In this episode, the Get Stacked team, consisting of Rodrigo Gordillo, Corey Hoffstein, Adam Butler and Mike Philbrick delve into the intricacies of Return Stacking, market trends, and the impact of taxes on investment strategies. They provide detailed insights into their research and findings, discussing the implications of their work for the investment landscape.

Key Points

1. Higher interest rates do not necessarily reduce the efficacy of return stacking, as the strategy focuses on excess returns over the risk-free rate.
2. Tax considerations are significant when dealing with managed futures and commodities within return stacking strategies, but proper asset location can help mitigate tax burdens.
3. Combining top-down and bottom-up replication methods in trend-following strategies significantly reduces tracking error, providing a more reliable replication of the SocGen CTA Trend Index.

(0:00) Introduction to the topic of risk-free rates and episode overview

(2:36) Return stacking in a higher interest rate environment and tax considerations

(4:15) Trend replication research and fundamentals of excess returns

(10:18) Leveraging futures contracts for portfolio construction

(17:31) Importance of non-correlated return streams in investing

(21:38) Deep dive into tax implications of return stacking

(25:18) Tax efficiency comparison: Stacked strategies vs. traditional funds

(32:23) Enhancing trend replication strategies and decision-making

(37:36) Top-down vs. bottom-up approaches in trend replication

(42:01) Correlation, tracking error, and trend definition analysis

(50:54) Realized tracking error and volatility weighting in models

(56:26) Optimizing gross returns and turnover in trend models

(1:02:12) Trend lookback periods and their impacts pre- and post-2008

(1:07:28) Market-specific contributions to trend-following performance

(1:13:34) WTI crude, commodities, and correlation dynamics in trend models

(1:18:00) Sponsor: XY Capital

(1:18:37) Using extensive data for model training and market replication

(1:22:05) Universe selection's impact on tracking error and ensemble methods

(1:30:31) Validating design principles and preview of the next episode

(1:32:27) Additional resources for listeners and closing remarks