Demystify AI & Quantitative Research
Quantamental Sherpa Guides for your data-driven journey
The Evolution of Portfolio Management: Bridging Traditional Models and Machine Learning
A dendrogram illustrating how Hierarchical Risk Parity (HRP) clusters stocks based on their correlation structure. This visualization is useful for understanding how HRP groups assets before applying risk-based weighting.
The Trump Transition Trade: Q1 2025 AI-Designed Tactical Alpha Capture ETF Portfolio
The Alpha Capture Outlooks across Macro Regions, Sectors and Risk Premia are converted into a Long/Short ETF Portfolio.
2025 Global Asset Allocation Wall Street Alpha Capture Outlook: Resilience Amid Uncertainty
The process we use with Gemini Advanced 1.5
The LLM Revolution in Quantitative Investment: A Practical Guide
Comprehensive LLM Model Rankings by Investment Phase
LLM Primer 3: GPT Model Quality Control Portfolio
Step-By-Step Guide to create a Quality-Control process by comparing the output from two different AIs: ChatGPT 4.o and o1-preview in order to identify and analyze inconsistencies in the AI’s interpretation of the same inputs and prompts.
The output of that process produces two separate Long-Short Portfolio Recommendations. These two reports are then reintroduced to ChatGPT 4o with Prompt Engineering Instructions to ensure the comparison and analytics are done thoroughly to finalize a high-quality market-neutral portfolio recommendation.
Digital Asset Landscape Primer: Part 1
This first Digital Asset Primer in a series explores the digital asset landscape, aiming to broaden the reader's understanding of fundamental terms and concepts. The goal is to help individuals become more familiar with the language of the industry by introducing key ideas that shape it. The content is broken down into sections, gradually expanding on terminology and concepts throughout each part.
Quantamental LLM Primer 2: Convert Sellside/Buyside Ideas into a Long/Short Alpha Capture Portfolio
This is part 2 of a series on leveraging LLMs in your fundamental analysis workflow. In Primer 1, we collected ideas from Sellside/Buyside outlooks and used ChatGPT to synthesize an alpha capture outlook for global stocks. This series is intended for fundamental and discretionary research analysts who are seeking to incorporate quantitative methods into their research process. As a practitioner of over 20 years who's also made the journey, I'm sharing my research process step-by-step to demonstrate the power of these tools to build and test an investment thesis. Perhaps more importantly, we will demonstrate how to build a process around sanity checking to ensure that the model’s output reflects your expert opinion. As we conclude this piece, we reiterate that Computers probably shouldn’t “assume” anything about the future direction of Central Bank policies!
Quantamental LLM Primer 101: Crowdsource Alpha Capture Portfolio with ChatGPT (Part 1)
The following is a “beginner’s guide” or Primer to unlocking the power of LLMs. This “Quantamental” series is intended for fundamental and discretionary research analysts who are seeking to incorporate quantitative methods into their research process. As a practitioner of over 20 years who's also made the journey, I'm sharing my research process step-by-step to demonstrate the power of these tools to build and to test an investment thesis.
In Primer 1, we will upload Sellside/Buyside global stock market outlooks to ChatGPT and synthesize an outlook on global equities, risk factors, sector performance, and regional forecasts. We will use the synthesized view to design an Alpha Capture Portfolio in Primer 2.