Generative AI for Trading for Asset Management

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.