Not All Quants Are Alike: Exploring Where Equity Alpha Comes From

Quantitative equity alpha increasingly comes from proprietary, continuously refreshed stock-selection insights rather than static exposure to traditional style factors.

• Quant strategies performed well together, but their dispersion widened: Goldman Sachs Asset Management and eVestment compare 36 monthly observations in January 2013–December 2015 with January 2023–December 2025 and find wider relative-performance ranges in the more recent period.
• The authors attribute this differentiation to passive-investing growth, greater retail participation, investor-base fragmentation, alternative data, and uneven investment in data infrastructure, engineering, and machine learning.
• Proprietary signals should be evaluated by their correlation with common style factors; the selected QIS Equity Alpha signals showed correlations close to zero with Axioma factors over the 10 years to December 2025.
• Static factor exposure is vulnerable to regime shifts: MSCI World Growth outperformed from 2017 to 2020 but then reversed and underperformed the broader index by more than 11% in 2022, while value lagged for much of the decade before 2021.
• A modern quant process therefore requires broad signals across sectors, market capitalizations, countries, and time horizons, alongside efficient implementation, continuous research, monitoring of crowding and decay, and dynamic risk management; the team’s 2012 model overlaps by only approximately 40% with its 2026 model conclusions.

Takeaway: Investors should select systematic managers by decomposing active risk, testing proprietary-signal correlation with common factors, and examining behavior through drawdowns and factor rotations rather than relying on style labels such as value, quality, or momentum.