Annual Securities Reports (ASRs) prepared by Japanese companies are textual documents that investigate and report on their financial condition and potential risks. These documents include management policies, risks specific to companies or industries, and materials for management decisions, serving as critical resources for evaluating investment opportunities and financial health. By employing a clustering-based approach, we aim to statistically identify commonalities and differences among companies and examine the relationships between risk-related topics and various management indicators. Our analysis utilizes Fin-BERT, a domain-specific language model, and compares it with a general BERT to assess their ability to capture nuanced patterns in corporate risk descriptions. The results demonstrate that Fin-BERT effectively captures granular semantic information, particularly in financial contexts, which standard models might overlook. We observed a modest correlation between shifts in corporate risk descriptions and key management metrics, such as stock price fluctuations, indicating that risk narratives influence investor perceptions and pricing behaviors. These findings suggest opportunities to improve the integration of domain-specific language models into financial analysis and provide a foundation for future research to explore industry-specific variations and refine the methodologies for analyzing corporate risk descriptions.