RT Journal Article
SR Electronic
T1 Timing Small versus Large Stocks
JF The Journal of Portfolio Management
FD Institutional Investor Journals
SP 41
OP 50
DO 10.3905/jpm.2007.698033
VO 34
IS 1
A1 Jean-François L'Her
A1 Tammam Mouakhar
A1 Mathieu Roberge
YR 2007
UL https://pm-research.com/content/34/1/41.abstract
AB Historical evidence suggests that accurate timing can reap huge potential profits in trading between large and small stocks. Artificial intelligence approaches—recursive partitioning, neural networks, and genetic algorithms—are an aid in development of size-timing strategies to decide when to be long or short with respect to the U.S. Fama-French small-minus-big portfolio. AI strategies are trained on both a static learning sample and on an expandable learning sample. The results here show that most AI strategies could successfully time the U.S. size premium over 1990-2004. When predictions differ, a consensus strategy, or one favored by two of the three AI approaches, outperforms the SMB strategy more systematically, and reduces transaction costs.TOPICS: Statistical methods, portfolio management/multi-asset allocation, style investing