AIM Theory of Causality

Part I: A Framework for Navigating Complexity

Housekeeping: Consiliences

The goal of this essay—the first part in a series—is to continue along the quest of introducing consilient ideas to the realm of trading and investing. Think of it like building out the speculator’s toolkit from first principles. Once I have introduced my various frameworks, I can get busy discussing how they play out real-time on the discretionary side and how they can be modelled stylistically and quantitatively.

Preamble: The Phylogeny of Philosophy

My inspiration for this essay comes from Causality, Probability and Medicine, by one of my personal favourite living philosophers Donald A. Gillies, a philosopher and historian of science and mathematics. Interestingly, Gillies’ doctoral advisor was Imre Lakatos, the late renowned philosopher of mathematics and science; and his academic advisor was the late, great philosopher of science Karl Popper, who is one of my greatest intellectual influences. Accordingly, Gillies’ intellectual genetic lineage of doctoral advisers comes from the greats and can be traced to Gottfried Wilhelm Leibniz and Max Planck. Additionally, it is no surprise that Soros’ concept of the theory of reflexivity is useful within this framework, since the palindrome was a student of Popper.

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Introduction: Do You Know What Caused that Move?

Causality in financial markets is a vexing subject. In the financial market machine, causality stands as a notoriously elusive quarry. Since it is multivariate and recursive and is not easily amenable to reductive analysis. Just when you think you have distilled it down into a deterministic set of rules, it tends to confound one even further with counterintuitive moves. Hence, Taleb calls it “macrobullshit”!

What truly causes markets to move? Is it a sudden change in monetary policy that sends bond yields scrambling, or is it the subtle interplay of algorithmic trading strategies that surge at different inflection points? Perhaps it is the mere rumour of a geopolitical disruption that somehow sets currencies and commodities adrift. We might observe a sudden political shock rattling investors’ nerves, or a seemingly orchestrated tightening of liquidity by institutional actors; yet the underlying cause-effect relationship often remains maddeningly opaque. At times, it seems as though collective psychology itself, the old interplay of fear and greed—and narrative—can move mountains of capital without a single measurable cause.

In markets, causality rarely arrives gift-wrapped with a neat label. Instead, we face a bewildering ecosystem of catalysts, triggers, and feedback loops. To navigate such complexity, consider the AIM theory of causality—Action, Intervention, and Manipulation—a conceptual tool for tracing the origin and evolution of market movements. AIM, while initially rooted in the philosophical study of knowledge and systems, proves remarkably adaptable to the realm of global macro trading and investing. The financial sphere is not merely a coldly rational marketplace; it is a complex, reflexive environment shaped as much by ideas and expectations as by fundamental supply-demand equilibria. By dissecting events through the AIM framework, traders and analysts gain a sharper lens through which to understand how cause within markets can cascade into criticality—or slowly oscillate into obscurity. More importantly, it allows one to better anticipate the subtle shifts that lead from one causal type to another, empowering strategic decisions in real-time.

AIM in a More Nuanced Context

AIM is comprised of three conceptual pillars:

1. Action: Actions are first-order causal effects. These are readily observable occurrences—transparent catalysts that appear in news headlines and official announcements. They are “seen” causes, such as a government declaring a fiscal stimulus package, a technology company reporting outstanding quarterly earnings, or a monetary authority adjusting policy rates.

2. Intervention: Interventions are second-order causal effects. These occur when an external force deliberately alters the marketplace’s conditions, introducing a novel variable into the system. Interventions might arise from central banks defending currency pegs or letting go—think euroswissie—governmental bodies imposing sudden capital controls—think Iceland, India and Russia—or sovereign wealth funds purchasing risky assets en masse to support local markets—think China. Interventions are more subtle and less predictable; they represent authoritative intrusions into the market’s natural equilibria.

3. Manipulation: Manipulations are third-order causal effects. This dimension extends beyond transparent motives, venturing into the shadows where deliberate distortions are engineered. Whether it’s high-frequency trading strategies being exploited by spoofing—think the flash crash—or political decisions to impose price caps on commodities—think LME Nickel—or a single-stock like MiMedx engaging in channel stuffing—see Viceroy’s MiMedx Greatest Hits; Manipulations mask true intentions. They distort price signals, obscure underlying value, and often require forensic analysis to detect.

It is crucial to recognise that these three categories interact and overlap. A market event may initially look like a pure Action, but hidden beneath it might be a subtle Intervention, and in turn, those conditions could foster Manipulations that feed back into the broader system. Thus, the AIM framework is not static; it evolves and interconnects temporally, mirroring the complexity of financial markets themselves.

Action: Beyond the Evident Catalyst

Actions are the events we encounter every day as market participants—interest rate decisions, regulatory changes, corporate earnings, or economic data—whatever might cross

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’s suite. At first glance, Action-based causality appears straightforward. Traditional economic theory often posits a neat mechanistic chain: a rate hike discourages borrowing, which slows growth and depresses equity valuations. In an idealised toy model, this linearity feels comforting.

