From Deep Learning to LLMs: A survey of AI in Quantitative Investment

TL;DR

This survey reviews how recent AI advancements, especially deep learning and large language models, are transforming quantitative investment by enhancing predictive models and enabling autonomous decision-making workflows.

Contribution

It provides a comprehensive overview of AI's evolving role in quant finance, highlighting the transition from traditional models to deep learning and LLMs in investment pipelines.

Findings

Deep learning enables scalable modeling across the entire quant pipeline.

LLMs extend AI capabilities to unstructured data processing and autonomous decision-making.

AI advancements suggest a paradigm shift in quantitative investment strategies.

Abstract

Quantitative investment (quant) is an emerging, technology-driven approach in asset management, increasingy shaped by advancements in artificial intelligence. Recent advances in deep learning and large language models (LLMs) for quant finance have improved predictive modeling and enabled agent-based automation, suggesting a potential paradigm shift in this field. In this survey, taking alpha strategy as a representative example, we explore how AI contributes to the quantitative investment pipeline. We first examine the early stage of quant research, centered on human-crafted features and traditional statistical models with an established alpha pipeline. We then discuss the rise of deep learning, which enabled scalable modeling across the entire pipeline from data processing to order execution. Building on this, we highlight the emerging role of LLMs in extending AI beyond prediction, empowering autonomous agents to process unstructured data, generate alphas, and support self-iterative workflows.