Automatic Domain Adaptation Outperforms Manual Domain Adaptation
for Predicting Financial Outcomes
Abstract
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