Generative AI for Trading and Asset Management
-
Form:Einzelkauf Download
-
Sprache:Englisch
-
eBook Format:PDF
- PDF 37,99 € ausgewählt
- ePUB 3 37,99 €
37,99 €
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
Kopierschutz
Ja
Family Sharing
Nein
Text-to-Speech
Nein
Erscheinungsdatum
30.04.2025
Verlag
WileySeitenzahl
316 (Printausgabe)
Dateigröße
6153 KB
Auflage
1. Auflage
Sprache
Englisch
EAN
9781394267002
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.
Kundinnen und Kunden meinen
Verfassen Sie die erste Bewertung zu diesem Artikel
Helfen Sie anderen Kund*innen durch Ihre Meinung
Kurze Frage zu unserer Seite
Vielen Dank für dein Feedback
Wir nutzen dein Feedback, um unsere Produktseiten zu verbessern. Bitte habe Verständnis, dass wir dir keine Rückmeldung geben können. Falls du Kontakt mit uns aufnehmen möchtest, kannst du dich aber gerne an unseren Kund*innenservice wenden.
zum Kundenservice