Generative AI for Trading and Asset Management

Expert guide on using AI to supercharge traders' productivity, optimise portfolios, and suggest new trading strategies.

Generative AI for Trading and Asset Management is an essential guide to understanding how generative AI has emerged as a transformative force in the realm of asset management, particularly in the context of trading. Its ability to analyse vast datasets, identify intricate patterns, and suggest complex trading strategies makes it indispensable. Practically, this book explains how to utilise various types of AI: unsupervised learning, supervised learning, reinforcement learning, and large language models, to suggest new trading strategies, manage risks, optimise trading strategies and portfolios, and generally improve the productivity of algorithmic and discretionary traders alike. These techniques converge into an algorithm to trade on the Federal Reserve chair's press conferences in real time.

Written by Hamlet Medina, chief data scientist at Criteo, and Ernie Chan, founder of QTS Capital Management and Predictnow.ai, this book explores topics including:

• How large language models and other machine learning techniques can improve the productivity of algorithmic and discretionary traders, from ideation, signal generations, backtesting, risk management, to portfolio optimisation.
• The pros and cons of tree-based models versus neural networks as they relate to financial applications, and how regularisation techniques can enhance out-of-sample performance.
• Comprehensive exploration of the main families of explicit and implicit generative models for modelling high-dimensional data, including their advantages and limitations in model representation and training, sampling quality and speed, and representation learning.
• Techniques for combining and utilising generative models to address data scarcity and enhance data augmentation for training ML models in financial applications like market simulations, sentiment analysis, risk management, and more.
• Application of generative AI models for processing fundamental data to develop trading signals.
• Exploration of efficient methods for deploying large models into production, highlighting techniques and strategies to enhance inference efficiency, such as model pruning, quantisation, and knowledge distillation.
• Using existing LLMs to translate Federal Reserve Chair's speeches to text and generate trading signals.

Generative AI for Trading and Asset Management earns a well-deserved spot on the bookshelves of all asset managers seeking to harness the ever-changing landscape of AI technologies to navigate financial markets.