Key Takeaways
- • AlphaBee Multi-Arbitrage Fund won The Hedge Fund Journal's Best Performing Fund award for 2022 and over 2 and 5-year periods in the Fund of Hedge Funds (Arbitrage Strategies) category, with a Sharpe ratio exceeding 2.0 in 2022.
- • The fund was seeded in June 2017 by co-founders David Arnaud (CIO with M&A, quantitative, and hedge fund due diligence expertise) and Frederic Guibaud (background in long-only assets, commercial real estate, and liquid alternatives), with a four-person team holding nearly 100 years of cumulative financial experience.
- • AlphaBee enforces significant co-investment by its managers in their own funds, aligning manager and investor interests through an entrepreneurial operating model.
- • Emerging managers sourced through the fund of funds structure generate alpha even after accounting for survivorship bias, according to co-founder Frederic Guibaud, providing institutional investors access to niche arbitrage strategies unavailable through traditional channels.
- • The Multi-Arbitrage fund's original "Mongoose" naming reflects the investment philosophy: identifying resilient, alpha-hungry managers capable of outperforming industry leaders despite resource asymmetries.
Summary
AlphaBee Multi-Arbitrage Fund has established itself as a high-performing fund of funds platform focused on arbitrage strategies. The fund received The Hedge Fund Journal's CTA and Discretionary Trader award for Best Performing Fund in 2022 and demonstrated sustained performance over 2 and 5-year periods within the Fund of Hedge Funds (Arbitrage Strategies) category. Its 2022 Sharpe ratio exceeded 2.0, a benchmark reflecting strong risk-adjusted returns in an environment where many traditional strategies faced headwinds.
The fund was established in June 2017 by co-founders David Arnaud and Frederic Guibaud. Arnaud brings decades of CIO experience alongside substantive exposure to M&A, quantitative trading, and hedge fund due diligence. Guibaud contributes background in long-only asset management, commercial real estate, and liquid alternatives. The four-person management team collectively holds close to 100 years of investment and finance experience. This pedigree informs AlphaBee's investment process, which combines quantitative and qualitative analysis through a structured investment committee framework.
A core operational principle distinguishes AlphaBee's approach to manager alignment: the fund requires its managers to maintain significant personal capital invested alongside client assets. This co-investment mandate ensures that decision-makers bear proportional downside risk and creates an entrepreneurial mentality across the organization. The structure reflects conviction that aligned incentives drive superior capital allocation and risk discipline.
AlphaBee's thesis centers on identifying emerging hedge fund managers capable of generating alpha despite not yet commanding the scale or brand recognition of established firms. According to co-founder Frederic Guibaud, "We find emerging managers can make more alpha even after controlling for survivorship bias." This position validates the fund of funds model—often criticized as an inefficient fee layer—as a vehicle providing institutional allocators access to specialized arbitrage strategies and return streams otherwise unavailable through direct manager relationships or traditional hedge fund infrastructure.
The fund's original working name, "Mongoose," exemplifies its philosophy: like the animal's capacity to dispatch larger predators through agility and precision, AlphaBee targets nimble emerging managers capable of outperforming industry behemoths. This metaphor encapsulates the firm's conviction that capital-efficient, specialized managers operating in niche arbitrage spaces represent an underappreciated opportunity for institutional investors seeking diversified alpha sources across multiple market environments.
The fund was established in June 2017 by co-founders David Arnaud and Frederic Guibaud. Arnaud brings decades of CIO experience alongside substantive exposure to M&A, quantitative trading, and hedge fund due diligence. Guibaud contributes background in long-only asset management, commercial real estate, and liquid alternatives. The four-person management team collectively holds close to 100 years of investment and finance experience. This pedigree informs AlphaBee's investment process, which combines quantitative and qualitative analysis through a structured investment committee framework.
A core operational principle distinguishes AlphaBee's approach to manager alignment: the fund requires its managers to maintain significant personal capital invested alongside client assets. This co-investment mandate ensures that decision-makers bear proportional downside risk and creates an entrepreneurial mentality across the organization. The structure reflects conviction that aligned incentives drive superior capital allocation and risk discipline.
AlphaBee's thesis centers on identifying emerging hedge fund managers capable of generating alpha despite not yet commanding the scale or brand recognition of established firms. According to co-founder Frederic Guibaud, "We find emerging managers can make more alpha even after controlling for survivorship bias." This position validates the fund of funds model—often criticized as an inefficient fee layer—as a vehicle providing institutional allocators access to specialized arbitrage strategies and return streams otherwise unavailable through direct manager relationships or traditional hedge fund infrastructure.
The fund's original working name, "Mongoose," exemplifies its philosophy: like the animal's capacity to dispatch larger predators through agility and precision, AlphaBee targets nimble emerging managers capable of outperforming industry behemoths. This metaphor encapsulates the firm's conviction that capital-efficient, specialized managers operating in niche arbitrage spaces represent an underappreciated opportunity for institutional investors seeking diversified alpha sources across multiple market environments.
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