Summary Transforming Sentiment Analysis in Financial Services arxiv.org
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The study examines ChatGPT's effectiveness in sentiment analysis for the foreign exchange market using a dataset of forex news headlines.
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Key Points
- ChatGPT, a large language model, is explored for sentiment analysis in the financial domain, specifically in the foreign exchange market.
- The study addresses limitations and challenges in financial sentiment analysis and highlights the potential of large language models like ChatGPT 3.5.
- Empirical studies have shown that changes in investor sentiment can significantly impact asset prices and trading volume.
- The researchers collected a curated dataset of forex-related news headlines to evaluate ChatGPT's performance in financial sentiment analysis.
- Various sentiment analysis models, including GPT-P3 and FinBERT, were evaluated in the study, with GPT-P1N achieving the highest directional accuracy.
- The ChatGPT model consistently outperforms the FinBERT model in financial sentiment analysis.
- The study also references other research works related to sentiment analysis, language models, and financial services.
Summaries
32 word summary
This study evaluates the use of ChatGPT for sentiment analysis in the financial domain, focusing on the foreign exchange market. Researchers collected a dataset of forex news headlines to assess ChatGPT's performance.
39 word summary
This study examines the use of ChatGPT, a large language model, for sentiment analysis in the financial domain, specifically in the foreign exchange market. The researchers collected a dataset of forex-related news headlines and evaluated ChatGPT's performance. They address
497 word summary
This study explores the potential of ChatGPT, a large language model, for sentiment analysis in the financial domain, with a focus on the foreign exchange market. The researchers utilize a curated dataset of forex-related news headlines and evaluate ChatGPT's performance
Addressing limitations and challenges in financial sentiment analysis represents an opportunity for advancements. This study explores the potential of large language models, specifically ChatGPT 3.5, in financial sentiment analysis, focusing on the foreign exchange market. A zero-shot prompting
Empirical studies have shown that changes in investor sentiment can significantly impact asset prices and trading volume. Initially, sentiment analysis in finance relied on manually curated financial lexicons, but this approach was often too simplistic. ML techniques, such as Naive Bayes
The study aims to explore the application and performance of ChatGPT in financial sentiment analysis, specifically within the context of the forex market. The researchers collected news headlines relevant to key forex pairs from reputable platforms over a period of 86 days. They manually
The most common tokens in the headlines categorized as positive and negative sentiment are displayed in figures 6 and 7, after removing stopwords, forex pairs, and central banks' names. Table 2 provides additional quantitative details on token distribution in the dataset.
We conducted experiments to evaluate ChatGPT's capabilities in financial sentiment analysis using a zero-shot prompting approach. This approach allowed us to assess ChatGPT's inherent abilities without any task-specific fine-tuning. We used various prompts, ranging from emulating
The evaluation of sentiment analysis models in financial services involved multiple steps. One step was assigning integer codes to predicted sentiment classes for market-related model evaluation. Another step involved setting parameters for the different models, such as the max tokens parameter and temperature parameter. The
GPT-P3, a sentiment analysis model trained on financial news headlines, performs relatively well but doesn't match the performance of other GPT models that use forex pair information in their prompts. GPT-P1, designed to perform emotion analysis on headlines
FinBERT's performance declined when the forex pair was not mentioned directly, but GPT-P1, P2, and P3 still outperformed FinBERT in handling complex scenarios. GPT-P2 maintained high scores in sentiment classification. GPT
The study evaluates the performance of different sentiment analysis models in the financial services sector. The highest directional accuracy (DA) is achieved by GPT-P1N, indicating its ability to predict market movement based on sentiment scores. Numerical models generally have higher
The ChatGPT model consistently outperforms the FinBERT model in financial sentiment analysis, regardless of the specific prompt used. Prompts GPT-P4, P6, and P6N showed considerable performance in sentiment analysis, providing potentially valuable insights
Blaskowitz and Herwartz (2011) discuss the economic evaluation of directional forecasts. Bollen, Mao, and Zeng (2011) explore the relationship between Twitter mood and stock market prediction. Brown et al. (2020)
This excerpt includes a list of references and citations from various sources related to sentiment analysis, language models, and financial services. The sources mentioned cover topics such as textual analysis, deep contextualized word representations, affective computing, opinion mining, language understanding,