@article {L{\textquoteright}Her41, author = {Jean-Fran{\c c}ois L{\textquoteright}Her and Tammam Mouakhar and Mathieu Roberge}, title = {Timing Small versus Large Stocks}, volume = {34}, number = {1}, pages = {41--50}, year = {2007}, doi = {10.3905/jpm.2007.698033}, publisher = {Institutional Investor Journals Umbrella}, abstract = {Historical evidence suggests that accurate timing can reap huge potential profits in trading between large and small stocks. Artificial intelligence approaches{\textemdash}recursive partitioning, neural networks, and genetic algorithms{\textemdash}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}, issn = {0095-4918}, URL = {https://jpm.pm-research.com/content/34/1/41}, eprint = {https://jpm.pm-research.com/content/34/1/41.full.pdf}, journal = {The Journal of Portfolio Management} }