@article {Miller116, author = {Keith L. Miller and Chee Ooi and Hong Li and Daniel Giamouridis}, title = {Size Rotation in the U.S. Equity Market}, volume = {39}, number = {2}, pages = {116--127}, year = {2013}, doi = {10.3905/jpm.2013.39.2.116}, publisher = {Institutional Investor Journals Umbrella}, abstract = {In this article, Miller, Ooi, Lee, and Giamouridis develop a hybrid model that relies on the nonlinear classification decision tree (DT) approach, and also on multivariate predictive regressions, to help implement a size rotation strategy in the U.S. equity markets. They derive an investment prediction with a two-stage algorithm. In the first stage, they use a decision tree to determine whether large-cap or small-cap stocks will outperform in the subsequent quarter. In the second stage, the authors use a multiple linear regression model to predict whether large-cap stocks will outperform or underperform small-cap stocks in the next quarter. A binary variable obtained from the first stage of the analysis{\textemdash}the DT model{\textemdash}is a key variable in the second-stage model. The authors find that a size rotation strategy based on the proposed hybrid model outperforms strategies based on the constituent models, as well as alternative strategies investigated in other studies.TOPICS: Exchanges/markets/clearinghouses, global, analysis of individual factors/risk premia}, issn = {0095-4918}, URL = {https://jpm.pm-research.com/content/39/2/116}, eprint = {https://jpm.pm-research.com/content/39/2/116.full.pdf}, journal = {The Journal of Portfolio Management} }