  In this paper, we extend financial sentiment analysis~(FSA) to event-level
since events usually serve as the subject of the sentiment in financial text.
Though extracting events from the financial text may be conducive to accurate
sentiment predictions, it has specialized challenges due to the lengthy and
discontinuity of events in a financial text. To this end, we reconceptualize
the event extraction as a classification task by designing a categorization
comprising coarse-grained and fine-grained event categories. Under this
setting, we formulate the \textbf{E}vent-Level \textbf{F}inancial
\textbf{S}entiment \textbf{A}nalysis~(\textbf{EFSA} for short) task that
outputs quintuples consisting of (company, industry, coarse-grained event,
fine-grained event, sentiment) from financial text. A large-scale Chinese
dataset containing $12,160$ news articles and $13,725$ quintuples is publicized
as a brand new testbed for our task. A four-hop Chain-of-Thought LLM-based
approach is devised for this task. Systematically investigations are conducted
on our dataset, and the empirical results demonstrate the benchmarking scores
of existing methods and our proposed method can reach the current
state-of-the-art. Our dataset and framework implementation are available at
https://anonymous.4open.science/r/EFSA-645E
