Simple replication of managed futures indices is slow and has high tracking error. A superior “informed replication” approach combines backward-looking index data with forward-looking trend system priors and active risk management, resulting in a more robust beta-like exposure.
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Speculation is often maligned as mere gambling, but it is a critical component for price discovery, liquidity, and risk transfer in any healthy financial market. Without speculators, markets would be inefficient. Prediction markets are an explicit tool to harness this power for accurate forecasting.
Unlike typical investors who chase performance, sophisticated institutions often rebalance into managed futures when the strategy is in a drawdown. They take profits after strong years (like 2022) and re-allocate capital during weak periods to maintain strategic exposure.
The most profitable periods for trend following occur when market trends extend far beyond what seems rational or fundamentally justified. The strategy is designed to stay disciplined as prices move to levels few can imagine, long after others have exited.
Instead of opaque 'black box' algorithms, MDT uses decision trees that allow their team to see and understand the logic behind every trade. This transparency is crucial for validating the model's decisions and identifying when a factor's effectiveness is decaying over time.
“Crisis Alpha” is not a guaranteed hedge but the result of a managed futures strategy successfully capturing extreme macroeconomic shifts. The strategy is fundamentally about following major macro themes, with a crisis simply being one of the most intense themes it can follow.
Contrary to the belief that indexing creates market inefficiencies, Michael Mauboussin argues the opposite. Indexing removes the weakest, 'closet indexing' players from the active pool, increasing the average skill level of the remaining competition and making it harder to find an edge.
Combining managed futures with equities in a single product makes the strategy easier for investors to hold behaviorally. However, this “smoother ride” comes at a cost: it dilutes the powerful, anti-correlated impact that a pure-play managed futures strategy can have during a significant market downturn.
Contrary to expectations, drawdowns in managed futures frequently occur when equity markets are performing well. The strategy's recovery periods, however, often coincide with equity market turbulence, highlighting its counter-cyclical nature and making it behaviorally difficult to hold.
Investors hesitant to buy assets like gold near all-time highs can use trend following for exposure. The strategy systematically enters prevailing trends and, crucially, provides a built-in, non-emotional exit signal when the trend reverses, mitigating timing risk.
MDT deliberately avoids competing on acquiring novel, expensive datasets (informational edge). Instead, they focus on their analytical edge: applying sophisticated machine learning tools to long-history, high-quality standard datasets like financials and prices to find differentiated insights.