# FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications arXiv: https://arxiv.org/abs/2403.12285 ยท HTML: https://mlsanity.com/paper/2403.12285/ ## Key facts - Cited by 23 papers in the map (data through 14 Oct 2026). - Builds on 9 of its 24 references that are in the map. - First posted on arXiv 18 Mar 2024. ## Builds on (most cited first, 9 of 9) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [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) - [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017) - [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023) - [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019) - [FinGPT: Open-Source Financial Large Language Models](https://mlsanity.com/paper/2306.06031/) (2023) - [FinGPT: Democratizing Internet-scale Data for Financial Large Language Models](https://mlsanity.com/paper/2307.10485/) (2023) - [Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models](https://mlsanity.com/paper/2306.12659/) (2023) ## Built on by (most cited first, 23 of 23) - [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) - [Large Language Model Agent in Financial Trading: A Survey](https://mlsanity.com/paper/2408.06361/) (2024) - [Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2502.10708/) (2025) - [Agentic AI: A Comprehensive Survey of Architectures, Applications, and Future Directions](https://mlsanity.com/paper/2510.25445/) (2025) - [FAMMA: A Benchmark for Financial Domain Multilingual Multimodal Question Answering](https://mlsanity.com/paper/2410.04526/) (2024) - [From Deep Learning to LLMs: A survey of AI in Quantitative Investment](https://mlsanity.com/paper/2503.21422/) (2025) - [TradingGroup: A Multi-Agent Trading System with Self-Reflection and Data-Synthesis](https://mlsanity.com/paper/2508.17565/) (2025) - [SoK: Security of Autonomous LLM Agents in Agentic Commerce](https://mlsanity.com/paper/2604.15367/) (2026) - [Transport and Merge: Cross-Architecture Merging for Large Language Models](https://mlsanity.com/paper/2602.05495/) (2026) - [Reasoning or Overthinking: Evaluating Large Language Models on Financial Sentiment Analysis](https://mlsanity.com/paper/2506.04574/) (2025) - [RedOne: Revealing Domain-specific LLM Post-Training in Social Networking Services](https://mlsanity.com/paper/2507.10605/) (2025) - [Learning to Conceal Risk: Controllable Multi-turn Red Teaming for LLMs in the Financial Domain](https://mlsanity.com/paper/2509.10546/) (2025) - [Fin-Ally: Pioneering the Development of an Advanced, Commonsense-Embedded Conversational AI for Money Matters](https://mlsanity.com/paper/2509.24342/) (2025) - [AlphaCrafter: Harnessing Multi-Agent Workflows for Cross-Sectional Quantitative Trading](https://mlsanity.com/paper/2605.05580/) (2026) - [From Efficiency to Leakage -- Privacy Backdoor in Federated Language Model Fine-Tuning](https://mlsanity.com/paper/2606.20553/) (2026) - [SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis](https://mlsanity.com/paper/2412.19140/) (2024) - [A Bayesian Hybrid Parameter-Efficient Fine-Tuning Method for Large Language Models](https://mlsanity.com/paper/2508.02711/) (2025) - [News-Aware Direct Reinforcement Trading for Financial Markets](https://mlsanity.com/paper/2510.19173/) (2025) - [TextReasoningBench: Does Reasoning Really Improve Text Classification in Large Language Models?](https://mlsanity.com/paper/2603.19558/) (2026) - [Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems](https://mlsanity.com/paper/2606.08285/) (2026) - [RA-FinBERT: Rule-aware LoRA adaptation for low-resource financial sentiment classification](https://mlsanity.com/paper/2608.09834/) (2026) - [MM-FinEval: A Multi-Task Multimodal Benchmark for Real-World Financial Forecasting](https://mlsanity.com/paper/2609.38523/) (2026) ## Abstract (arXiv) There are multiple sources of financial news online which influence market movements and trader's decisions. This highlights the need for accurate sentiment analysis, in addition to having appropriate algorithmic trading techniques, to arrive at better informed trading decisions. Standard lexicon based sentiment approaches have demonstrated their power in aiding financial decisions. However, they are known to suffer from issues related to context sensitivity and word ordering. Large Language Models (LLMs) can also be used in this context, but they are not finance-specific and tend to require significant computational resources. To facilitate a finance specific LLM framework, we introduce a novel approach based on the Llama 2 7B foundational model, in order to benefit from its generative nature and comprehensive language manipulation. This is achieved by fine-tuning the Llama2 7B model on a small portion of supervised financial sentiment analysis data, so as to jointly handle the complexities of financial lexicon and context, and further equipping it with a neural network based decision mechanism. Such a generator-classifier scheme, referred to as FinLlama, is trained not only to classify the sentiment valence but also quantify its strength, thus offering traders a nuanced insight into financial news articles. Complementing this, the implementation of parameter-efficient fine-tuning through LoRA optimises trainable parameters, thus minimising computational and memory requirements, without sacrificing accuracy. Simulation results demonstrate the ability of the proposed FinLlama to provide a framework for enhanced portfolio management decisions and increased market returns. These results underpin the ability of FinLlama to construct high-return portfolios which exhibit enhanced resilience, even during volatile periods and unpredictable market events.