Summary of Finllama: Financial Sentiment Classification For Algorithmic Trading Applications, by Thanos Konstantinidis et al.
FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications
by Thanos Konstantinidis, Giorgos Iacovides, Mingxue Xu, Tony G. Constantinides, Danilo Mandic
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Statistical Finance (q-fin.ST); Trading and Market Microstructure (q-fin.TR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces FinLlama, a novel approach to financial sentiment analysis using Large Language Models (LLMs). The authors highlight the need for accurate sentiment analysis in finance, as standard lexicon-based approaches can be context-sensitive and word-ordering dependent. They propose fine-tuning the Llama 2 7B model on a small supervised dataset to jointly handle financial lexicon and context complexities. This generator-classifier scheme, FinLlama, is trained to classify sentiment valence and quantify its strength, offering traders nuanced insights into financial news articles. The authors also implement parameter-efficient fine-tuning using LoRA optimisation, minimizing computational and memory requirements without sacrificing accuracy. Simulation results demonstrate the ability of FinLlama to provide enhanced portfolio management decisions and increased market returns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making better trading decisions by analyzing financial news online. It’s like having a super smart assistant that can understand what people are saying in articles and tell you how they feel about the markets. The authors use big language models, which are really good at understanding human language, but they need to be trained on financial data to make them useful for trading. They create a new model called FinLlama that’s specifically designed for finance and can analyze news articles to give traders a better idea of what’s happening in the markets. |
Keywords
* Artificial intelligence * Fine tuning * Llama * Lora * Parameter efficient * Supervised
