Description
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.
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.
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.
About the author
Ernie is the founder and Chief Scientist of PredictNow.ai, machine learning SaaS for enterprise resource optimization, and also the founder and non-executive chairman of QTS Capital Management, LLC., a commodity pool operator and trading advisor. PredictNow.ai uses contextual information for resource allocations.
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