Execution and Position Management: A Systems Analysis of Turning Thesis into Trade

A trading thesis is a claim about the structure of a system. It states that certain conditions—float, short interest, borrow dynamics, catalyst timing—have arranged themselves in a way that makes a particular outcome more probable than not. But a thesis is not a trade. The gap between analysis and action is where most failure occurs. A correct thesis sized incorrectly can destroy capital. A correct thesis executed poorly can transform a winning edge into a losing outcome. Execution is the layer where analysis meets the market, where the framework encounters the reality of live price action, and where the psychology of decision-making is tested under conditions that make disciplined thought most difficult. Systems Thinking in Practice (STiP) offers a lens for understanding execution not as a set of mechanical rules but as a structural problem: how to design a decision process that remains coherent under uncertainty, pressure, and partial information (Sterman, 2000).

This article examines execution and position management through a systems lens. It argues that the decisions surrounding entry, stop placement, profit-taking, and the management of both losing and winning positions are not isolated choices but interconnected components of a single decision system. Each choice constrains the others. Position sizing constrains stop placement. Stop placement constrains entry timing. Entry timing constrains profit-taking. The trader who treats these as separate decisions is not executing a strategy. They are improvising, and improvisation under pressure is where cognitive biases exact their highest toll (Kahneman, 2011).

Entry as a Structural Decision

The decision of how to enter a position—all at once or in scaled increments—is often framed as a question of preference or style. The systems perspective suggests something different. Entry method is a structural variable that determines the risk profile of the entire trade. It sets the average price, the maximum exposure, and the relationship between the trader and subsequent price movement (Simon, 1957).

An all-at-once entry is the simplest structure. The full position is established at a single price, at a single decision point. There is no ambiguity about average cost. There is no subsequent decision to make about whether to add. The trade is either on or off. This simplicity is also the limitation. An all-at-once entry concentrates timing risk. If the market moves against the position immediately, the entire exposure is adverse. There is no mechanism for adjustment, no way to reduce the cost basis, no opportunity to reassess before committing further capital (Sterman, 2000).

The market microstructure literature explains why this concentration of risk is particularly acute in thin, low-float securities. Kyle (1985) models the price impact of informed trading, demonstrating that large orders move prices against the trader even before the trade is complete. The act of buying pushes the price up. The act of selling pushes it down. The trader who enters all at once in a thin stock is not merely taking on risk. They are actively creating it. The order itself becomes a market event, alerting other participants to the presence of a buyer and inviting front-running (Kyle, 1985).

A scaled entry distributes the decision across multiple points. A portion of the intended position is entered at the first signal. Another portion is added if the price moves favourably and the thesis confirms. A final portion is committed when the catalyst approaches or the structure reaches a critical threshold. This structure reduces the risk of entering at the worst possible price. It allows the trader to add to a thesis that is being validated and to withhold capital from one that is not. It spreads the timing risk across a sequence of decisions rather than concentrating it in one (Thaler, 1980).

The cost of scaling is that the trader is never fully positioned when the move begins. If the stock runs hard from the first entry, the remaining capital is unproductive. Scaling also introduces a subtle psychological risk: it can become a mechanism for avoiding commitment. The trader who always scales may be signalling that their conviction is not as strong as they believe. The decision to scale or not is therefore not merely tactical. It is diagnostic. It reveals something about the trader's relationship to their own thesis (Kahneman, 2011).

The choice between entry structures depends on the liquidity of the instrument, the volatility of the setup, and the proximity of the catalyst. In a low-float stock with wide spreads, an all-at-once entry risks moving the price against the trader. A scaled entry, executed carefully, may achieve a better average price. In a stock where conviction is high and the catalyst is imminent, hesitation carries its own cost. The decision must be made in advance, as part of the plan, not in the moment of execution (Meadows, 2008).

