标题： 使用LLM和NLP方法增强文献综述。 算法交易案例 显示英文标题

标题： Enhancing literature review with LLM and NLP methods. Algorithmic trading case

摘要： 本研究利用机器学习算法来分析和整理算法交易领域的知识。 通过对包含 1.36 亿篇研究论文的数据集进行筛选，我们确定了 14,342 篇发表于 1956 年至 2020 年第一季度之间的相关文章。 我们将基于关键词的算法和嵌入技术等传统实践与最先进的主题建模方法（采用降维和聚类）进行了比较。 这种比较使我们能够评估不同方法和主题在算法交易中的流行程度和演变趋势。 我们展示了自然语言处理 (NLP) 在自动提取知识方面的有用性，并强调了大型语言模型 (LLM) 如 ChatGPT 最新迭代所创造的新可能性。 聚焦于此主题的理由源于我们的分析，该分析显示关于算法交易的研究文章的增长速度比总体出版物数量的增长速度快。 尽管股票和主要指数占所有资产的一半以上，但某些资产类别（如加密货币）显示出更强的增长趋势。 近年来，机器学习模型已成为最受欢迎的方法。 本研究表明，LLM 在精炼数据集和解决有关分析文章的复杂问题方面具有有效性，例如比较不同模型的效率。 我们的研究表明，通过将任务分解为更小的部分并结合推理步骤，我们可以有效地利用案例分析解决复杂问题。 这种方法有助于更深入地理解算法交易方法，并强调了高级 NLP 技术在文献综述中的潜力。 显示英文摘要

摘要： This study utilizes machine learning algorithms to analyze and organize knowledge in the field of algorithmic trading. By filtering a dataset of 136 million research papers, we identified 14,342 relevant articles published between 1956 and Q1 2020. We compare traditional practices-such as keyword-based algorithms and embedding techniques-with state-of-the-art topic modeling methods that employ dimensionality reduction and clustering. This comparison allows us to assess the popularity and evolution of different approaches and themes within algorithmic trading. We demonstrate the usefulness of Natural Language Processing (NLP) in the automatic extraction of knowledge, highlighting the new possibilities created by the latest iterations of Large Language Models (LLMs) like ChatGPT. The rationale for focusing on this topic stems from our analysis, which reveals that research articles on algorithmic trading are increasing at a faster rate than the overall number of publications. While stocks and main indices comprise more than half of all assets considered, certain asset classes, such as cryptocurrencies, exhibit a much stronger growth trend. Machine learning models have become the most popular methods in recent years. The study demonstrates the efficacy of LLMs in refining datasets and addressing intricate questions about the analyzed articles, such as comparing the efficiency of different models. Our research shows that by decomposing tasks into smaller components and incorporating reasoning steps, we can effectively tackle complex questions supported by case analyses. This approach contributes to a deeper understanding of algorithmic trading methodologies and underscores the potential of advanced NLP techniques in literature reviews.