AiLA’s Uncorrelated Approach To Systematic Commodities

In the evolving landscape of systematic investing, true diversification remains elusive. Traditional strategies, especially in commodities, are increasingly dominated by trend-following and carry-based approaches, often leading to significant overlap and correlation among commodity quantitative investment strategies. We offer a compelling alternative, delivering returns that remain structurally uncorrelated even under extreme market conditions.

In today’s market environment, where demand for volatility carry strategies in commodities is strong, the need for true diversification in systematic portfolios is more important than ever. Relying too heavily on trend or carry leaves investors exposed to common risk factors.

We rigorously tested whether AiLA’s investment strategies exhibit correlation with conventional commodity trading advisor momentum strategies under extreme market conditions. The findings confirmed persistent non-correlation, even in stressed environments. This structural independence underscores the need for innovative approaches beyond traditional trend and carry strategies, offering investors access to a truly uncorrelated source of returns.

For investors, this represents a unique opportunity: a factor-based, systematic approach within the commodity asset class that operates independently of the widely adopted trend and curve strategies. This distinction is especially relevant in today’s environment where many portfolios are overexposed to volatility carry and directional trend risks. 

A common assumption is that any futures-based strategy inevitably resembles CTA products, which often exhibit high inter-strategy correlations, frequently exceeding 50%. However, AiLA’s design fundamentally differs. Built on distinct allocation principles, AiLA’s models have historically shown low correlation with standard CTA and quantitative investment strategies.

To empirically evaluate this distinction, AiLA researchers conducted a study with a contrarian aim: to force the strategies into behaving like conventional CTA strategies and observe how feasible this transformation is. Two complementary methods were used to test this:

Method 1: Detecting Model Bias Toward CTA Signals

This approach assessed whether specific AiLA allocation models exhibit a historical bias — positive or negative — towards traditional CTA momentum signals. Using a discrete signal system (-1, 0, 1) for both AiLA and CTA indicators, the team measured the conditional probability of a CTA signal occurring when an AiLA signal was present.

Findings indicated some persistent model-specific biases. However, even when constructing portfolios from models with the strongest positive or negative biases, out-of-sample correlation with CTA momentum strategies remained modest, typically within a 25% range. This limited impact underscores the distinct behavior of AiLA models, even when some signal alignment exists historically.

Method 2: Forcing Correlation via Portfolio Constraints

The second method introduced a correlation constraint mechanism, modifying AiLA’s allocation to intentionally increase alignment with a CTA strategy. This was done by minimizing the angular distance between AiLA’s original portfolio vector and one that also satisfies a maximum angular constraint from the CTA vector.

While the method successfully raised correlation under low constraints, it failed to maintain performance or achieve higher correlation targets (above ~0.4). Attempts to push correlation beyond moderate levels broke down entirely, highlighting that the underlying allocation signals are too structurally dissimilar to reconcile easily, even under direct optimization.

The consistent inability to force high correlation through either historical bias selection or portfolio constraint mechanisms reinforces a key conclusion: AiLA’s allocation approach is fundamentally different from CTA momentum or carry strategies. This makes AiLA’s strategies not a substitute, but a valuable complement for traditional systematic strategies.

In a world where many systematic portfolios rely heavily on a narrow set of common risk factors, AiLA’s structural independence provides a robust source of diversification. Especially in the commodity space, where conventional strategies often move in tandem, AiLA offers a new path, one that promises greater resilience through true differentiation.