Stops as Balancing Loops

A stop loss is a structural mechanism for interrupting a losing trade. It is a balancing loop: it acts to return the system to a stable state by terminating a position that has moved beyond acceptable parameters. The stop is not a prediction about where the price will go. It is a commitment about where the trader will exit if the thesis is wrong (Sterman, 2000).

The distinction between a hard stop and a mental stop is the distinction between a structural constraint and an intention. A hard stop is an order placed with a broker. It executes automatically when the price reaches the specified level. No decision is required at the moment of exit. The loop is closed by the structure, not by the trader. A mental stop is a price level the trader has decided to exit at, but no order has been placed. The exit depends on the trader executing the decision in the moment. This is where the system is vulnerable. The same cognitive biases that caused the trader to enter a losing position will be active at the moment of exit. Loss aversion makes the loss feel unbearable. Confirmation bias suggests the thesis is still intact. Recency bias suggests the move against the position is temporary. The mental stop, which seemed firm when the trade was opened, becomes flexible under pressure (Kahneman and Tversky, 1979).

The structural defence is the hard stop. It removes the exit decision from the moment of maximum emotional pressure. The trader does not need to be disciplined at the moment of exit because the decision was made in advance, under conditions of relative calm. The hard stop is not a confession of weakness. It is an acknowledgment that the trader's decision-making capacity is compromised under pressure, and that the system should be designed accordingly (Simon, 1957).

The limitation of the hard stop is that it can be triggered by noise. In a thin, low-float stock, a brief spike can run through the stop level and trigger an exit that was not warranted by the underlying thesis. The price then recovers, and the trader is left without the position they still believe in. This is not merely a nuisance. It is a structural feature of trading in illiquid markets. Glosten and Milgrom (1985) model the bid-ask spread as the cost of trading with heterogeneously informed participants. In thin markets, the spread widens, and prices can move discontinuously. A stop placed too tightly is not a protection. It is a gift to the market makers, who will run the price through the stop and recover it before the trader can react (Glosten and Milgrom, 1985).

The compromise is a volatility-adjusted stop. The stop is placed at a level that accounts for the normal volatility of the instrument, rather than at a fixed percentage or a round number. This reduces the probability of being stopped out by noise while still providing protection against a genuine reversal. The stop distance and the position size are not separate decisions. They are two expressions of the same underlying choice: how much the trader is willing to lose if the thesis is wrong. A wider stop requires a smaller position. A tighter stop allows a larger position. The two must be solved together (Thaler, 1980).

Profit-Taking and the Management of Gains

The management of a winning position presents a different set of structural challenges. The fear that dominates the losing trade is the fear of loss. The fear that dominates the winning trade is the fear of giving back the gain. Both fears are forms of loss aversion. Both can distort the decision process. The trader who exits a winning position too early is not taking profits. They are responding to the same psychological pressure that makes losing positions hard to close (Kahneman and Tversky, 1979).

Partial profit-taking is a structural solution to this problem. It allows the trader to reduce exposure as the position moves in their favour, locking in some gain while retaining the possibility of further upside. The structure addresses the emotional pressure: some profit is secured, which makes it easier to hold the remainder through volatility. The trader is no longer all-or-nothing (Shefrin and Statman, 1985).

The disposition effect, identified by Shefrin and Statman (1985), is the empirical tendency to sell winners too early and hold losers too long. It is not a failure of discipline. It is a structural property of how humans evaluate gains and losses within the framework of prospect theory. The trader who understands this is better equipped to design a system that counteracts it. Partial profit-taking at predetermined levels is one such system. It commits the trader to a course of action before the emotional pressure of a live position can distort the decision (Shefrin and Statman, 1985).

The cost of partial profit-taking is that it caps upside on the portion sold. If the stock runs far beyond the point of the first sale, the trader has left money on the table. The decision to take partial profits must therefore be made in advance, as part of the plan, rather than in response to the emotional pull of the moment. Predetermined levels provide this structure. The trader decides, before entering, that a third will be sold at a certain price, another third at a higher price, and the final third held for the full thesis. The decision is made under conditions of relative calm, not under the pressure of watching a profit fluctuate (Sterman, 2000).

