Momentum Trading's Struggle and Potential Due to Randomness and Complexity

In the ever-evolving world of commodity trading, understanding market dynamics is crucial for success. Last year, 2020, proved to be a testament to this, with trend following strategies demonstrating potential success using various techniques to extract alpha from markets.

One key factor that emerged was the importance of recognizing and adapting to market noise. Noise, in this context, is what blurs understanding and perception, making forecasting difficult, particularly in markets. As markets have become noisier over the past decade, resulting in increased complexity and challenges, the need for notable enhancements in modeling has become evident.

Measuring market noise can be helpful in terms of momentum trading or trend following, as it can warn of potential instability ahead. However, signal processing, used to de-noise market inputs, must be done carefully to avoid taking out structure and weakening the signal.

Signal processing techniques, such as those used in seismology, may help identify these market regimes, their onset, and their extent. This is because both seismology and the study of market behavior share a commonality: both seek to forecast the behavior of open systems.

Researchers affiliated with the Massachusetts Institute of Technology (MIT) have studied stock market behavior and developed methods for measuring market pressure useful for momentum trading and trend-following. Their work during the "Trump Rally" in 2017 and the subsequent report in August 2025 discussed critical aspects affecting markets, including generative artificial intelligence projects and their impact on investor sentiment.

Another significant factor influencing market behavior is nonlinearity. Nonlinearity in markets refers to the increased sensitivity to small changes in the environment of the process being observed. This can lead to sudden changes in market regimes that cannot be captured by volatility models or classical approaches to trend following.

In 2020, nonlinearity led to the proliferation of distinct market regimes within shorter and shorter timeframes. This was evident in the commodities market, which experienced a sudden and massive rally, a disjointed regime from the previous decade-long bear market. The market started off with a confident rally, only to crash in March, and make new all-time highs by June.

Specific techniques from nonlinear dynamics can help model the expected sensitivity of the price action when certain changes are detected in the market. Researchers like Salim Lahmiri, Gazi Salah Uddin, and Stelios Behiros have published research on the nonlinear dynamics of equity, currency, and commodity markets in the aftermath of the global financial crisis.

BH-DG Systematic Trading LLP, a company authorized and regulated by the United Kingdom Financial Conduct Authority, provides services to professional clients. It is important to note that this document is for information purposes only and does not constitute investment advice. Commodity trading involves substantial risk of loss.

Recognizing and adapting to these challenges can increase the likelihood of success for trend followers and other directional traders in the future. As markets continue to evolve, understanding and harnessing the power of nonlinearity and signal processing will be key to navigating the complexities of commodity trading.