Generative AI for Trading and Asset Management - Hardcover

by Hamlet Jesse Medina Ruiz (Author), Ernest P. Chan (Author)

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

Generative AI for Trading and Asset Management is an essential guide to understand how generative AI has emerged as a transformative force in the realm of asset management, particularly in the context of trading, due to its ability to analyze vast datasets, identify intricate patterns, and suggest complex trading strategies. Practically, this book explains how to utilize various types of AI: unsupervised learning, supervised learning, reinforcement learning, and large language models to suggest new trading strategies, manage risks, optimize 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 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 productivity of algorithmic and discretionary traders from ideation, signal generations, backtesting, risk management, to portfolio optimization
• The pros and cons of tree-based models vs neural networks as they relate to financial applications. How regularization techniques can enhance out of sample performance
• Comprehensive exploration of the main families of explicit and implicit generative models for modeling high-dimensional data, including their advantages and limitations in model representation and training, sampling quality and speed, and representation learning.
• Techniques for combining and utilizing 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, quantization, 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.

Front Jacket

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.

Back Jacket

Praise for GENERATIVE AI FOR TRADING AND ASSET MANAGEMENT

"Kudos to Medina Ruiz and Chan for sharing their hard-earned knowledge and creating a practitioner resource that is timely, relevant, and 'roll up your sleeves' practical. They have the distinction of bringing to life a text that is both information-dense and comprehensive yet highly engaging and readable--a rare combination! Whether you plan to engage with Generative AI as your co-pilot or go deep into coding custom solutions, this book will serve as both a trusted guide and as a foundational reference. The addition of accompanying code repositories and notebooks will allow organizations to hit the ground running with Generative AI. I anticipate that I'll have a well-thumbed copy near my computer monitor in the very near future!"
--JAY VYAS, Chief Strategy Officer, Firinne Capital; former Head of Research and Innovation, Canada Pension Plan Investment Board

"The book by Medina Ruiz and Chan has appeared when people are still wondering what ChatGPT in particular and LLMs in general are all about. Those who will read and implement it first will have an undisputed advantage: they have learned the industry-defining subject from experts who are known for their knack for making the complex easily accessible. The book is not just about LLMs. It is about Generative AI and its intelligent use in trading and, more generally, finance. Highly recommended for practitioners and academics alike."
--PAUL ALEXANDER BILOKON, CEO, Thalesians Ltd; Visiting Professor, Imperial College London

"Generative AI for Trading and Asset Management by renowned quant Hamlet Medina Ruiz and Ernest Chan is the definitive guide to harnessing Generative AI in financial markets. From no-code AI for finance beginners to the fine-tuning of deep, multi-layered models, this book unpacks cutting-edge techniques with clarity and precision. Dive into deep autoregressive models, latent variable architectures, flow-based models, sentiment analysis, GANs, and LLMs--all tailored for quantitative trading and asset management. Whether you're an aspiring quant, a hedge fund strategist, or a Goldman Sachs MD, this book is your blueprint for staying ahead in the AI-driven evolution of finance."
--ALEXANDER FLEISS, CEO of Rebellion Research, AI Asset Manager & Research Think Tank, Advisory Board Member of both Cornell Financial Engineering & Fordham Gabelli Quantitative Finance, Editor of The Journal of Financial Data Science

Author Biography

HAMLET JESSE MEDINA RUIZ holds the position of Chief Data Scientist at Criteo. He specializes in time series forecasting, machine learning, deep learning, and Generative AI. He actively explores the potential of cutting-edge AI technologies, such as Generative AI across diverse applications. He holds an electronic engineering degree from Universidad Rafael Belloso Chacin in Venezuela, as well as two master's degrees with honors in mathematics and machine learning from the Institut Polytechnique de Paris and Université Paris-Saclay. Additionally, he earned a PhD in physics from Université Paris-Saclay. Hamlet has consistently achieved first place and top ten rankings in global machine learning contests, earning the titles of Kaggle Expert and Numerai Expert for these challenges. Recently, he also earned a MicroMaster's in finance from MIT's Sloan School of Management.

ERNEST CHAN (ERNIE) is the Founder and Chief Scientific Officer of PredictNow.ai (www.predictnow.ai), which offers AI-driven adaptive optimization solutions to the finance industry and beyond. He is also the Founder and Non-executive Chairperson of QTS Capital Management (www.qtscm.com), a quantitative CTA/CPO since 2011. He started his career as a machine learning researcher at IBM's T.J. Watson Research Center's language modeling group, which produced some of the best-known quant fund managers. Ernie is the acclaimed author of three previous books, Quantitative Trading (2nd Edition), Algorithmic Trading, and Machine Trading, all published by Wiley. More about these books and Ernie's workshops on topics in quantitative investing and machine learning can be found at www.epchan.com. He obtained his PhD in physics from Cornell University and his BS in physics from the University of Toronto.