The alternative is to take profits based on the structure of the move. The trader exits when the tape suggests the move is losing momentum, or when the framework indicates the position is approaching a structural level where resistance is likely. This is more flexible but requires more judgement and more active management. The structural defence against early exit is the same as the defence against confirmation bias: the trader writes down, in advance, the conditions under which they will take profits. The written plan acts as a counterweight to the emotional pull of the moment (Meadows, 2008).

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Managing the Losing Trade

A position moves against the trader. The first question is not whether to exit. The first question is whether the thesis has changed. The distinction between a thesis that is failing and a thesis that is being tested is the distinction between noise and signal. The data tells the difference. Utilisation, lender depth, borrow fee—these are the structural variables that determine whether the mechanics still support the trade. The framework tells the trader where they are in the cycle. The tape tells them whether the current movement matches the structural signature of the stage they believe they are in (Sterman, 2000).

If the thesis is intact, the move against the position is noise. The position should be managed accordingly. If the thesis has changed, the position must be exited. The stop loss is the mechanism. A hard stop executes automatically. A mental stop requires a decision under pressure. The structural difference is the difference between a system that catches the error and a system that relies on the trader to catch it themselves (Simon, 1957).

There is a third possibility that deserves attention: adding to a losing position. Averaging down can be a valid strategy if the thesis is intact and the price has declined for reasons that do not affect the mechanics. But averaging down without a clear plan is not managing the trade. It is refusing to accept the loss. The distinction is structural. A planned addition is made because the thesis is stronger at the lower price. An unplanned addition is made because the loss is unbearable and the trader is trying to avoid it by doubling the bet. The two look similar in execution but are opposite in structure (Kahneman, 2011).

The defence is the written plan. The trader decides in advance whether they will average down, under what conditions, and to what maximum size. The plan turns a potentially emotional decision into a structural one. The emotion is still there. It is simply no longer in control of the decision (Shefrin and Statman, 1985).

Managing the Winning Trade

The management of a winning trade is often more difficult than the management of a losing one. The losing trade is unpleasant, but the decision is usually clear: the stop is there, and the thesis is either intact or it is not. The winning trade presents a more insidious problem. The fear of losing the gain can be stronger than the fear of taking the original loss. The trader watches the profit fluctuate and feels the pull to exit, to lock it in, to avoid the pain of watching it evaporate (Kahneman and Tversky, 1979).

The structural defence is the same as for the losing trade. The trader writes down, in advance, the conditions under which they will take profits. The plan may specify predetermined levels. It may specify structural conditions—a loss of momentum on the tape, a shift in the framework, a change in the broader environment. The point is that the decision is made before the pressure arrives. The trader is not deciding in the moment whether to hold or sell. They are executing a plan that was made under conditions of relative calm (Sterman, 2000).

The emotional risk in the winning trade is complacency. The position is working. The thesis is confirmed. The trader stops checking the data. The framework is no longer evaluated. The tape is no longer watched. But a system that is still feeding new information after entry is a system that is still telling the trader whether the thesis holds. The same discipline applies whether the position is winning or losing. The framework matters, not the P&L (Meadows, 2008).

The Structural Limits of Execution

Execution can manage the trader's decisions, but it cannot manage the market. The company can still do something irrational. It can dilute into a spike, destroying the setup. It can bury bad news at the worst possible moment. The broader environment can shift. A catalyst can be pre-empted by day traders who run the price up in anticipation and then sell on the news. These are not failures of execution. They are properties of the system within which the trader is operating (Sterman, 2000).

