Using FFT modelling for equity price modelling

Abstract We examine the explanatory and predictive value of the fast and frugal trees, as developed by Professor Laura Martignon, in understanding financial markets. We ask when do fast-and-frugal trees work well compared to standard statistical methods? The relative success of fast- and-frugal methods is often associated with: uncertainty as opposed to risk, skewed outcome environments or skewed cues invoked to explained those skewed outcomes sparse/or flawed data being available for statistical modelling. We examine these claims by studying how • company size, • analysts following, and impacts upon the relative performance of fast-and-frugal trees versus standard statistical methods (here regression, models). We use our findings to suggest where fast-and-frugal trees (FFT) might be most beneficially used in modelling equity returns.