Versor Research Book: A Decade of Quantitative Investment Research

This collection brings together a decade of quantitative investment research from Versor Investments, presented as ten white papers. The papers examine how systematic, evidence-led methods apply across liquid markets: the return convexity of trend following and managed futures, the diversifying role of global macro, the structure and risk management of systematic merger arbitrage, the long evolution of systematic value, and the broader study of the factors that drive returns. The research speaks to institutional allocators, OCIOs, and consultants. A single theme runs throughout: disciplined factor analysis clarifies which exposures may reward investors and which are better avoided. The papers favour measured, probabilistic conclusions over forecasts, and they frame their views as analytical perspective grounded in data. Together they trace how the firm’s thinking developed over ten years, and how that experience shapes its market-neutral hedge fund programs today. The emphasis throughout is on method, breadth, and risk management rather than any single signal or market call.

What This Collection Examines

• Whether trend following still delivers the return convexity investors expect, and where convexity can otherwise be found.
• How cross-sectional global macro can diversify portfolios during periods of market stress.
• What separates a rigorous, forecast-driven approach to merger arbitrage from a naive one, including the ESG characteristics of mergers.
• Why systematic value endured a prolonged drawdown, and what valuation spreads imply for its recovery.
• How the study of factors across asset classes informs which exposures to harvest and which to avoid.

Key Findings

• Trend following has lost much of its historical convexity, but convexity can be rebuilt. The long-term signals that dominate the largest managed futures funds now show little of the positive convexity investors expect. Adding non-trend, cross-sectional signals tends to restore it.
• Cross-sectional global macro tends to perform when equities do not. Market-neutral macro profits from dispersion across assets rather than market direction. This gives it low correlation to stocks and bonds and a useful role in turbulent periods.
• A forecast-driven approach to merger arbitrage can improve on a naive one. Predicting which deals complete, terminate, or attract competing bids matters for outcomes. Completed mergers are also associated with large, measurable improvements in the ESG scores of target companies.
• Wide valuation spreads tend to precede stronger returns to value. When the gap between cheap and expensive stocks reaches extremes, it has historically tended to compress. Disciplined, market-neutral implementations tend to benefit when it does.
• Understanding factors is as much about what to avoid as what to own. Deep factor research clarifies which exposures are genuinely rewarded and which are not. That understanding is precisely why certain exposures are deliberately excluded from the firm’s hedge fund programs today.

The Authors

Deepak Gurnani, Founder and Chief Investment Officer

Deepak Gurnani is the Founder and Chief Investment Officer of Versor Investments. Deepak has three decades of experience in applying quantitative methods to uncover alpha across global equity markets. Over the past decade, he has focused on pioneering the use of AI and alternative data in equity investing.

Ludger Hentschel, Founding Partner, Investment Advisor

Ludger Hentschel joined Versor Investments as a Founding Partner and is based in New York. Ludger has over 20 years of experience in quantitative research and investing.

Disclaimer: Past performance is not necessarily indicative of future results. Not an offer to sell or a solicitation of any type with respect to any securities or financial products.

Methodology: The research draws on broad, liquid universes. The futures work simulates roughly 100 liquid contracts across equities, fixed income, commodities, and currencies. The merger studies use a proprietary database of several thousand announced mergers across North America and Europe. The value and factor work covers large-cap and mid-cap equities across the US, Europe, Japan, the UK, Canada, and Australia. Sample periods run from the early 2000s through the early 2020s, with some series beginning in 1990.

The methods are systematic and hypothesis-driven. They include returns-based style analysis, rolling regressions to measure return convexity, machine-learning forecasts for merger outcomes, and market-neutral, industry-adjusted portfolio construction. Third-party data sources referenced across the papers include Bloomberg, S&P Global, Refinitiv, and Société Générale indices.