The honest position is that the trader cannot control these events. They can only manage their exposure to them. This is not a counsel of despair. It is a recognition of the boundaries of the decision system. The framework identifies the setup. The exposure strategy determines the involvement. The psychology determines whether the plan can be executed. The execution mechanics determine whether the plan is actually carried out. But the outcome is never fully within the trader's control. The market is a complex system, and complex systems produce surprises (Simon, 1957).

The trader who accepts this is not weakened. They are freed from the illusion that they can control the outcome. They can focus on what they can control: the process. The process is the thing that compounds. The outcomes are data. The distinction is structural, and it is the same distinction that separates the trader who survives from the trader who does not (Tetlock and Gardner, 2015).

Conclusion: Execution as a System

Execution is not a set of mechanical rules. It is a system of interconnected decisions, each constraining the others. Position sizing constrains stop placement. Stop placement constrains entry timing. Entry timing constrains profit-taking. The trader who treats these as separate decisions is not executing a strategy. They are improvising, and improvisation under pressure is where cognitive biases exact their highest toll (Kahneman, 2011).

The systems perspective reframes execution as a design problem. The trader is not trying to be disciplined. They are trying to build a decision structure that functions under pressure, that catches errors before they compound, and that separates the evaluation of process from the evaluation of outcome. The hard stop catches the error. The written plan counters the emotional pull. The sizing rule constrains the loss. The framework provides the external object of evaluation. The trader is not fighting themselves. They are redesigning their own decision system (Meadows, 2008).

The thesis is the claim. The execution is the structure that turns the claim into action. The outcome is the data that feeds back into the next iteration of the loop. The trader who understands this is no longer a victim of their own psychology or of the market's unpredictability. They are an engineer of their own process, and the process is the only thing they truly control (Simon, 1957).

References

Glosten, L.R. and Milgrom, P.R. (1985) 'Bid, ask and transaction prices in a specialist market with heterogeneously informed traders', Journal of Financial Economics, 14(1), pp. 71–100.

Kahneman, D. (2011) Thinking, Fast and Slow. New York: Farrar, Straus and Giroux.

Kahneman, D. and Tversky, A. (1979) 'Prospect theory: an analysis of decision under risk', Econometrica, 47(2), pp. 263–291.

Kyle, A.S. (1985) 'Continuous auctions and insider trading', Econometrica, 53(6), pp. 1315–1335.

Meadows, D.H. (2008) Thinking in Systems: A Primer. White River Junction, VT: Chelsea Green Publishing.

Shefrin, H. and Statman, M. (1985) 'The disposition to sell winners too early and ride losers too long: theory and evidence', The Journal of Finance, 40(3), pp. 777–790.

Simon, H.A. (1957) Models of Man: Social and Rational. New York: John Wiley & Sons.

Sterman, J.D. (2000) Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston, MA: Irwin/McGraw-Hill.

Tetlock, P.E. and Gardner, D. (2015) Superforecasting: The Art and Science of Prediction. New York: Crown.

Thaler, R. (1980) 'Toward a positive theory of consumer choice', Journal of Economic Behavior & Organization, 1(1), pp. 39–60.

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Regards,

Russell Larke

BA (Hons) Business Management | MSc Candidate (Systems Thinking)

Trading Beyond Charts

Narrative and Reflexivity in Financial Markets — When Stories Become Price Action

Financial markets have long been understood through the lens of fundamentals: earnings, cash flows, discount rates, and risk premiums. Yet price movements frequently diverge from what these metrics would predict — a stock with strong fundamentals can languish while a stock with weak fundamentals rallies spectacularly. This divergence points to something beyond the numbers: the role of narrative and reflexivity in shaping market outcomes.

Narrative, in this context, refers to the stories market participants tell themselves and each other about why prices are moving. These stories are not merely commentary on events; they become part of the events themselves. When traders believe a stock is about to squeeze, their buying activity can help create the very squeeze they anticipated. This is reflexivity — a concept most associated with George Soros, who argued that market participants' perceptions influence the fundamentals they are trying to perceive, creating a feedback loop between belief and reality [1]. As Soros described it, financial markets always provide a distorted view of the underlying fundamentals, but the degree of distortion varies over time; when markets become far removed from fundamentals, disequilibrium builds until a "reality test" forces a correction [2].

