Enhancing literature review with NLP methods Algorithmic investment strategies case

Stanisław Łaniewski and Robert Ślepaczuk

No 2024-16, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: This study utilizes machine learning algorithms to analyze and organize knowledge in the field of algorithmic trading, based on filtering 136 million research papers to 14,342 articles ranging from 1956 to Q1 2020. We compare previously used practices such as keyword-based algorithms and embedding techniques with state-of-the-art dimension reduction and clustering for topic modeling method (BERTopic) to compare the popularity and evolution of different approaches and themes. We show new possibilities created by the last iteration of Large Language Models (LLM) like ChatGPT. The analysis reveals that the number of research articles on algorithmic trading is increasing faster than the overall number of papers. The stocks and main indices comprise more than half of all assets considered, but the growing trend in some classes is much stronger (e.g. cryptocurrencies). Machine learning models have become the most popular methods nowadays, but they are often flawed compared to seemingly simpler techniques. The study demonstrates the usefulness of Natural Language Processing in asking intricate questions about analyzed articles, like comparing the efficiency of different models. We demonstrate the efficiency of LLMs in refining datasets. Our research shows that by breaking tasks into smaller ones and adding reasoning steps, we can effectively address complex questions supported by case analyses.

Keywords: trading; quantitative finance; neural networks; literature review; knowledge representation; natural language processing (NLP); topic modeling; model comparison; artificial intelligence (search for similar items in EconPapers)
JEL-codes: C15 C22 C4 C45 C53 C58 C61 G11 G14 G15 G17 (search for similar items in EconPapers)
Pages: 36 pages
Date: 2024
New Economics Papers: this item is included in nep-big and nep-cmp
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