In our previous blog post, we dissected the subtle complexities of a seemingly mundane act: throwing a ball in a park. Remarkably, this action, influenced by myriad contextual elements, illustrates a broader truth about our world. Now, imagine crafting a predictive trajectory in a sanitized lab environment, devoid of the unpredictable. Here, an act brimming with variables could potentially be transformed into an exact science.

Within such a controlled space, our ‘what-if’ experiments might stretch our findings to other similarly defined contexts. The outcome? Trajectories that, though almost identical, differ minutely due to minor variations in conditions. When these paths are overlaid, a dominant, highly probable course emerges, leaving only a trace of variation.

In essence, we would have established a seemingly universal rule of thumb, but with a catch. This only holds if we exclude elements that lie beyond our pristine experimental arena.

The trajectories from these controlled experiments converge tightly, representing an almost perfect prediction. Yet, reality presents a challenge. The entry of unforeseen, correlated variables—like an enthusiastic dog’s chase, a sudden injury, or the unpredictable sway of the wind—can lead to deviations far removed from any linear assumptions.

For those who play golf, they might notice how the trajectory of a ball materially varies through small variations, and that is just through small variations on the ball.

If we look at more realistic scenarios of ball flight we see that the ball does not travel through a homogenous substrate of air. They actually might travel through a correlated landscape of different environments. Turbulent zones, zones where playful dogs abound, birds and insects are abundant and a plethora of correlated conditions that have the potential to materially change the flight path so that the best prediction is way off. This correlated landscape has the ability to embed serial correlation into the flight path in clusters throughout the journey that ensures the path materially diverts from its simplistic route.

It isn’t just about the little nuances overlooked in our original model. Each stage of the ball’s journey is open to unexpected, correlated influences. This ever-looming potential for error? This is the terrain where trend followers find their edge.

Common wisdom might paint trend followers as precogs, but they are more so opportunists of unpredictability. They acknowledge the omnipresent shadows of uncertainty over projections. More profoundly, they understand that even the sharpest of predictions can be derailed by unexpected shifts, birthing trends that defy conventional linear perspectives.

Trend is a feature of change, and change is ubiquitous in complex adaptive systems. It’s a principle that shines particularly in the financial world. While many market players turn to predictive models, trend followers adopt a contrarian stance. They bet on the very likelihood of conventional forecasts going astray. When such predictions come up short, exposing the gap between anticipated outcomes and reality, trend followers stand poised to benefit.

Of course, this isn’t to suggest an infallible touch. There are instances when predictive algorithms hit their mark, momentarily side-lining trend followers. But their meticulous risk management often provides a buffer against such hitches. When they strike it right, the rewards are magnified, especially when unforeseen variables add to the mix, leading to a favourable skew in outcomes.

Navigating the fluid landscape of markets, teeming with their inherent unpredictability and intricacies, requires a leap beyond orthodox predictive methodologies. The journey mandates tenacity, an unencumbered spirit free from forecasting’s limitations, and a deep dive into real-world market dynamics. Embracing the inherent volatility and recognizing that markets are in perpetual flux are crucial. This continual ebb and flow, this ceaseless evolution, are but reflections of the innate nature of adaptive systems.

Trade well and Prosper

The ATS mob