Ernest Chan – Generative AI for Asset Managers Workshop Recording

Description

Generative AI for Asset Managers Workshop Recording , Ernest Chan – Generative AI for Asset Managers Workshop Recording download

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.

Workshop Speakers
This workshop is hosted by Dr. Ernest Chan, Founder and CEO of Predictnow.ai, Dr. Roger Hunter, Chief Technology Officer at QTS Capital Management, and Dr. Hamlet Medina, Chief Data Scientist at Criteo. We are honoured to be joined by Dr. Lisa Huang, Head of AI Investment Management at Fidelity Investments who will present as a keynote speaker.

Host
Dr. Ernest Chan
Founder and CEO of Predictnow.ai

Host
Dr. Roger Hunter
CTO of QTS Capital Management

Host
Dr. Hamlet Medina
Chief Data Scientist at Criteo

Keynote Speaker
Dr. Lisa Huang
Head of AI Investment Management, Fidelity Investments.

Workshop Overview

Day 1:
Delve into Generative AI and LLMs in Asset Management

• Comprehensive exploration of Large Language Models (LLMs) like BARD, ChatGPT, and their manifold applications in the finance sector.
• Harnessing the power of LLMs to structure discretionary trading strategies efficiently.
• Hands-on session: Transforming unstructured data into actionable high-frequency trading signals.

Day 2:
Advanced Techniques and Real-World Applications

• Fireside Chat with Lisa Huang
• Extensive coverage on prompt engineering and strategies to mitigate risks inherent to LLMs.
• Augmenting trading strategies with nuanced sentiment analysis using LLMs.
• Hands-on exercise: Backtesting trading strategies and a brainstorming session on LLM’s potential to revolutionize asset management practices.

Testimonials
Behnam Javanparast
PhD, Chief Executive Officer
yiyaniQ

“This workshop provided me with a great overview of current LLM technologies, their use in Financial Industry, and the challenges one might face in using them. Hands-on portion was very useful to get started with recent tools in the space. The fireside chat gave me the perspective of decision-makers in established financial institutions on the topic. Overall, after the workshop one would know where to go next to keep exploring.”

Intended Audiences
Asset Managers
Venture Investors
Entrepreneurs
Product Developers
Regulators
Finance & AI Researchers

Workshop Outline

01
Large Language Models (LLMs) & Generative Pre-trained Transformers (GPT)

• Introduction to LLM: BARD, ChatGPT, and other large language models
• Typical Applications of LLMs
• How LLMs work
• Using BARD/PaLM on the web through their API

02
Building Applications

• Overview of Prompt Engineering
• Building applications such as text generation, summarization, etc.
• Few-shot learning with BARD
• Introduction to embeddings
• Overview of the BARD embeddings API and its usage

03
Risks Associated with LLMs

• Understanding main risks with LLMs, such as, hallucinations, bias, consent and security
• Methods for reducing the risks of Hallucinations, such as, retrieval augmentation, prompt engineering, and self-reflection
• Methods to detect and address hallucinations, including reinforcement learning from human feedback (RLHF) and model-based approaches

04
Using LLMs for trading Federal Reserve Chair’s speeches

• Why we chose the BARD family among the many available LLMs
• Evaluating BARD’s native performance
• Improving performance with embeddings
• Worked example: computing sentiment ratings on public companies using embeddings
• Test data: Video archives of the press conferences of the Federal Reserve Chair.
• Backtesting a discretionary trading strategy using the sentiment output of a LLM.

05
Deploying LLMs in Production

• Best Practices for Deploying LLMs in Production
• Overview of alternative generative models such as ChatGPT, BART, Cohere, Alpaca, etc.Commonly Asked Questions:
  1. Business Model Innovation: Acknowledge the reality of a legitimate enterprise! Our approach involves the coordination of a collective purchase, in which the costs are shared among the participants. We utilize this cash to acquire renowned courses from sale pages and make them accessible to individuals with restricted financial resources. Our clients appreciate the affordability and accessibility we provide, despite the authors’ concerns.
  2. Generative AI for Asset Managers Workshop Recording Course
  • There are no scheduled coaching calls or sessions with the author.
  • Access to the author’s private Facebook group or web portal is not permitted.
  • No access to the author’s private membership forum.
  • There is no direct email support available from the author or their team.