# Financial News Analytics Using Fine-Tuned Llama 2 GPT Model

arXiv: https://arxiv.org/abs/2308.13032 · HTML: https://mlsanity.com/paper/2308.13032/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 24 Aug 2023.

## Builds on (most cited first, 7 of 7)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [FinGPT: Open-Source Financial Large Language Models](https://mlsanity.com/paper/2306.06031/) (2023)
- [Methods of Informational Trends Analytics and Fake News Detection on Twitter](https://mlsanity.com/paper/2204.04891/) (2022)
- [Forming Predictive Features of Tweets for Decision-Making Support](https://mlsanity.com/paper/2201.02049/) (2022)
- [Sales Time Series Analytics Using Deep Q-Learning](https://mlsanity.com/paper/2201.02058/) (2022)
- [Analytics of Business Time Series Using Machine Learning and Bayesian Inference](https://mlsanity.com/paper/2205.12905/) (2022)

## Built on by (most cited first, 6 of 6)

- [A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges](https://mlsanity.com/paper/2406.11903/) (2024)
- [A Survey on LoRA of Large Language Models](https://mlsanity.com/paper/2407.11046/) (2024)
- [Analysis of Disinformation and Fake News Detection Using Fine-Tuned Large Language Model](https://mlsanity.com/paper/2309.04704/) (2023)
- [Evolution of meta's llama models and parameter-efficient fine-tuning of large language models: a survey](https://mlsanity.com/paper/2510.12178/) (2025)
- [Multilevel Analysis of Cryptocurrency News using RAG Approach with Fine-Tuned Mistral Large Language Model](https://mlsanity.com/paper/2509.03527/) (2025)
- [Using GPT Models for Qualitative and Quantitative News Analytics in the 2024 US Presidental Election Process](https://mlsanity.com/paper/2410.15884/) (2024)

## Abstract (arXiv)

The paper considers the possibility to fine-tune Llama 2 GPT large language model (LLM) for the multitask analysis of financial news. For fine-tuning, the PEFT/LoRA based approach was used. In the study, the model was fine-tuned for the following tasks: analysing a text from financial market perspectives, highlighting main points of a text, summarizing a text and extracting named entities with appropriate sentiments. The obtained results show that the fine-tuned Llama 2 model can perform a multitask financial news analysis with a specified structure of response, part of response can be a structured text and another part of data can have JSON format for further processing. Extracted sentiments for named entities can be considered as predictive features in supervised machine learning models with quantitative target variables.