  Researchers and financial professionals require robust computerized tools
that allow users to rapidly operationalize and assess the semantic textual
content in financial news. However, existing methods commonly work at the
document-level while deeper insights into the actual structure and the
sentiment of individual sentences remain blurred. As a result, investors are
required to apply the utmost attention and detailed, domain-specific knowledge
in order to assess the information on a fine-grained basis. To facilitate this
manual process, this paper proposes the use of distributed text representations
and multi-instance learning to transfer information from the document-level to
the sentence-level. Compared to alternative approaches, this method features
superior predictive performance while preserving context and interpretability.
Our analysis of a manually-labeled dataset yields a predictive accuracy of up
to 69.90%, exceeding the performance of alternative approaches by at least 3.80
percentage points. Accordingly, this study not only benefits investors with
regard to their financial decision-making, but also helps companies to
communicate their messages as intended.
