FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications

TL;DR

FinLlama is a finance-specific Llama 2-based model fine-tuned for nuanced financial sentiment analysis, aiding algorithmic trading by providing detailed sentiment insights with efficient training, leading to improved portfolio performance.

Contribution

This paper introduces FinLlama, a novel fine-tuned Llama 2 model with a neural decision mechanism for financial sentiment analysis, optimized with LoRA for efficiency and enhanced trading decisions.

Findings

FinLlama accurately classifies financial sentiment and quantifies its strength.

It improves portfolio returns and resilience during volatile market periods.

The approach reduces computational costs while maintaining high accuracy.

Abstract

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.