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In this paper, we automatically create sentiment dictionaries for predictingfinancial outcomes. We compare three approaches: (I) manual adaptation of thedomain-general dictionary H4N, (ii) automatic adaptation of H4N and (iii) acombination consisting of first manual, then automatic adaptation. In ourexperiments, we demonstrate that the automatically adapted sentiment dictionaryoutperforms the previous state of the art in predicting the financial outcomesexcess return and volatility. In particular, automatic adaptation performsbetter than manual adaptation. In our analysis, we find that annotation basedon 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 targetdomain instead.

Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes