This paper proposes an LLM-as-a-Judge evaluation framework of stock price fluctuation articles automatically generated based on financial news, corporate disclosures, and stock price fluctuation data. This automatic article generation framework emulates the workflow of human financial journalists by analyzing recent stock price fluctuations and incorporating relevant causal factors extracted from textual and numerical information. In particular, the generation process utilizes news articles and numerical stock price data, including price fluctuation ranges over the past three days. Based on those automatically generated stock price fluctuation articles, this study places particular emphasis on the LLM-as-a-Judge evaluation methodology. We conduct an item wise human evaluation and compare it with the LLM-as-a-Judge automatic metric. We analyze the correlation among these evaluation methods to assess their reliability. Furthermore, through comparisons between zero-shot and few-shot prompting, we examine the effectiveness of the proposed framework and the validity of LLM based evaluation for assessing factual and causal consistency in financial text generation.

Evaluating causal mechanism explanations in generated financial news is challenging because stock price movements may have multiple plausible explanations without a unique gold explanation. We propose a reference-free LLM-as-a-Judge framework that evaluates explanations from three perspectives: Relevance, Completeness, and Economic Plausibility. We construct 200 articles from Chinese stock market data and compare Gemini 3.5 Flash, GPT-5.5, and Claude Opus 5 with human annotations under zero-shot and few-shot settings. Before aggregating the evaluation results, we applied a systematic matched-subset procedure to examine potential self-preference and generation-source bias. Based on the data examined in this study, these analyses found no statistically significant evidence of systematic bias, supporting the aggregation of all 200 articles for the overall agreement analysis. The results show moderate to high agreement with human evaluation, with Completeness generally showing higher agreement than Relevance. Few-shot prompting does not consistently improve agreement and generally leads to more conservative judgments. These findings demonstrate the potential of LLM-as-a-Judge for reference-free evaluation of causal mechanism explanations in financial news.