Enhancing Positive Convexity in Managed Futures Using AI and Alternative Data
Institutional portfolios remain dominated by equity risk, so allocators keep searching for strategies that hold up when equities fall. Managed futures convexity has become harder to find from familiar sources, because many traditional trend following programs have drifted toward longer-term signals and weaker responses to sharp selloffs. In this recorded discussion, Versor Founder and Managing Partner Deepak Gurnani explains how AI, machine learning, and alternative data can be applied to cross-sectional equity index futures to pursue uncorrelated returns and positive convexity. We believe convexity can be engineered through the design of a strategy rather than purchased through a standing hedge. The conversation covers why trend following convexity appears to have declined, where differentiated sources of return may still exist, and the risks that come with adopting advanced statistical methods. It is a practical, non-promotional look at how a disciplined quantitative process approaches a problem that matters to allocators.
What We Cover
- Why traditional trend following has shown weaker convexity, especially during rapid equity declines
- How dislocations between cash and futures markets can serve as a differentiated source of return
- Why those dislocations tend to widen during periods of market stress
- How AI, machine learning, and alternative data are applied across signal generation and portfolio construction
- The main risks of advanced methods, including overfitting, data quality, and non-stationarity, and how they can be managed
Key Takeaways
- Positive convexity can be engineered, not purchased. A strategy can be built to deliver convexity through its design and return sources, in contrast to relying on a single hedge or option overlay that carries an ongoing cost.
- Dislocations between cash and futures markets are a distinct source of return. Different investor types and their conflicting objectives create dislocations that liquidity providers resolve over time, which a market-neutral approach can seek to capture.
- Dislocations tend to widen when markets are stressed. Because the gap between spot and futures prices often expands during equity selloffs, the opportunity set can grow at the moment equity portfolios are most exposed.
- Trend following convexity has structurally declined. As assets concentrated in longer-term trend signals, index-level trend following has reported weaker convexity, particularly when markets fall sharply rather than gradually.
- In AI-driven investing, experience and process discipline are the edge. Alternative data and open-source machine learning are now widely available, so the differentiator lies in framing the problem, controlling data quality, and avoiding overfitting.
The Presenters
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.
Disclaimer: Past performance is not necessarily indicative of future results. Participation in this webinar is limited to Qualified Eligible Participants (QEPs) as defined under applicable regulations. For informational purposes only. Not an offer to sell or a solicitation of any type with respect to any securities or financial products. This webinar was conducted in collaboration with Middlemark Partners.
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