  Large language models (LLMs) have drastically changed the possible ways to
design intelligent systems, shifting the focuses from massive data acquisition
and new modeling training to human alignment and strategical elicitation of the
full potential of existing pre-trained models. This paradigm shift, however, is
not fully realized in financial sentiment analysis (FSA), due to the
discriminative nature of this task and a lack of prescriptive knowledge of how
to leverage generative models in such a context. This study investigates the
effectiveness of the new paradigm, i.e., using LLMs without fine-tuning for
FSA. Rooted in Minsky's theory of mind and emotions, a design framework with
heterogeneous LLM agents is proposed. The framework instantiates specialized
agents using prior domain knowledge of the types of FSA errors and reasons on
the aggregated agent discussions. Comprehensive evaluation on FSA datasets show
that the framework yields better accuracies, especially when the discussions
are substantial. This study contributes to the design foundations and paves new
avenues for LLMs-based FSA. Implications on business and management are also
discussed.
