## 1 Introduction  
The financial sector, known for its dynamic nature, is greatly impacted by the continuous flow of news and information that changes market dynamics and investor views [1]. Every day, a large number of articles and microblogs containing detailed financial information about traded corporations are distributed online. This generates a substantial quantity of disorganized written information, posing a significant difficulty for companies, investors, and analysts involved in data analysis. The conventional approaches of manual data analysis are time consuming and increasingly unfeasible due to the vast amount of data [2]. Automated sentiment analysis, a popular natural language processing (NLP) task, is a viable solution that enables the automatic identification of emotional tones in texts, thereby offering valuable insights into investor behavior and market trends [3, 4]. The decision-making processes of investors and market stakeholders are significantly impacted by the sentiments conveyed through financial news [5]. For example, positive news regarding a company's performance may increase investor confidence and increase stock prices, whereas negative news may incite dread and trigger selloffs. Sentiment analysis in this field utilizes several strategies, such as lexicon-based approaches, conventional machine learning techniques, advanced deep learning models, and transformer-based architectures [6]. Large language models (LLMs) like ChatGPT and Gemini have the potential to greatly impact sentiment analysis, as they can effectively work in situations where there are little or no training data available without requiring substantial manual annotation effort for supervised training [7]. Rigorously evaluating their performance in sentiment analysis for specific domains, such as the financial sector, is essential to assess their suitability for integration into domain-specific tools, including financial reporting software. Sentiment analysis can be performed at four different levels of detail: document, sentence, target, and aspect level. The document level evaluates the overall sentiment of the entire text, while the sentence level analyzes sentiment in individual sentences. The target level assesses sentiment toward specific entities or categories, referred to as targets within the text [8; 9; 10], and the aspect level focuses on sentiment toward different features or attributes of an entity. Prior research on financial sentiment analysis has predominantly focused on the document and sentence levels [11; 12; 13]. Nevertheless, target-based analysis is essential for accurately interpreting and responding to the complex sentiments of financial news, as these documents frequently contain multiple sentiments associated with distinct entities [4]. For example, a headline such as \"Netflix shares dip despite Apple's surge in streaming subscribers\" illustrates divergent sentiments associated with specific companies, Netflix and Apple, within a single sentence, emphasizing the necessity of nuanced, target-based sentiment analysis. Despite the financial sector's significant relevance in target-level sentiment analysis, this domain is primarily underexplored with limited exceptions such as implementations in economic and political news [14] and several commercial products [15]. Additionally, the considerable interest in the integration of LLMs in various disciplines has prompted extensive investigation into their potential applications in a wide range of domains [16; 17]. However, their deployment within the stock market, particularly in investigating their effectiveness in conducting target-level financial sentiment analysis in zero-shot learning scenarios, has not yet been investigated. Furthermore, there is a significant lack of comparative performance analyses between traditional models and LLMs for financial sentiment analysis tasks.