The reflexive loop operates through a well-documented mechanism. A price movement attracts attention. Attention generates narrative. Narrative attracts buying interest. Buying interest pushes price further. Price movement generates more attention. The loop reinforces itself. In the context of short squeezes, this mechanism is particularly potent: a trapped short position creates mechanical pressure for covering; that pressure generates price movement; the price movement attracts retail attention and narrative; the narrative brings additional buying interest; the buying interest tightens the squeeze further; the loop accelerates until it reaches a violent resolution [3]. GameStop's 2021 squeeze provides a clear empirical demonstration: Keith Gill's bullish thesis on Reddit, recognising the stock's high short interest, led many retail investors to follow his strategy, creating a feedback loop that significantly inflated the stock's price. As institutions that had shorted the stock were compelled to buy back at higher prices, herding behaviour — driven by fear of missing out rather than fundamental analysis — created a rational bubble that diverged from the "wisdom of crowds" principle [4].

This reflexivity is not noise; it is a structural feature of markets that emerges from the interaction between price, attention, and behaviour. Herbert Simon's concept of bounded rationality helps explain why narrative becomes necessary in the first place [5]. Market participants do not have perfect information or unlimited computational capacity; they rely on heuristics, social signals, and narrative to make sense of complex environments. Narrative is a cognitive shortcut — a way of organising information into a coherent story that guides decision-making under uncertainty.

From a systems perspective, narrative functions as a feedback variable that modulates the relationship between market structure and price. It is not an exogenous force acting on markets; it is endogenous to the market system. Narrative emerges from price action, amplifies it, and is in turn amplified by it. This recursive relationship is characteristic of complex adaptive systems, where agents' expectations shape the environment that shapes their expectations [6]. The reflexive loop is a classic example of a reinforcing feedback loop — one that amplifies movement in the direction it is already travelling.

This dynamic has been explored extensively by Robert Shiller, whose work on narrative economics emphasises how stories, transmitted through social networks, influence economic decision-making at scale [7]. Shiller argues that narratives are not just reflections of economic reality but are themselves drivers of economic outcomes, capable of propagating through populations like epidemics and shaping collective behaviour [8]. In financial markets, this means the spread of a narrative — whether about a stock, a sector, or the broader economy — can become a self-fulfilling prophecy, at least in the short term. Shiller further notes that narratives can be contagious, spreading through populations in ways analogous to biological epidemics, and that major economic events are often preceded by the diffusion of specific stories [9].

The efficient market hypothesis (EMH), long the dominant paradigm in financial economics, struggles to account for these dynamics. While EMH asserts that market prices comprehensively reflect all relevant information about an asset's intrinsic value, it encounters substantial difficulties in explaining anomalies observed in financial markets — notably the unpredictable behaviours seen in cryptocurrency markets and during short squeezes [4]. This failure is not incidental: EMH's assumption of unidirectional causality — that fundamentals determine prices — breaks down under reflexive conditions [10]. When causality reverses, models that assume linear cause-effect relationships fail to predict economic phenomena, as Hendry observed in his work on predictive failures in econometric modelling [10].

The distinction between narrative-driven moves and mechanically-driven moves has practical implications. Narrative-driven moves tend to be more volatile, with larger swings and less consistent stepping. They are more likely to reverse when buying interest dries up. Mechanically-driven moves, by contrast, show the signature of structural pressure: rising utilisation, shrinking lender depth, climbing borrow fees, and the characteristic stepping of a trapped short running out of room. The narrative may accelerate the mechanical move, but the mechanics are the foundation. Empirical research on retail trading forums confirms this distinction: analysis of the 200 most-mentioned stocks on Reddit's r/WallStreetBets found that spikes in mentions coincided with increased volatility, and that only a limited number of days saw these stocks influenced by retail trading frenzies, suggesting that narrative-driven moves are episodic rather than sustained [11].

