mlsanityData to 14 Oct 2026

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning

arXiv 2412.09859 ↗First posted 13 Dec 2024cs.LG cs.CLcited by 1

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Abstract (arXiv)

The Efficient Market Hypothesis (EMH) highlights the essence of financial news in stock price movement. Financial news comes in the form of corporate announcements, news titles, and other forms of digital text. The generation of insights from financial news can be done with sentiment analysis. General-purpose language models are too general for sentiment analysis in finance. Curated labeled data for fine-tuning general-purpose language models are scare, and existing fine-tuned models for sentiment analysis in finance do not capture the maximum context width. We hypothesize that using actual and synthetic data can improve performance. We introduce BertNSP-finance to concatenate shorter financial sentences into longer financial sentences, and finbert-lc to determine sentiment from digital text. The results show improved performance on the accuracy and the f1 score for the financial phrasebank data with 50% and 100% agreement levels.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 32 references that are in the map. First posted on arXiv 13 Dec 2024.

Builds on

7 of 32 references.

References · 7

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  2. 2023BloombergGPT: A Large Language Model for Financecited by 939
  3. 2019FinBERT: Financial Sentiment Analysis with Pre-trained Language Modelscited by 385
  4. 2023Does Synthetic Data Generation of LLMs Help Clinical Text Mining?cited by 154
  5. 2023AugGPT: Leveraging ChatGPT for Text Data Augmentationcited by 130
  6. 2023FinGPT: Democratizing Internet-scale Data for Financial Large Language Modelscited by 67
  7. 2023Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Modelscited by 58

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025A three-step machine learning approach to predict market bubbles with financial newscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models
  2. 2024FANAL -- Financial Activity News Alerting Language Modeling Framework
  3. 2025Reasoning or Overthinking: Evaluating Large Language Models on Financial Sentiment Analysis
  4. 2025FinBERT2: A Specialized Bidirectional Encoder for Bridging the Gap in Finance-Specific Deployment of Large Language Models
  5. 2024Enhancing TinyBERT for Financial Sentiment Analysis Using GPT-Augmented FinBERT Distillation
  6. 2024FinBen: A Holistic Financial Benchmark for Large Language Models
  7. 2024Aligning LLMs with Human Instructions and Stock Market Feedback in Financial Sentiment Analysis
  8. 2025FinDPO: Financial Sentiment Analysis for Algorithmic Trading through Preference Optimization of LLMs