Interconnected stock tickers symbolize inter-asset correlation with an upwards trend.

Trend Following Strategies: How to Optimize Your Investment Allocations

Mira Elwood in Business & Economy July 2025 • 4 min read.

"Unlock new profit opportunities by mastering the art of inter-asset correlation in trend following—strategies that go beyond simple diversification."

For decades, investors have sought ways to profit from market trends. Trend following (TF) strategies, which adjust market exposure based on past price movements, have become a popular tool for capturing gains across various time horizons. Yet, the profitability of these strategies is often debated, making it crucial to optimize their implementation.

Traditionally, fund managers build diversified portfolios to reduce risk and enhance profit, aiming to decorrelate individual TF strategies as much as possible. However, a groundbreaking study challenges this conventional approach, revealing that it can lead to suboptimal portfolios. The key lies in understanding and leveraging inter-asset correlations, which, when properly accounted for, can significantly improve risk-adjusted returns.

This article explores how to move beyond simple diversification by actively incorporating inter-asset correlations into your trend following strategies. We’ll delve into the principles of estimating trends more reliably and adjusting TF positions more efficiently, potentially unlocking new profit opportunities that traditional methods overlook.

The Math Behind Trend Following Optimization

Interconnected stock tickers symbolize inter-asset correlation with an upwards trend.

To understand the intricacies of optimizing trend following strategies, it's essential to grasp the underlying mathematical framework. A portfolio allocation problem for trend following strategies on multiple correlated assets involves analytical formulas for the mean and variance of the portfolio return. This article then constructs the optimal portfolio that maximizes risk-adjusted return by accounting for inter-asset correlations. The dynamic allocation problem for n assets is equivalent to the classical static allocation problem for n² virtual assets that include lead-lag corrections in positions of TF strategies.

Researchers have developed models that rely on the ability of market participants to assess market auto-correlation or, equivalently, the excess variance. Explicit persistence in asset returns is introduced to study trend following from a risk-reward perspective. Price persistence was modeled by adding a stochastic trend term to a Gaussian market model, and researchers derived analytical formulas for the mean and variance of the strategy profit-and-losses (P&L).

Simplifying assumptions: The model starts with simplifying assumptions of a Gaussian market and linear TF strategies to make the analysis tractable.
Analytical formulas: It derives analytical formulas for the mean and variance of the portfolio return under these assumptions.
Optimal portfolio construction: It constructs the optimal portfolio to maximize risk-adjusted return by accounting for inter-asset correlations.
Equivalence to static allocation: It shows that the dynamic allocation problem for \( n \) assets is equivalent to a static allocation problem for \( n^2 \) virtual assets, including lead-lag corrections.

The respective roles of asset auto-correlations and inter-asset correlations are investigated in depth for the two-asset case and a sector model. In contrast to the principle of diversification suggesting to treat uncorrelated assets, inter-asset correlations allow one to estimate apparent trends more reliably and to adjust the TF positions more efficiently. If properly accounted for, inter-asset correlations are not deteriorative but beneficial for portfolio management that can open new profit opportunities for trend followers.

Making Inter-Asset Correlations Work for You

The principle of diversification in portfolio management calls for investing in as many uncorrelated assets as possible in order to reduce a portfolio risk. The same principle is applied to trend following portfolios. Properly modeling the source of correlations can be beneficial as a mean to estimate apparent trends more reliably, to adjust the TF portfolio more efficiently, and thus to enhance the Sharpe ratio. Each strategy should incorporate information from other strategies. Each asset investment can be represented as a linear combination of all assets strategy signals.

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Everything You Need To Know

What are trend following strategies and how are they typically used by investors?

Trend Following (TF) strategies involve adjusting market exposure based on past price movements to capture gains across various time horizons. Investors traditionally use diversified portfolios to reduce risk and enhance profit, aiming to decorrelate individual TF strategies. However, this conventional approach may lead to suboptimal portfolios, as it overlooks the potential benefits of inter-asset correlations. Understanding and leveraging these correlations can significantly improve risk-adjusted returns, potentially unlocking new profit opportunities.

How does incorporating inter-asset correlations improve trend following portfolios, and why is this approach considered groundbreaking?

Incorporating inter-asset correlations into trend following strategies allows for more reliable estimation of trends and more efficient adjustment of TF positions. This is a departure from traditional diversification, which seeks to decorrelate assets. By properly accounting for how assets move in relation to each other, investors can enhance the Sharpe ratio and potentially unlock new profit opportunities. Researchers have found that inter-asset correlations are not deteriorative but beneficial for portfolio management.

What is the mathematical foundation for optimizing trend following strategies, and what are the key components of the models used?

The optimization of trend following strategies relies on analytical formulas for the mean and variance of portfolio returns. The models used construct an optimal portfolio that maximizes risk-adjusted return by accounting for inter-asset correlations. The dynamic allocation problem for n assets is equivalent to a static allocation problem for n² virtual assets, including lead-lag corrections in positions of TF strategies. Researchers also study trend following from a risk-reward perspective by introducing explicit persistence in asset returns and modeling price persistence with a stochastic trend term.

How do asset auto-correlations and inter-asset correlations individually impact trend following strategies, and what implications does this have for portfolio management?

Asset auto-correlations and inter-asset correlations play distinct roles in trend following strategies. While diversification principles suggest treating assets as uncorrelated to reduce risk, leveraging inter-asset correlations allows for more reliable estimation of apparent trends and more efficient adjustment of TF positions. Properly accounting for inter-asset correlations is not deteriorative but beneficial for portfolio management and can open new profit opportunities for trend followers, which indicates that each strategy should incorporate information from other strategies.

In practice, how can investors apply the principles of inter-asset correlation to their trend following portfolios to enhance returns and manage risk more effectively?

Investors can apply inter-asset correlations by incorporating information from other strategies into each asset investment. This means representing each asset investment as a linear combination of all assets strategy signals. Properly modeling the source of correlations allows for a more reliable estimation of apparent trends and a more efficient adjustment of the TF portfolio, enhancing the Sharpe ratio. By moving beyond simple diversification and actively incorporating inter-asset correlations, investors can potentially unlock new profit opportunities and improve the overall risk-adjusted returns of their trend following portfolios.