  Using a corpus of 17,000+ financial news reports (involving over 10M words),
we perform an analysis of the argument-distributions of the UP and DOWN verbs
used to describe movements of indices, stocks and shares. In Study 1
participants identified antonyms of these verbs in a free-response task and a
matching task from which the most commonly identified antonyms were compiled.
In Study 2, we determined whether the argument-distributions for the verbs in
these antonym-pairs were sufficiently similar to predict the most
frequently-identified antonym. Cosine similarity correlates moderately with the
proportions of antonym-pairs identified by people (r = 0.31). More
impressively, 87% of the time the most frequently-identified antonym is either
the first- or second-most similar pair in the set of alternatives. The
implications of these results for distributional approaches to determining
metaphoric knowledge are discussed.
