a replicable account of this, and also showed how further modifications enabled fairly reliable outperformance of buy-and-hold. It remained unclear, however, whether adoption of monthly trading is necessary to achieve robust outperformance of buy-and-hold. Here we investigate and compare each of daily, weekly and monthly trading; we find that outperformance of buy-and-hold can be achieved even for daily trading, but as we move from monthly to daily trading the performance of evolved rules becomes increasingly dependent on prevailing market conditions. © 2010 Springer-Verlag Berlin Heidelberg.
KW - Data mining
KW - Genetic programming
KW - Technical trading rules
U2 - 10.1007/978-3-642-12242-2-18
DO - 10.1007/978-3-642-12242-2-18
M3 - Conference contribution
SN - 3642122418
SN - 9783642122415
VL - 6025 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 171
EP - 181
BT - Applications of Evolutionary Computation - EvoApplications 2010: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoMUSART, and EvoTRANSLOG, Proceedings
Y2 - 7 April 2010 through 9 April 2010
ER -