Save the best for last? The treatment of dominant predictors in financial forecasting

We study forecasting applications where the response variable is heavily correlated with one or a small set of covariates which we term dominant predictors. Dominant predictors commonly occur in financial forecasting where future market prices are heavily influenced by current prices, and to a much lesser degree, by many other, more subtle factors such as weather or calendar effects. We hypothesize that dominating predictors may mask the influence of the subtle factors, reducing forecasting accuracy. Consequently, we argue that it is crucial to find means of accurately accounting for the effect of the subtle factors on the response variable. To achieve this we present a two-stage modeling methodology which postpones the introduction of dominating predictors into the model building process until all predictive value from the other covariates has been extracted. To confirm our hypothesis and to test the effectiveness of the two-stage approach, we conduct an empirical study related to forecasting the outcome of sports events, which are well known to exhibit dominating predictors. Our results confirm that especially complex, nonlinear models are vulnerable to the masking effect and benefit from the two-stage paradigm. Our findings have important implications for forecasters who operate in environments where the influence of some predictors on the variable being forecast exceeds those of other covariates by a wide margin and we demonstrate appropriate ways to approach such forecasting tasks.

► Examine whether dominant predictors mask the effect of less predictive covariates.
► Present a two-stage modeling methodology for dealing with dominant predictors.
► Empirically confirm its effectiveness in a sports-betting context.