FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications
FinLlama fine-tunes Llama 2 model for financial sentiment analysis, enhancing trading decisions with minimal computational resources.
What it examines
The paper introduces FinLlama, a fine-tuned Llama 2 7B model for financial sentiment analysis aimed at enhancing algorithmic trading. It addresses the need for context-aware sentiment extraction in finance, leveraging parameter-efficient fine-tuning and 8-bit quantization to minimize computational resources.
What it concludes
The research concludes that FinLlama significantly enhances financial sentiment analysis and portfolio management. Potential applications include improved trading strategies and risk management. Future work aims to further refine sentiment classification accuracy and model efficiency.
Evidence objects
The research concludes that FinLlama significantly enhances financial sentiment analysis and portfolio management. Potential applications include improved trading strategies and risk management. Future work aims to further refine sentiment classification accuracy and model efficiency.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
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… ▽ More 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. △ Less
Source row: 847 · abstract type: unknown