FinLLM-B：当大语言模型遇上金融突破交易
FinLLM-B: When Large Language Models Meet Financial Breakout Trading
AI总结：
针对金融突破交易中真假突破难区分、现有大模型效果欠佳的问题，研究构建首个金融突破数据集，推出带多阶段结构的FinLLM-B模型，显著提升突破检测的答案与推理准确率。
摘要：
交易区间突破是金融交易技术分析中的核心方法，被股票、期货、外汇等金融市场的交易者广泛使用。然而，区分真假突破并给出正确依据对投资者来说是巨大挑战。传统量化方法需要大量数据，且无法直接呈现推理过程，在该领域存在不足。近年来，大语言模型在各类下游应用中取得成功，但在金融突破检测领域的效果欠佳，原因在于突破检测需要专属数据和特定领域知识。为解决这些问题，我们构建了首个金融突破数据集，并推出FinLLM-B——首款面向金融突破检测的大语言模型，可提升突破交易策略的有效性。此外，我们提出了一种新型大语言模型框架——多阶段结构，有效减少了下游应用中的错误。实验结果表明，与GPT-3.5相比，FinLLM-B的答案及推理平均准确率提升49.97%，其中多阶段结构贡献了9.72%的提升；此外，其表现优于ChatGPT-4 42.38%。
英文摘要：
Trading range breakout is a key method in the technical analysis of financial trading, widely employed by traders in financial markets such as stocks, futures, and foreign exchange. However, distinguishing between true and false breakout and providing the correct rationale cause significant challenges to investors. Traditional quantitative methods require large amounts of data and cannot directly present the reasoning process, making them less than perfect in this field. Recently, large language models have achieved success in various downstream applications, but their effectiveness in the domain of financial breakout detection has been subpar. The reason is that the unique data and specific knowledge are required in breakout detection. To address these issues, we create the first financial breakout dataset and introduce FinLLM-B, the premier large language model for financial breakout detection, which enhances the effectiveness of breakout trading strategies. Furthermore, we have developed a novel framework for large language models, namely multi-stage structure, effectively reducing mistakes in downstream applications. Experimental results indicate that compared to GPT-3.5, FinLLM-B improves the average accuracy of answers and rational by 49.97%, with the multi-stage structure contributing 9.72% to the improvement. Additionally, it outperforms ChatGPT-4 by 42.38%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
