Generative AI for Trading and Asset Management - Tapa dura

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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.

De la contraportada

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.

De la solapa interior

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.