HarmoniQ Insights

William Mann

Independent Insight Provider

At HarmoniQ Insights, we empower investors to enhance their investment strategies by integrating cutting-edge quantitative research processes and technologies. Our solutions provide access to leading quantitative data providers and services, tailored to align with each client’s unique goals. By bridging the gap between traditional investment approaches and advanced data-driven methods, we help clients gain a competitive edge through innovation in both process and technology.

Cross Asset Strategy

<ul>
<li>A Comprehensive Guide to Building AI-Powered Investment Systems: The application of Large Language Models (LLMs) in quantitative finance, focusing on the current state of LLM technology in financial applications </li>
<li>I also cover: 1. Comparative analysis of leading models                2. Implementation frameworks for production systems 3. Risk management and quality control considerations </li>
<li>Key Findings: 1. Multi-model approaches outperform single-model solutions 2. Production implementation requires robust quality controls 3. Model selection should be task-specific within the investment process </li>
</ul>


A Comprehensive Guide to Building AI-Powered Investment Systems: The application of Large Language Models (LLMs) in quantitative finance, focusing on the current state of LLM technology in financial applications 
I also cover: 1. Comparative analysis of leading models                2. Implementation frameworks for production systems 3. Risk management and quality control considerations 
Key Findings: 1. Multi-model approaches outperform single-model solutions 2. Production implementation requires robust quality controls 3. Model selection should be task-specific within the investment process 


The LLM Quant Revolution: From ChatGPT to Wall Street

Equity Bottom-Up

Microchip Technology Incorporated designs, manufactures, and markets microcontrollers, related mixed-signal and memory products, and application development systems for high-volume embedded control applications. The Company also designs, develops, and markets linear/mixed-signal, power management, and thermal management products.

<ul>
<li>Initiate a short position in MCHP Target Price Range: $45-50 Time Horizon: 6-12 months </li>
<li>
Declining fundamentals: Q2 FY2025 revenue declined 48% YoY, with adjusted EPS falling 72% YoY. Valuation concerns: P/E of 36.8x vs. sector median of 22x, unsupported by current performance.  </li>
<li>
Geo-Political risk: 50% of sales from Asia, exposed to potential trade tensions. Operational challenges: Customer destocking and sluggish demand. ETF-Driven price support: Potential for sharp correction if sector rotation occurs. </li>
</ul>


Initiate a short position in MCHP Target Price Range: $45-50 Time Horizon: 6-12 months 

Declining fundamentals: Q2 FY2025 revenue declined 48% YoY, with adjusted EPS falling 72% YoY. Valuation concerns: P/E of 36.8x vs. sector median of 22x, unsupported by current performance.  

Geo-Political risk: 50% of sales from Asia, exposed to potential trade tensions. Operational challenges: Customer destocking and sluggish demand. ETF-Driven price support: Potential for sharp correction if sector rotation occurs. 


2025 High Conviction Short: Microchip Technology Inc. (MCHP)

Quantitative Analysis

<ul>
<li>This report outlines a Quality-Control step: comparing output recommendations from two AIs: ChatGPT 4.o and o1-preview, to identify and analyze inconsistencies between the AI’s interpretation of the same Inputs/Prompts. </li>
<li>
<a href="https://skr.ma/KGAZA" target="_blank" rel="noopener">The report from Primer 2</a> is uploaded to each AI with the same prompts: to compare and analyze the outputs and settle on a portfolio weighted with relative ETF convictions.</li>
<li>We analyze and adjust the merged AI-driven portfolio where we disagree with the AI's conclusions. Our logic is shared with the AI to derive a final conviction-weighted market-neutral portfolio.  </li>
</ul>


This report outlines a Quality-Control step: comparing output recommendations from two AIs: ChatGPT 4.o and o1-preview, to identify and analyze inconsistencies between the AI’s interpretation of the same Inputs/Prompts. 

The report from Primer 2 is uploaded to each AI with the same prompts: to compare and analyze the outputs and settle on a portfolio weighted with relative ETF convictions.
We analyze and adjust the merged AI-driven portfolio where we disagree with the AI's conclusions. Our logic is shared with the AI to derive a final conviction-weighted market-neutral portfolio.  


