  Extraction of sentiment signals from news text, stock message boards, and
business reports, for stock movement prediction, has been a rising field of
interest in finance. Building upon past literature, the most recent works
attempt to better capture sentiment from sentences with complex syntactic
structures by introducing aspect-level sentiment classification (ASC). Despite
the growing interest, however, fine-grained sentiment analysis has not been
fully explored in non-English literature due to the shortage of annotated
finance-specific data. Accordingly, it is necessary for non-English languages
to leverage datasets and pre-trained language models (PLM) of different
domains, languages, and tasks to best their performance. To facilitate
finance-specific ASC research in the Korean language, we build KorFinASC, a
Korean aspect-level sentiment classification dataset for finance consisting of
12,613 human-annotated samples, and explore methods of intermediate transfer
learning. Our experiments indicate that past research has been ignorant towards
the potentially wrong knowledge of financial entities encoded during the
training phase, which has overestimated the predictive power of PLMs. In our
work, we use the term "non-stationary knowledge'' to refer to information that
was previously correct but is likely to change, and present "TGT-Masking'', a
novel masking pattern to restrict PLMs from speculating knowledge of the kind.
Finally, through a series of transfer learning with TGT-Masking applied we
improve 22.63% of classification accuracy compared to standalone models on
KorFinASC.
