Large language models (LLMs) have proven highly effective for a wide range of tasks, including code generation. Recently, advancements in their capabilities have shown promise in areas like mathematical reasoning, chain-of-thought processes and self-reflection. However, their effectiveness in domains requiring nuanced understanding of financial contexts, such as accounting, remains unclear. In this study, we evaluate how well LLMs perform in generating code for domain-specific languages (DSLs) in accounting, using Beancount as a case study. We create a set of tasks based on common financial ratios, to evaluate the numeracy and financial literacy of LLMs. Our findings reveal that while LLMs are state-of-the art in generative tasks, they struggle severely with accounting, often producing inaccurate calculations and misinterpreting financial scenarios. We characterize these shortcomings through a comprehensive evaluation, shedding light on the limitations of LLMs in understanding and handling money-related tasks.