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.

Crypto

Virtual currencies are decentralized, math-based digital assets in which transactions can be performed cryptographically without the need for a central issuing authority. The first specification and proof of concept of Bitcoin, the first widely used virtual currency, was published in 2009 based on a paper published pseudonymously by Satoshi Nakamoto.

<ul>
<li>Market inefficiencies persist in crypto markets, particularly in cross-exchange arbitrage and futures basis trading</li>
<li>Traditional factor models can be adapted for cryptocurrency markets with size, momentum, and liquidity factors showing statistical significance</li>
<li>On-Chain metrics provide unique alpha signals unavailable in traditional markets</li>
</ul>


Market inefficiencies persist in crypto markets, particularly in cross-exchange arbitrage and futures basis trading
Traditional factor models can be adapted for cryptocurrency markets with size, momentum, and liquidity factors showing statistical significance
On-Chain metrics provide unique alpha signals unavailable in traditional markets


New Research: Unlocking Quantitative Alpha in Cryptocurrency Markets

Cross Asset Strategy

<ul>
<li>A fascinating lunch discussion with LLM engineer sparked an investigation into how AI models process information through vector spaces - revealing parallel but mechanistically different biases compared to human analysts </li>
<li>Built comprehensive framework mapping traditional financial biases against their LLM counterparts - showing how things like confirmation bias manifest differently (emotional investment vs statistical pattern reinforcement)</li>
<li>Result: Effective quant research requires understanding both human AND machine biases - can't just apply traditional risk controls to LLMs. Need tailored mitigations for how biases uniquely manifest in #AI.  </li>
</ul>


A fascinating lunch discussion with LLM engineer sparked an investigation into how AI models process information through vector spaces - revealing parallel but mechanistically different biases compared to human analysts 
Built comprehensive framework mapping traditional financial biases against their LLM counterparts - showing how things like confirmation bias manifest differently (emotional investment vs statistical pattern reinforcement)
Result: Effective quant research requires understanding both human AND machine biases - can't just apply traditional risk controls to LLMs. Need tailored mitigations for how biases uniquely manifest in #AI.  


Understanding LLM Biases in Quantitative Research: From Lunch Conversation to Framework

<ul><li>Podcast delayed, markets rebounded last week, led by news of reduced impact of Trump's tariffs</li><li>Fed slowed pace of quantitative tightening, reducing pressure on treasury market liquidity</li><li>Bitcoin and gold prices fluctuate, Chinese stocks rebound, DAX up 20% year to date, US equities impacted by tariffs and job losses</li></ul>This content is sourced through publicly available sources and has been machine generated. Information displayed is for general informational purposes only.

Podcast delayed, markets rebounded last week, led by news of reduced impact of Trump's tariffsFed slowed pace of quantitative tightening, reducing pressure on treasury market liquidityBitcoin and gold prices fluctuate, Chinese stocks rebound, DAX up 20% year to date, US equities impacted by tariffs and job lossesThis content is sourced through publicly available sources and has been machine generated. Information displayed is for general informational purposes only.

Tariffs, Tapering, and Tectonic Shifts: Markets Brace for April | The New Barbarians #013

<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

<ul><li>Market volatility continues, with futures down across the board</li><li>Atlanta Fed's GDP nowcast turns negative for the first time since 2022</li><li>Winners in the market so far this year include gold, European stocks, and bonds</li></ul>This content is sourced through publicly available sources and has been machine generated. Information displayed is for general informational purposes only.

Market volatility continues, with futures down across the boardAtlanta Fed's GDP nowcast turns negative for the first time since 2022Winners in the market so far this year include gold, European stocks, and bondsThis content is sourced through publicly available sources and has been machine generated. Information displayed is for general informational purposes only.

Volatility, Tariffs, and a Potential Recession: Breaking Down Macro Chaos | The New Barbarians #011

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.
