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In Generative AI for Trading and Asset Management, a team of veteran finance and technology experts delivers a transformative new guide to using contemporary forms of AI technology--including unsupervised learning, supervised learning, reinforcement learning, and large language models--to improve the productivity of algorithmic and discretionary traders.

The book explains how large language models and other machine learning techniques can contribute to trader efficiency and effectiveness, from ideation to signal generation, backtesting, risk management, and portfolio optimization. It demonstrates the pros and cons of tree-based models and neural networks as they relate to financial applications and how regularization techniques can enhance out-of-sample performance.

Generative AI for Trading and Asset Management offers comprehensive explorations of the most common families of explicit and implicit generative modeling of high-dimensional data and covers their advantages and limitations in model representation and training, sampling quality and speed, and representation learning.

You'll learn how to develop new trading strategies, manage risks, optimize your own trading behavior, and improve your portfolios. The authors explain how to train your AI models to learn and evolve in real-time positions, helping readers capitalize on market opportunities to swiftly mitigate risk.

The book also discusses how to deploy many of its insightful methods at scale, showing you effective techniques for pushing large models into production and demonstrating strategies to enhance inference efficiency, like model pruning, quantization, and knowledge distillation.

Perfect for asset managers everywhere, Generative AI for Trading and Asset Management is an invaluable resource for anyone seeking to harness the ever-changing landscape of AI tech to navigate financial markets.

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