The Adaptive Markets Hypothesis (AMH), developed by Andrew Lo, provides a framework for reconciling these observations [12]. The AMH proposes that financial markets are not always efficient but are highly competitive, innovative, and adaptive, varying in their degree of efficiency as investor populations and the financial landscape change over time. Intelligent but fallible investors learn from and adapt to randomly shifting environments, meaning market efficiency is not a static condition but a dynamic property that evolves with market conditions [12]. This perspective accommodates both the periods of relative stability where EMH holds and the episodes of reflexive amplification where narrative dominates.

This raises the question of whether narrative is ever "wrong" or "right." From a systems perspective, narrative is not a claim about objective reality; it is a coordination device. It aligns the expectations of market participants, enabling them to act collectively. A narrative that successfully coordinates buying interest is "right" in the sense that it produces the outcome it predicts. A narrative that fails to coordinate interest dissipates without effect. The truth of a narrative lies not in its correspondence to fundamentals but in its capacity to shape behaviour [1]. As Shiller noted, when people believe a recession is coming, they curtail spending and put entrepreneurial ideas on hold, making the recession more likely — a classic case of a narrative becoming self-fulfilling [13].

This insight has implications for how market participants should approach narrative. The goal is not to dismiss narrative as noise — that would be to ignore a real force in the market. The goal is to distinguish between narrative that is riding structural mechanics and narrative that is imitating structural mechanics. This distinction requires triangulation: comparing the narrative with the data on utilisation, lender depth, and borrow fees; examining the price action for the structural signature of a genuine squeeze; and positioning the stock within the broader cycle of accumulation, distribution, and resolution.

One of the most common errors in market analysis is confusing a good company with a good trade. A company with strong fundamentals may be a sound long-term investment, but that does not make it a good candidate for a short-term squeeze trade. Conversely, a company with weak fundamentals may possess precisely the structural characteristics — a small float, high utilisation, shrinking lender depth — that make it a compelling trade. The market does not care whether the observer likes the company; it responds to the structural conditions underneath. Narrative can blur this distinction by making a trade feel good, creating the illusion that the mechanics support it.

Narrative and reflexivity are not peripheral to market analysis; they are central to it. They explain why markets overshoot, why squeezes run further than fundamentals would predict, and why some moves fail despite compelling stories. They also explain why the same stock can squeeze once and not again, why momentum is self-reinforcing until it isn't, and why the market's collective attention is itself a scarce resource that shapes price discovery. John Maynard Keynes' observation that markets can remain irrational longer than participants can remain solvent captures a related insight — that narrative and sentiment can sustain mispricing for extended periods, and that timing matters as much as direction [14].

In summary, narrative is not noise. It is a feedback variable that shapes market outcomes. Reflexivity is not a deviation from efficient markets; it is a feature of markets as complex adaptive systems. Understanding when narrative is riding mechanics and when it is creating the appearance of mechanics is essential for reading the market as a system. The data reveals the mechanics. The tape reveals the price action. The narrative reveals the story. All three are real. All three matter. But only the mechanics provide the structural foundation for sustained movement.

References

[1] Soros, G. (1987). The Alchemy of Finance. Simon & Schuster.

[2] Soros, G. (2009). Keynote address at the Tenth Annual International Seminar on Policy Challenges for the Financial Sector. World Bank.

[3] GameStop: A Modern Case Study of George Soros' Reflexivity Theory in Action (2021-2024). Market Observer (2024).

[4] Exploring the Impact of Competing Narratives on Financial Markets II. SciTePress (2024).

[5] Simon, H.A. (1957). Models of Man: Social and Rational. John Wiley & Sons.

