  In many forecasting settings, there is a specific interest in predicting the
sign of an outcome variable correctly in addition to its magnitude. For
instance, when forecasting armed conflicts, positive and negative log-changes
in monthly fatalities represent escalation and de-escalation, respectively, and
have very different implications. In the ViEWS forecasting challenge, a
prediction competition on state-based violence, a novel evaluation score called
targeted absolute deviation with direction augmentation (TADDA) has therefore
been suggested, which accounts for both for the sign and magnitude of
log-changes. While it has a straightforward intuitive motivation, the empirical
results of the challenge show that a no-change model always predicting a
log-change of zero outperforms all submitted forecasting models under the TADDA
score. We provide a statistical explanation for this phenomenon. Analyzing the
properties of TADDA, we find that in order to achieve good scores, forecasters
often have an incentive to predict no or only modest log-changes. In
particular, there is often an incentive to report conservative point
predictions considerably closer to zero than the forecaster's actual predictive
median or mean. In an empirical application, we demonstrate that a no-change
model can be improved upon by tailoring predictions to the particularities of
the TADDA score. We conclude by outlining some alternative scoring concepts.
