The benefits of tree-based models for stock selection

The performance of stock relative to its peer group is influenced by a multitude of factors and their interactions, which are typically modelled by investment practitioners in a classical parametric framework. Although such models are in many cases useful for identifying linear interactions, they are less well suited to capturing the higher-order relationships between a company's fundamental characteristics and its subsequent relative return. Despite this, non-parametric and non-linear approaches such as classification and regression trees (CART) have been largely overlooked by the finance industry, which still relies heavily upon linear factor models. This article investigates the use of CART for stock selection within North America in order to highlight some of the advantages of adopting a broader suite of modelling tools. Its focus is on the period since the onset of the Global Financial Crisis in 2007 to late 2010 – a period associated with elevated volatility and sharp swings in investor sentiment. More specifically, we directly compare a CART model against a more traditional linear framework. We observe that the performance of portfolios formed from a tree-based model was quite robust during both the 2007/2008 downturn in equities and the subsequent market recovery. As such, we believe that stock selection models based on the CART approach offer an attractive opportunity to diversify model risk.