Ernest Chan – Generative AI for Asset Managers Workshop Recording
Unleash the Potential of Generative AI in Asset Management: Discover, Learn, and Apply!
The recorded content from our enlightening 2-day workshop held from September 30 to October 1, 2023, is now available for purchase. Hosted by industry stalwarts Dr. Ernest Chan, Dr. Roger Hunter, Dr. Hamlet Medina, and featuring an insightful keynote by Dr. Lisa Huang, this workshop meticulously explores the deployment of Large Language Models (LLMs) in asset management, particularly focusing on crafting robust discretionary trading strategies.
Learning Goals
A thorough understanding of Generative AI fundamentals and advanced techniques, tailor-made for asset management applications.
Practical acumen to design, evaluate, and deploy LLMs for innovative trading strategies.
An in-depth exploration of prompt engineering and risk mitigation strategies associated with LLMs.
Strategies to enhance trading methodologies with sentiment analysis employing LLMs.
Generative AI for Asset Managers is a 2-day online workshop to demonstrate how we construct a discretionary trading strategy using a LLM. We will demonstrate how asset managers and traders can use Google’s BARD to turn unstructured data such as the audio feed of the Federal Reserve’s Chair’s speech into high frequency trading signals and backtest such strategies, all at minimal cost. Participants can explore and experiment with variations and improvements on the basic code, as well as other use cases of LLM for asset management.
What You’ll Learn In Generative AI for Asset Managers Workshop Recording
Workshop Overview
Day 1:
Exploring Generative AI and Large Language Models (LLMs) in Asset Management
In depth look, into the applications of LLMs such as BARD, ChatGPT in the industry.
Utilizing LLMs to develop trading approaches.
Practical session; Converting data into signals for high frequency trading.
Day 2:
Advanced Strategies and Real World Uses
Informal Conversation with Lisa Huang
Discussion, on design and methods to manage risks associated with LLMs.
Enhancing trading tactics with detailed sentiment analysis through LLMs.
Hands on activity; Testing trading strategies and exploring how LLMs could transform asset management practices.
Workshop Outline
01 Exploring Big Language Models (BLMs) & Pre trained Generative Transformers (PGT)
Getting familiar, with BLMs like BARD, ChatGPT and other advanced language models
Common Uses of BLMs
Understanding the functionality of BLMs
Accessing BARD/PaLM online using their API
02 Developing Software
Introduction to Prompt Design
Creating software, for tasks like writing text summarizing content and more.
Exploring few shot learning, with BARD
Introduction to embeddings and their significance
An overview of the BARD embeddings API. How it is utilized
03 Risks Linked with Language Models (LLMs)
Recognizing risks associated with LLMs, including hallucinations, bias, consent and security.
Strategies, for mitigating the risk of hallucinations, such as retrieval enhancement, prompt manipulation and self analysis.
Techniques for identifying and managing hallucinations, including reinforcement learning based on feedback (RLHF) and model driven approaches.
04 Utilizing Language Models for Analyzing Federal Reserve Chairs Speeches
Reasons for selecting the BARD family over LLMs.
Assessment of BARDs performance.
Enhancing performance through embeddings.
Practical demonstration; evaluating sentiment scores on companies using embeddings.
Test data; Video recordings of the Federal Reserve Chairs press conferences.
Conducting an analysis of a trading strategy based on the sentiment analysis provided by an LLM.
05 Implementation of Language Models in Real world Scenarios
Practices for deploying LLMs in production environments.
Overview of models, like ChatGPT, BART, Cohere, Alpaca, etc.
