Andrew Beer from DBi and Adam Butler from ReSolve Asset Management discuss the replication of managed futures strategies. They explore return-based and process-based replication, selecting markets for diversification, and the challenges of derisking and top-down investing. They also discuss the implementation process for replicating an index and achieving broad-based exposure to the hedge fund industry. Overall, they highlight the potential benefits and cost-effectiveness of replication in investment portfolios.
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Quick takeaways
Replicating managed futures can help reduce fees and provide diversification benefits for investors.
Determining the number of markets to trade in replication is a trade-off between capturing outlier moves and practical constraints like trading costs.
Incorporating a bottom-up approach in replicating managed futures strategies can provide transparency, better fitting of the index, and a comprehensive understanding of portfolio positions.
Deep dives
Replicating Managed Futures Strategies Through Top-Down Approach
The podcast episode explores the replication of managed futures strategies. It discusses the top-down approach where investors try to replicate the returns of managed futures indices without replicating the actual process. The guests, Andrew Beer and Adam Butler, share their beliefs that more investors should allocate to managed futures and that replication can help reduce fees. They discuss return-based replication, process-based replication, determining the number of markets to trade, and expectations for tracking error.
The Benefits of Replicating Managed Futures
The podcast highlights the importance of replicating managed futures for investors. It emphasizes that managed futures have zero correlation to stocks and bonds, making them a valuable addition to an investment portfolio. The guests explain that replication allows investors to access the returns and diversification benefits of the managed futures space without the need to invest in expensive hedge funds. They also discuss the need for diversification and the challenges of picking the perfect factor model.
Trade-Offs in Determining the Number of Markets to Trade
The podcast delves into the trade-offs involved in determining the number of markets to trade when replicating managed futures. The guests explain that while more markets provide the opportunity to capture outlier moves, there are practical constraints such as trading costs and tax treatment. They highlight that a smaller set of markets can still achieve a high degree of efficacy in replicating the broader managed futures space, as evidenced by their own models that use 10 and 27 markets, respectively.
Navigating Challenges and Risks in Top-Down Replication
The podcast acknowledges some challenges and risks in top-down replication of managed futures. It discusses the lagged effect of replication, where the process looks in the rearview mirror of managers' strategies. The guests highlight the potential limitations of replicating short-term correlation structures and the difficulty in predicting outlier moves. They note that replication is a bet on short-term correlation and that derisking decisions can be influenced by various factors beyond just trend signals. The discussion also touches on the noise and practical considerations involved in implementation.
The Importance of Bottom-Up Approach in Replicating Managed Futures Strategies
The podcast episode discusses the significance of incorporating a bottom-up approach in replicating managed futures strategies. With experts in the field highlighting the benefits of blending top-down and bottom-up strategies, Adam, one of the speakers, explains how they developed a bottom-up tilt in their approach. By including various feasible and known strategies used by CTA's over the years, they focused on modeling breakout strategies, time series momentum, and price to moving average across different markets and trend lengths. They used advanced statistical techniques to determine the weights of these micro models that best match the long-term returns of the CTA trend index. The bottom-up approach provided transparency, better fitting of the index, and a comprehensive understanding of the positions in the portfolio.
Tracking Error and the Challenges of Replication
The podcast delves into the concept of tracking error and the challenges associated with replication in the managed futures space. Both speakers emphasize that replication in this context is not about achieving exact correlation or tracking error to a theoretical index, but rather about generating long-term outperformance. They highlight the inherent noise and variability in short-term tracking error and the statistical nature of replication. While replication aims to get as close as possible to the benchmark returns, it acknowledges the presence of short-term underperformance and the need for focus on long-term goals. The podcast stresses the importance of delivering a replicating strategy that provides diversification and efficiency for investors, enabling them to navigate different market conditions and achieve their long-term investment objectives.
In this special episode of Flirting with Models, I’m joined by two guests: Andrew Beer of DBi and Adam Butler of ReSolve Asset Management.
Rather than my usual interview format, I wanted to foster a conversation about the replication of managed futures strategies. Specifically, I wanted to bring on two practitioners who both share the same high level beliefs – namely that more investors should allocate to managed futures, that managed futures are well suited for replication, and that replication can help dramatically reduce fees – but differ on the implementation details.
And it is in that disagreement that I hoped to highlight the different pros and cons as well as any embedded assumption in any of these replication approaches.
We discuss return-based replication, process-based replication, determining the number of markets to trade, expectations for tracking error, and more.
I hope you enjoy this episode with Andrew Beer and Adam Butler.
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