Current orthodoxy is that US stocks should rise into year-end. The rationales include rebalancing, history since WW2, and technical factors. Analyzed as complex systems, an alternative view of markets emerges.
I have built and personally employ an Observable Early Warning Signal (hereafter referred to as EWS) model to identify market states that are Unpredictable. I do this because I reject the hubris implicit in much of investing that seeks perfect predictability models. I equate attempts to develop perfect prediction models to paddling upstream. You can keep paddling harder and with more people in a canoe, but you will never reach your destination. Instead, I prefer to simply identify Unpredictable State, when the odds of surprise events are high. Accurate identification of Unpredictable States eliminates the cost of and reliance on models that are dependent on historical information and often fail in these moments.
My EWS model currently indicates that the heightened possibility of a more sustained downturn. I get into the detail below. . .
Complex systems research has shown that certain Temporal (time) and Spatial (space) conditions occur in many systems in Unpredictable States. Research has also shown a level of genericness to these conditions thus creating a body of EWS research available for application across different systems.
I have repurposed two key temporal concepts, Critical Slowing Down (CSD) and Flickering to financial markets. With regard to CSD, simply put, when a system approaches a transition, we can expect it to become increasingly slow in recovering from perturbations. This concept is difficult to observe in many natural systems, and mathematical modeling has replaced observation. It is rather straightforward to observe in financial markets which are a series of observable price points. My tool here combines observation and mathematical confirmation.
With regard to Flickering, It has been observed that certain complex systems that are “stochastic” or random are likely to flicker between their current position and a new position (states also referred to as basins of attraction), often well in advance of a transition point. In such conditions, the gradual climax of critical slowing down prior to a new state would not be observed.
EWS that I have discussed up to this point are based on changes in a system over a time series (temporal), and have attracted a lot of attention in academia. Newer work suggests that for systems that are not well mixed (such as drylands and wetlands), changes in spatial characteristics of the system can be relevant EWS as well. More generally, the spatial structure of a system can provide information about its degradation level, if any, from the current state. Relevant ideas include spatial correlation whereby in natural systems, neighboring units become more like each other, as a critical transition point approaches and patchiness whereby a natural system (such as shrublands and mussel beds) exhibits striking self-organized periodic patterns that display patchiness of some kind. As it relates to financial markets, my research has found the application of patchiness to be most relevant. I have identified two proprietary patch patterns of previously traversed price action between various data points as indicative of a relevant EWS.
Temporal and Spatial EWS have a possible inherent weakness which is that they appear less relevant for systems that are more cyclical and chaotic, as opposed to stable. There is debate as to which type of system financial markets are. To address this shortcoming, I have developed an additional EWS that we believe is more relevant in cyclical and chaotic systems.
Agent Behavior EWS
A fair amount of academic research has occurred in understanding the micro-behavior of agents in markets. In financial markets, the activity of participants or agents boils down to them assessing market history (or patterns) in some fashion over varying timeframes and applying various strategies in response. The range of strategies in an agent’s toolbox is a function of their accumulated knowledge, which adjusts over time. These strategies include buying, selling, doing nothing, or doing some nearly infinite variation and magnitude of those. As I have previously stated, sometimes agents come together to do something in self-organizing fashion, producing a critical transition. The basis for my EWS is the magnitude of agent strategies at work relative to market histories.
Bottom Line: Each of these EWS is signaling right now.
Time Cycle EWS
If, and only if Market State models confirm an EWS, as they are doing now, I conclude that we are in an Unpredictable State with a high probability of sudden, unpredictable events.
With that knowledge in hand, I then select an entry time window in the relevant time frame based on a Time-Cycle EWS which aligns with major unpredictable events driven by human behavior. My time cycle EWS framework builds upon the foundational work of theorists such as John Ehlers, Christiaan Huygens, and Martin Armstrong. Much of this work is not embraced by mainstream economists. I believe this is because acceptance of uncontrollable cycles flies in the face of the human need to determine our fate. I think this is a mistake, as these cycles are based on underlying and unrelenting human behavior. Given the relative difficulty in identifying EWS that map agent behavior, I gladly embrace cycle theory as an input to my EWS suite.
Bottom Line: The Time Cycle EWS suggests the period between Nov 1 and Nov 21–22 to be of great significance.
Significant Price Event Tool
My EWS Suite, while adept at identifying Unpredictable States and Entry Timing, does not (and is not intended to) produce a directional signal for position entry. With the market state known, the need for a complicated direction selection model is eliminated. I deploy a single, simple Significant Price Event tool to determine trade direction.
Bottom Line: This time around, that signal is DOWN.
Viewed through the lens of complex systems, US Stocks are sending an Early Warning Signal for a major downward turn. The first 3 weeks of November will be critical in validating and expressing this downward turn.
Any opinions or forecasts contained herein reflect the subjective judgments and assumptions of the author only. There can be no assurance that developments will transpire as forecasted and actual results will be different. The accuracy of data is not guaranteed but represents the author’s best judgment and can be derived from a variety of sources. The information is subject to change at any time without notice.