Generative AI for Trading and Asset Management

H Medina

ISBN 10: 1394266979 / ISBN 13: 9781394266975

Published by Wiley, 2025

About this Item

Seller Inventory # 2098075615

Bibliographic Details

• Title: Generative AI for Trading and Asset Management
• Publisher: Wiley
• Publication Date: 2025
• Language: English
• ISBN 10: 1394266979
• ISBN 13: 9781394266975
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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.

About the Author

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.