But complexity rarely bows to simplicity. Markets are forward-looking, adaptive, and inhabited by agents who themselves reflexively anticipate others’ anticipations in a circular Keynesian beauty contest. A widely expected rate hike might paradoxically boost equities if investors consider the outcome more dovish than feared, or if traders had over-positioned for a bearish reaction. Similarly, a well-telegraphed corporate earnings beat might actually trigger a stock sell-off because investors deem it insufficiently strong relative to lofty expectations. These discrepancies underscore that Actions, while the most visible form of endogenous causal forces, are subject to layers of interpretation, reflexivity, and strategic behaviour.

Think of markets as ecologies of competing hypotheses, where each Action triggers a cascade of Bayesian updates in participants’ mental models. The initial catalyst may be clear, but its downstream cascades are filtered through individual experience, collective psychology, liquidity conditions, algorithmic triggers, and historical analogues. Here, we glimpse the wisdom of not treating Actions as isolated variables, but as initial conditions in a dynamic system. Understanding that complexity encourages traders to keep an open mind, to question the obvious, and to consider how the collective interpretation of an Action might deviate dramatically from simple textbook predictions.

Intervention: When External Forces Rewire the System

Interventions introduce new conditions that reshape the market’s playing field abruptly and unpredictably. Unlike Actions, which might be part of the market’s regular rhythm, Interventions function as outside jolts. Consider a scenario in which a central bank declares it will buy corporate bonds in massive quantities to stabilise credit markets. Such a move is neither an organically emergent Action nor a simple piece of data; it’s a deliberate alteration of structural parameters.

A historical example close to one’s heart is the abrupt abandonment of a currency peg when a central bank authority retracted its promise to defend an exchange rate: the Swiss National Bank’s (SNB) decision to scrap the EURCHF floor on Jan 15, 2015. The decision revealed just how dramatically an Intervention can upend what traders considered “settled” reality. Investors who had grown complacent, trusting in the SNB’s previously unwavering stance, found themselves navigating a market suddenly bereft of its anchor. The fallout—wild price swings, liquidity vacuums, margin calls and widespread losses—demonstrated the sheer potency of these externally induced transformations.

Interventions also serve as reminders of the broader political and institutional landscape in which markets are embedded. While economic theory might desire autonomous, frictionless exchanges, reality brims with policymakers, corporate players, regulators, and even subtle cultural biases that shape outcomes. In essence, Interventions challenge the notion of markets as closed systems, underscoring that the rules can be changed mid-game by actors who command outsized influence.

For a trader, the central question becomes: who can intervene, on what basis, and under what conditions? By paying close attention to the macro-institutional environment—the rhetoric of central banks, the historical tendencies of regulatory bodies, the fiscal idiosyncrasies of dominant states—one can detect patterns and probabilities. Such vigilance allows traders to anticipate the sort of Interventions that can abruptly alter risk-reward landscapes, turning what once were rational bets into perilous gambits, or vice versa; just like the euroswissie.

Manipulation: Shadows and Smoke

While Actions and Interventions are generally visible—whilst sometimes surprising—Manipulations lurk behind the curtains. They are deliberate attempts to engineer outcomes through deception or force, often disguised as ordinary market moves. These manoeuvres can be subtle: carefully placed spoof orders that nudge prices in a preferred direction, or large entities quietly influencing benchmark rates to produce more favourable contractual payoffs. They can also be grandiose and geopolitical, as when a resource-rich nation with a commodity monopoly strategically throttles exports to compete in trade wars against geopolitical rivals, like China with rare earths and critical minerals: gallium, germanium and antimony.

To understand Manipulation, one must acknowledge that markets are information ecosystems. Price signals, ideally, should reflect the aggregate knowledge and preferences of participants. But when a handful of powerful actors distort these signals, markets lose their informational clarity. Participants may chase noise, misread demand, or fail to grasp the true scarcity or abundance of key resources. The consequences are profound, not only for adverse selection against individual portfolios but for entire economies that rely on accurate price discovery.

Detecting Manipulation often demands skepticism, investigative rigour, and an intellectual agility capable of questioning consensus narratives. In a world increasingly dominated by high-frequency algorithms and opaque dark pools, patterns of price action that defy economic logic or deviate from well-established statistical norms warrant scrutiny. Persistent anomalies—say, recurring sudden spikes in illiquid hours, or puzzling cross-market correlations that surface without fundamental cause—serve as potential red flags.

The presence of Manipulation is a reminder that markets, for all their complexity, are human constructs. They are not purely mechanistic systems governed by impersonal laws. Instead, they remain vulnerable to concentrated power, perverse incentives, and clandestine collusion. For the professional investor, recognising this truth does not diminish the utility of markets; rather, it imparts a healthy wariness and a commitment to rigorous, evidence-based analysis.

