## 4 Discussion
### Limitations and Future Research Directions  
The quantitative experiments reported here highlight the potential for NLP [4; 26] and cognitive networks [11; 15; 19; 25] to complement and enhance a supervisor's view of both their firm and the sector within which it operates. Identifying emerging themes and understanding how an insurer is managing these is a core component of supervision. Understanding negative changes in market perceptions could highlight the need for more proactive supervision to understand the underlying drivers. That said, the automated analyses reported here come with some key limitations. Three noticeable shortcomings are: (1) the lexicon to categorise words is not insurance specific but rather relative to finance in general [28]; (2) the documents analysed are limited and stylised to meet the needs of a specific audience (in contrast the PRA receives a vast amount of additional information [2], including risk reports, that potentially contain a much richer risk-related source of intel--applying the same methods explained here to such documents might reveal more patterns); and (3) the use of share price as a proxy for prudential risk should be replaced by the PRA's internal view--noting that share price movements may not always be aligned to prudential risk [1; 3]. In presence of better lexicons and more extensive documents, the techniques reported here could be better developed to identify the ways of associating ideas between differential investor day transcripts. Specifically, as indicated by the network analysis of \"climate change\", emotionally structured networks, representing associations between ideas, might be automatically extracted from reports as summaries of the content and sentiment of text towards specific aspects, e.g., \"risk\" or \"investment\". Within this regard, we can discuss the recent methodology described by [PERSON] and colleagues [5]. The authors used cognitive networks enriched by lexicon-based sentiment analysis to quantify the specific sentiment surrounding \"Pfizer\" and \"Astrazeneca\" across 5000 Italian news articles. The authors quantified that the perception of both these firms was drastically negative, even though in the same corpus overall concept of \"vaccine\" was portrayed with overwhelmingly positive sentiment. Future research adopting word-level sentiment patterns, e.g., the sentiment attributed to \"risk\" or \"investment\" across time-ordered reports, might provide correlations stronger than the one found in this study, leading to more fine-grained studies relating to textual information and its ability to predict market volatility or share prices. To conclude, our work found a correlation between the sentiment of investor day transcripts and share price movements. Furthermore, the NLP pipelines implemented here managed to highlight several events of relevance for the insurance sector by analysing over 800 reports in a few seconds, a task that would have taken way longer for a human coder. Our results thus indicate that natural language processing represents a crucial direction for developing next-generation systems of firm analysis in the insurance sector. When text analysis is adopted more broadly in the insurance market, also on the onset of the promising exploratory results reported here, it will be important to monitor and assess any changes in the insurer or market analyst behaviour [22; 32]. For example, will insurers try to game the system by using highly positive words, even when delivering bad news? Such concerns feel somewhat premature, however, as such analysis becomes widespread, being mindful of these possibilities will be increasingly important. Conceptualization, S.C. and M.S. ; methodology, S.C. and M.S. ; software, S.C.; validation, S.C. and M.S. ; formal analysis, S.C.; investigation, S.C. and M.S. ; data curation, S.C.; writing--original draft preparation, S.C. and M.S. ; writing--review and editing, S.C. and M.S. ; visualization, S.C.; supervision, M.S. All authors have read and agreed to the published version of the manuscript.