Quantamental LLM Primer 3: Quality-Control 2 AI-designed Portfolios to Devise a Final Portfolio

<ul>
<li>Ask the AI to convert the Trading Strategy Recommendations from <a href="https://www.smartkarma.com/insights/quantamental-llm-primer-crowdsource-expert-outlooks-to-design-a-trading-strategy-with-llms-part-1?slug=quantamental-llm-primer-crowdsource-expert-outlooks-to-design-a-trading-strategy-with-llms-part-1" target="_blank" rel="noopener">Part 1</a> into a L/S Alpha Capture portfolio of ETFs. Iterate several times to ensure the portfolio recommendations are sound. </li>
<li>
Sanity Checkand provide feedback to AI to iterate on portfolio. Generate report with final recommendations. Run same process with another AI to compare its interpretation of the Trading Strategy.</li>
<li>Using Prompt Engineer, compare these two separate AI-driven reports, in both AIs to highlight similarities and differences between the models. Resolve to a final portfolio with this Sanity-Check comparison</li>
</ul>


Ask the AI to convert the Trading Strategy Recommendations from Part 1 into a L/S Alpha Capture portfolio of ETFs. Iterate several times to ensure the portfolio recommendations are sound. 

Sanity Checkand provide feedback to AI to iterate on portfolio. Generate report with final recommendations. Run same process with another AI to compare its interpretation of the Trading Strategy.
Using Prompt Engineer, compare these two separate AI-driven reports, in both AIs to highlight similarities and differences between the models. Resolve to a final portfolio with this Sanity-Check comparison


Quantamental LLM Primer 2: Convert Sellside/Buyside Ideas into a Long/Short Alpha Capture Portfolio

<ul>
<li>Ingest/Summarize Reports: Upload 30+ reports in batches. For each report, catalog key information (title, source, date) and summarize the outlook on equities, risk factors, sector performance, and regional forecasts.</li>
<li>Synthesize and Analyze Data: After summarizing all reports, identify common themes, such as equities trends, sector performance, quantitative risk factors, and regional forecasts. Highlight both consensus views and contrarian perspectives.</li>
<li>Compose Outlook: Use synthesized research to compose Q4 2024 stock market outlook, with sections on consensus market sentiment, key risks, contrarian views, and regional outlooks, attributing insights to respective sources.</li>
</ul>


Ingest/Summarize Reports: Upload 30+ reports in batches. For each report, catalog key information (title, source, date) and summarize the outlook on equities, risk factors, sector performance, and regional forecasts.
Synthesize and Analyze Data: After summarizing all reports, identify common themes, such as equities trends, sector performance, quantitative risk factors, and regional forecasts. Highlight both consensus views and contrarian perspectives.
Compose Outlook: Use synthesized research to compose Q4 2024 stock market outlook, with sections on consensus market sentiment, key risks, contrarian views, and regional outlooks, attributing insights to respective sources.


Quantamental LLM Primer: Crowdsource Expert Outlooks to Design a Trading Strategy with LLMs (Part 1)

Independent Insight Provider, HarmoniQ Insights

Bill Mann is a seasoned expert in bridging the gap between traditional fundamental analysis and cutting-edge quantitative methodologies. His career in quantitative finance was shaped by a pivotal experience during the 2008 financial crisis at AIG, where he witnessed the dangers of emotional attachment to underperforming investments. This experience sparked his shift from Fundamental to Quantitative analytics, which led him to key roles at Bloomberg and AQR, and ultimately to eight impactful years at Two Sigma.

Throughout his tenure at quantitative hedge funds, Bill led initiatives to optimize alpha modeling throughput by spearheading collaborative research processes that integrated advanced data science and ML/AI capabilities. His unique blend of expertise, underpinned by CPA and CFA designations, enabled him to excel as an industry-specific quant fundamentals analyst, combining fundamental research with quantitative rigor.

As the Co-Founder and Managing Partner of HarmoniQ Insights, Bill now offers his clients a powerful combination of deep industry knowledge and expertise in cutting-edge technology. He empowers fundamental analysts to make confident, data-driven decisions through sophisticated statistical analysis. Leveraging his extensive experience collaborating with quantitative researchers and engineers, Bill is adept at building consensus among senior executives, guiding them to invest with confidence in transformative technologies.

When he’s not driving innovation in the finance world, Bill enjoys playing tennis or spending a day at the beach with his children.