Reflexivity: The Feedback Loops That Bind AIM Theory

An essential layer to overlay upon AIM is the concept of reflexivity: the notion that market participants’ perceptions and expectations shape reality, and that reality in turn reshapes perceptions in a continuous feedback loop. If traders believe a central bank’s rate hike will induce a downturn, they may reduce investment, curtail hiring, and reorder supply chains—thus manifesting the economic downturn that the central bank originally sought to forestall. In other words, market outcomes are not merely passively observed; they are co-created by the beliefs and behaviours of participants.

Reflexivity ensures that causality in financial markets is never a neat arrow running from event to outcome. Instead, it is a dense network of interacting influences, each node capable of transforming into another cause or effect. Actions taken by policymakers morph into psychological expectations; Interventions alter the very foundations upon which traders build models and strategies; Manipulations shift the informational landscape, prompting participants to adapt their heuristics and narratives. Over time, markets evolve, akin to complex organisms, guided not only by concrete data points but by the swirling streams of collective consciousness.

For a discretionary speculator, acknowledging reflexivity is both humbling and empowering. It overturns the simplistic notion of stable equilibria, repudiates the idea of a rational agent such as the archetype homo economicus, and encourages a more dynamic, probabilistic, and doxastically-open approach. Rather than relying solely on static models, one must embrace iterative cycles of hypothesis formation, testing, and revision—continually updating beliefs as new data and interpretations emerge. Market participants who grasp this can sometimes anticipate shifts in sentiment and narrative before they fully register on price charts, capitalising on the lag between changing perceptions and their tangible manifestations.

From Theory to Practice: Harnessing AIM for Strategic Advantage

How, then, can a practitioner leverage the AIM framework and its reflexive underpinnings to navigate complexity and discover edge?

1. When Action Dominates: In periods where the market’s immediate focus rests on transparent catalysts—such as monetary policy announcements, major economic data releases, or high-profile corporate reports—analysts can concentrate on understanding consensus intersubjective expectations. Historical analogues, sentiment indicators, and options implied probabilities can offer insights into how widely anticipated an outcome is. By determining the level of homogeneity/heterogeneity in market beliefs and distinguishing between consensus and contrarian interpretations, a trader might exploit overreactions or underreactions to Actions as they unfold.

2. When Intervention Dominates: When the possibility of a policy shift or state-driven market adjustment looms, awareness of institutional behaviours and political incentives becomes crucial. Studying central bank communications, historical policy responses to crises through scenario analyses, or the legislative environment surrounding fiscal expansions and trade barriers; can uncover patterns. Positioning ahead of predictable Interventions—such as a bailout in stressed credit conditions or currency support during a speculative attack—allows one to profit from the ensuing shifts in liquidity and sentiment.

3. When Manipulation Dominates: In contexts where price action defies rational explanation, one must become a detective. Mining large datasets, applying machine learning, or scrutinising microstructure anomalies can reveal hidden signals. Diversifying information sources, cross-referencing different markets, and adopting a skeptical stance toward superficially compelling narratives helps one identify when hidden hands are at work. In such an opaque environment, adopting flexible trade structures, using tight risk controls, and focusing on markets or instruments where transparency is higher can mitigate downside risk.

Importantly, the greatest opportunities often emerge from transitions between these realms. A straightforward Action, like a well-telegraphed interest rate hike, might trigger a series of Interventions if market stability falters, and these Interventions in turn could invite covert Manipulations by certain players aiming to exploit the new regime. Recognising these transitions allows a trader to stay one step ahead of the curve, profiting not just from the immediate catalyst but from the evolving narrative that follows.

Conclusion: AIM as a Compass in a Multifaceted Market

The AIM theory of causality enriches our understanding of financial markets by parsing complexity into Action, Intervention, and Manipulation—an intellectual scaffolding that, while imperfect, affords greater clarity in a domain prone to confusion. Markets are ecosystems of adaptive agents, incomplete information, feedback loops, and shifting equilibria. Deterministic cause-and-effect relationships we crave are rarely reducible. Instead, we encounter overlapping layers of intention, anticipation, and reinterpretation.

Yet, by applying AIM, we impose a measure of conceptual order onto this chaos. We see more clearly the difference between a scheduled policy announcement and a sudden, forceful reshaping of the market’s structure. We develop an instinct for when price signals are genuine and when they are tainted by hidden machinations. And we embrace reflexivity, acknowledging that our participation and judgements feed back into the systems we study.

In the final analysis, a trader’s success stems not only from deciphering causality but from learning how to profitably coexist with it. Each time we interpret an Action, prepare for an Intervention, or unravel a suspected Manipulation, we ourselves influence the flow of markets. Thus, applying AIM transforms us from passive onlookers into mindful participants. We step into the arena armed with a richer understanding that, while incomplete, brings us closer to navigating the vast complexity with confidence, skepticism, and strategic foresight.

Thanks for reading,

C.H-T.

Nb. Parts II, III & IV of this series on causality introduces an amendment to the AIM Theory, as well as provides a classification of action and intervention variables and discusses the quantification of the approach. Thereafter, we put it all together in a pluralistic theory of probability and causality.