FinEAS: Financial Embedding Analysis of Sentiment

• URL: http://arxiv.org/abs/2111.00526v1
• Date: Sun, 31 Oct 2021 15:41:56 GMT
• Title: FinEAS: Financial Embedding Analysis of Sentiment
• Authors: Asier Guti\'errez-Fandi\~no, Miquel Noguer i Alonso, Petter Kolm, Jordi Armengol-Estap\'e
• Abstract summary: We introduce a new language representation model in finance called Financial Embedding Analysis of Sentiment (FinEAS) In this work, we propose a new model for financial sentiment analysis based on supervised fine-tuned sentence embeddings from a standard BERT model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a new language representation model in finance called Financial Embedding Analysis of Sentiment (FinEAS). In financial markets, news and investor sentiment are significant drivers of security prices. Thus, leveraging the capabilities of modern NLP approaches for financial sentiment analysis is a crucial component in identifying patterns and trends that are useful for market participants and regulators. In recent years, methods that use transfer learning from large Transformer-based language models like BERT, have achieved state-of-the-art results in text classification tasks, including sentiment analysis using labelled datasets. Researchers have quickly adopted these approaches to financial texts, but best practices in this domain are not well-established. In this work, we propose a new model for financial sentiment analysis based on supervised fine-tuned sentence embeddings from a standard BERT model. We demonstrate our approach achieves significant improvements in comparison to vanilla BERT, LSTM, and FinBERT, a financial domain specific BERT.

Related papers

• Financial Sentiment Analysis on News and Reports Using Large Language Models and FinBERT [0.0]
  This paper investigates the application of large language models (LLMs) and FinBERT for financial sentiment analysis. The study emphasizes the advantages of prompt engineering with zero-shot and few-shot strategy to improve sentiment classification accuracy.
  arXiv  Detail & Related papers  (2024-10-02T19:48:17Z)
• Predicting Stock Prices with FinBERT-LSTM: Integrating News Sentiment Analysis [2.7921137693344384]
  We use deep learning networks, based on the history of stock prices and articles of financial, business, technical news that introduce market information to predict stock prices. We developed a pre-trained NLP model known as FinBERT, designed to discern the sentiments within financial texts. This model utilizes news categories related to the stock market structure hierarchy, namely market, industry, and stock related news categories, combined with the stock market's stock price situation in the previous week for prediction.
  arXiv  Detail & Related papers  (2024-07-23T03:26:07Z)
• Fine-Tuning Gemma-7B for Enhanced Sentiment Analysis of Financial News Headlines [4.198715347024138]
  We use Natural Language Processing (NLP) and Large Language Models (LLM) to analyze sentiment from the perspective of retail investors. We fine-tune several models, including distilbert-base-uncased, Llama, and gemma-7b, to evaluate their effectiveness in sentiment classification. Our experiments demonstrate that the fine-tuned gemma-7b model outperforms others, achieving the highest precision, recall, and F1 score.
  arXiv  Detail & Related papers  (2024-06-19T15:20:19Z)
• AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework [48.3060010653088]
  We release AlphaFin datasets, combining traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data. We then use AlphaFin datasets to benchmark a state-of-the-art method, called Stock-Chain, for effectively tackling the financial analysis task.
  arXiv  Detail & Related papers  (2024-03-19T09:45:33Z)
• FinBen: A Holistic Financial Benchmark for Large Language Models [75.09474986283394]
  FinBen is the first extensive open-source evaluation benchmark, including 36 datasets spanning 24 financial tasks. FinBen offers several key innovations: a broader range of tasks and datasets, the first evaluation of stock trading, novel agent and Retrieval-Augmented Generation (RAG) evaluation, and three novel open-source evaluation datasets for text summarization, question answering, and stock trading.
  arXiv  Detail & Related papers  (2024-02-20T02:16:16Z)
• Revolutionizing Finance with LLMs: An Overview of Applications and Insights [47.11391223936608]
  Large Language Models (LLMs) like ChatGPT have seen considerable advancements and have been applied in diverse fields. These models are being utilized for automating financial report generation, forecasting market trends, analyzing investor sentiment, and offering personalized financial advice.
  arXiv  Detail & Related papers  (2024-01-22T01:06:17Z)
• Transforming Sentiment Analysis in the Financial Domain with ChatGPT [0.07499722271664146]
  This study investigates the potential of large language models, particularly ChatGPT 3.5, in financial sentiment analysis. ChatGPT exhibited approximately 35% enhanced performance in sentiment classification and a 36% higher correlation with market returns. By underlining the significance of prompt engineering, particularly in zero-shot contexts, this study spotlights ChatGPT's potential to substantially boost sentiment analysis in financial applications.
  arXiv  Detail & Related papers  (2023-08-13T09:20:47Z)
• Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models [18.212210748797332]
  We introduce a simple yet effective instruction tuning approach to address these issues. In the experiment, our approach outperforms state-of-the-art supervised sentiment analysis models.
  arXiv  Detail & Related papers  (2023-06-22T03:56:38Z)
• PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance [63.51545277822702]
  PIXIU is a comprehensive framework including the first financial large language model (LLMs) based on fine-tuning LLaMA with instruction data. We propose FinMA by fine-tuning LLaMA with the constructed dataset to be able to follow instructions for various financial tasks. We conduct a detailed analysis of FinMA and several existing LLMs, uncovering their strengths and weaknesses in handling critical financial tasks.
  arXiv  Detail & Related papers  (2023-06-08T14:20:29Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines. We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• Gaussian process imputation of multiple financial series [71.08576457371433]
  Multiple time series such as financial indicators, stock prices and exchange rates are strongly coupled due to their dependence on the latent state of the market. We focus on learning the relationships among financial time series by modelling them through a multi-output Gaussian process.
  arXiv  Detail & Related papers  (2020-02-11T19:18:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.