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.
Abstract (translated)
网上有很多金融新闻来源，它们会影响市场运动和交易者的决策。这表明，除了适当的算法交易策略外，还需要进行准确的 sentiment 分析，才能做出更好的交易决策。基于标准的 lexicon 的 sentiment 方法已经在帮助金融决策方面展现出其力量。然而，它们已知存在关于上下文敏感性和词序的问题。在這種情況下，也可以使用大型语言模型（LLMs）。但是，它们不是专门针对金融领域的，并且通常需要大量的计算资源。为了促进金融特定的 LLM 框架，我们引入了一种基于 Llama 2 7B 基础模型的全新方法，以利用其生成性质和全面的语言操作能力。这是通过在少量监督金融情感分析数据上微调 Llama2 7B 模型，从而共同处理金融词汇表的复杂性和上下文，并进一步配备基于神经网络的决策机制来实现的。这种生成分类器-分类器方案，被称为 FinLlama，旨在不仅对情感极性进行分类，而且计量其强度，从而为交易者提供对金融新闻文章的细微洞察。此外，通过使用参数高效的微调通过 LoRA 优化可训练参数，从而最小化计算和内存需求，同时保持准确性。模拟结果证实了所提出的 FinLlama 能够为增强型组合管理决策和提高市场回报提供一个框架。这些结果进一步加强了 FinLlama 构建高回报组合并展示出增强弹性的能力，即使在波动时期和不可预测的市场事件中也是如此。
URL
https://arxiv.org/abs/2403.12285
