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Systematic Multi-Aspect Evaluation of Time Series-Based Report Generation: The Case of Financial Analysis from Stock Data

This paper explores the capability of large language models (LLMs) to generate coherent textual reports from time series data, using financial reports from stock data as the use case. We conduct a comprehensive multi-aspect evaluation across four model families, including linguistic quality, content source attribution, automated metrics, and expert human assessment. We evaluate models using four major stock indices and two synthetic time series to assess generalization. We assess reports based on single and multiple time series data, and experiment with plain text and multi-modal prompting. We examine temporal effects by analyzing report quality as data approaches model knowledge cutoffs and testing synthetic future intervals. Our evaluation shows that LLMs are capable of creating high-quality financial analyst reports, with larger models demonstrating superior performance, however even those require human oversight and have potential for temporal logic errors. Our findings reveal model-specific behavioral patterns that enable tailored generation pipelines and inform future research about model pitfalls in time series-to-text generation tasks.