This study evaluates the performance of local large language models (LLMs) in interpreting financial texts, in comparison to closed-source, cloud-based models. Our study is comprised of two main exercises. The first exercise benchmarks local LLM performance in analyzing financial and economic texts. Through this exercise, we introduce new benchmarking tasks for assessing LLM performance and explore the refinements needed to improve local LLM performance. Benchmarking results suggest that local LLMs are viable as a tool for general NLP analysis of financial and economic texts. In the second exercise, we leverage local LLMs to analyze the tone and substance of bank earnings calls in the post pandemic era, including calls conducted during the banking stress of early 2023. Using local LLMs, we analyze remarks in bank earnings calls in terms of topics discussed, overall sentiment, temporal orientation, and vagueness. In response to the banking stress of early 2023, bank calls tended to converge to a similar set of topics and conveyed a distinctly less positive sentiment.