The world of finance is drowning in data. Every day, a deluge of research papers, market reports, and news articles floods the desks of traders and analysts. But what if artificial intelligence could help us navigate this ocean of information? A new study explores how Large Language Models (LLMs) and Natural Language Processing (NLP) can revolutionize literature reviews in the complex field of algorithmic trading. Researchers tackled a massive dataset of 136 million research papers, identifying over 14,000 relevant articles on algorithmic trading published between 1956 and 2020. Their goal was ambitious: to see if AI could not only organize this vast knowledge base but also answer complex questions about trading strategies, model performance, and emerging trends. Traditional methods, like keyword searches and basic statistical analysis, revealed some interesting insights. Algorithmic trading research is booming, with machine learning models rapidly gaining popularity. Stocks and indices remain the dominant focus, but interest in cryptocurrencies has exploded in recent years, mirroring real-world market activity. However, the real magic happened when the researchers unleashed the power of LLMs like ChatGPT. They found that LLMs could delve deeper into the research, comparing the effectiveness of different trading models and even identifying which ones outperformed others. This goes far beyond what simple keyword searches can achieve, requiring a nuanced understanding of the research methodologies and results. The study also highlighted the importance of analyzing full research papers rather than just abstracts. Key details about model comparisons, hyperparameter optimization, and data frequency were often buried within the full text, demonstrating the limitations of relying solely on summaries. While LLMs showed immense promise, the researchers also encountered challenges. Processing massive research papers sometimes overwhelmed the LLMs, leading to errors or hallucinations. Ensuring the LLMs “read” and understood the context, rather than just performing keyword searches, required careful prompt engineering and a step-by-step approach. This research opens exciting new possibilities for AI-driven literature reviews. Imagine a future where researchers can quickly synthesize vast amounts of information, uncovering hidden connections and accelerating the pace of discovery. While challenges remain, this study demonstrates the potential of LLMs to transform how we navigate the ever-expanding universe of scientific knowledge, not just in finance but across all disciplines.