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

TL;DR

This paper leverages NLP and Large Language Models to analyze, organize, and enhance literature reviews in algorithmic trading, revealing trends, asset class growth, and the efficacy of advanced models in knowledge extraction.

Contribution

It introduces a novel application of LLMs and NLP techniques to improve literature review processes and analyze trends in algorithmic trading research.

Findings

Research articles on algorithmic trading are increasing rapidly.

Machine learning models are the most popular recent methods.

Cryptocurrencies show a stronger growth trend compared to traditional assets.

Abstract

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.