  In this paper, we automatically create sentiment dictionaries for predicting
financial outcomes. We compare three approaches: (I) manual adaptation of the
domain-general dictionary H4N, (ii) automatic adaptation of H4N and (iii) a
combination consisting of first manual, then automatic adaptation. In our
experiments, we demonstrate that the automatically adapted sentiment dictionary
outperforms the previous state of the art in predicting the financial outcomes
excess return and volatility. In particular, automatic adaptation performs
better than manual adaptation. In our analysis, we find that annotation based
on an expert's a priori belief about a word's meaning can be incorrect --
annotation should be performed based on the word's contexts in the target
domain instead.
