## 5 Conclusions  
This study has performed a thorough assessment of target-level financial sentiment analysis (TLPSA), specifically examining the efficacy of conventional sentiment analysis techniques, such as VADER, DistilFinRoBERTa, and DeBERTa-v3-base-ABSA-v1.1, in comparison to advanced LLMs like ChatGPT and Gemini, within the domain of the stock market. The study utilized a zero-shot prompting strategy to investigate the ability of LLMs to identify target-specific sentiments. This was accomplished by utilizing a novel, rigorously constructed dataset of annotated stock-related news headlines. The dataset is made available to the public to promote further research and innovation (due to copyright restrictions, only the URLs of Bloomberg headlines with the manual annotations are provided; however, these URLs can be used with a Bloomberg terminal to reconstruct the complete dataset). VADER and DistilFinRoBERTa exhibit reduced effectiveness for TLFSA, lacking the specialized capabilities to accurately detect and evaluate the sentiment toward specific targets within the text. Hence, their performance in sentiment analysis at the target level is lower than models specifically developed for this task, such as DeBERTa-v3-base-ABSA-v1.1. Indeed, the fine-tuned version of the DeBERTa-v3-base-ABSA-v1.1 model exhibits exceptional performance, attaining the top scores in all evaluation metrics. LLMs also achieve very competitive performance, with ChatGPT-4, ChatGPT-4.0, and Gemini 1.5 Pro demonstrating impressive abilities. These models obtained almost ideal outcomes without requiring any training at all, emphasizing their potential for direct practical use in TLFSA. The findings obtained from this study are relevant for investors, financial analysts, and fintech companies, illustrating how both conventional approaches and advanced LLMs can improve decision-making processes. The existing performance leaderboard offers beneficial information for choosing models depending on individual performance requirements and the availability of resources. This study not only expands our understanding of advanced sentiment analysis tools but also lays the foundation for future advancements that could improve the accuracy of financial sentiment analysis, facilitating more strategic and informed decision making in financial markets. Although the study has shown encouraging results, it also acknowledges limitations, specifically the limited range of data used, which could impact the applicability of our findings to a broader context. Future studies should (i) expand the dataset to include a broader range of financial documents and extend the data collection timeframe to better capture the diversity of market dynamics and (ii) incorporate additional existing datasets to evaluate the generalizability of the findings. Furthermore, it will be essential to evaluate the potential of other LLMs for financial sentiment analysis as generative AI technologies continue to evolve, including models such as BLOOM, LLaMA, and competitive small-scale models (e.g., ORCA, OLMO). A thorough assessment of their few-shot performance is also recommended to further corroborate our preliminary observations on this aspect. Conceptualization, I.M. and M.R. ; Data curation, I.M. and M.R. ; Methodology, M.R. ; Resources, I.M. and M.R. ; Software, I.M. ; Validation, I.M. and M.R. ; Writing--original draft, I.M. ; Writing--review and editing, M.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The dataset developed with this research is available at [[https://github.com/iftikharm895/Target-Level_Financial_Sentiment_Analysis--accessed](https://github.com/iftikharm895/Target-Level_Financial_Sentiment_Analysis--accessed)]([https://github.com/iftikharm895/Target-Level_Financial_Sentiment_Analysis--accessed](https://github.com/iftikharm895/Target-Level_Financial_Sentiment_Analysis--accessed)) on 23 December 2024.