[6] Meadows, D.H. (2008). Thinking in Systems: A Primer. Chelsea Green Publishing.

[7] Shiller, R.J. (2017). Narrative Economics. American Economic Review, 107(4), 967-1004.

[8] Shiller, R.J. (2019). Narrative Economics: How Stories Go Viral and Drive Major Economic Events. Princeton University Press.

[9] Shiller, R.J. (2019). Interview on Bubbles, Reflexivity, and Narrative Economics. CFA Institute Enterprising Investor.

[10] Rigor and the Prescription of Causal Relevance in Finance and Economics. Journal of European Public Policy (2026).

[11] Modelling Financial Markets during Times of Extreme Volatility: Evidence from the GameStop Short Squeeze. MDPI (2022).

[12] Lo, A. & Zhang, R. (2024). The Adaptive Markets Hypothesis: An Evolutionary Approach to Understanding Financial System Dynamics. Oxford University Press.

[13] Shiller, R.J. (2019). Interview with the CFA Institute Research and Policy Center.

[14] Keynes, J.M. (1936). The General Theory of Employment, Interest and Money. Macmillan.

[15] Sterman, J.D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill.

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Regards,

Russell Larke

BA (Hons) Business Management | MSc Candidate (Systems Thinking)

Trading Beyond Charts

Mandates and Size: The Structural Constraints on Institutional Capital

Abstract

Institutional investors—funds, pensions, and asset managers—operate under constraints that retail traders rarely encounter. Two of these constraints dominate their behaviour: the mandate, a formal rulebook that defines the boundaries of permissible investment, and size, the sheer scale of capital that makes discreet execution impossible. This essay argues that institutional behaviour cannot be understood through the same lens as retail trading. Institutions are not simply larger versions of individual participants; they are structurally different actors whose decisions are shaped by organisational rules, fiduciary obligations, and the mechanics of moving money without moving the market. Using systems thinking and bounded rationality as analytical frames, the essay examines how mandates create boundary judgements that exclude otherwise attractive opportunities, and how size forces institutions to interact with markets as a patient, distributed process rather than a single decisive act. The result is a distinct market footprint—often visible as quiet, persistent price drift—that sophisticated traders learn to recognise not as a secret signal, but as the ordinary behaviour of capital constrained by structure.

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1. The Institutional Actor as a Different Species

Retail traders operate with a degree of freedom that is easy to take for granted. They can buy or sell almost anything, at almost any time, in any size their account permits. Their only binding constraints are capital and judgement. Institutional investors do not share this freedom. They are not individuals expressing a personal view; they are organisations managing other people's money under conditions that are legal, contractual, and structural. The difference is not one of scale alone. It is a difference in the kind of actor.

An institution is a system, not a person. Its decisions are the output of committees, mandates, risk frameworks, and compliance processes. The person executing the trade may have a strong conviction, but that conviction operates within a defined boundary. Understanding this is essential for any trader trying to interpret market behaviour. When institutional capital moves, it does so for reasons that are often opaque to outsiders—not because institutions are secretive, but because their decision-making process is structurally different from that of an individual.

Herbert Simon's concept of bounded rationality provides a useful frame. Simon argued that decision-makers do not optimise under perfect information; they satisfice, choosing the first acceptable option within the limits of their cognitive capacity and organisational context (Simon, 1957). For institutional investors, those limits are not merely cognitive. They are codified. The mandate is the organisational expression of bounded rationality: a formal acknowledgement that the fund cannot evaluate every possible investment, and therefore must restrict its attention to a predefined universe. That restriction is not irrational. It is adaptive. But it produces consequences that ripple through the market.

2. The Taxonomy of Institutional Capital

Before examining the constraints, it is useful to distinguish the main types of institutional investor, because they are not a monolith. Each category has a different time horizon, a different tolerance for risk, and a different set of legal obligations.

Pension funds and insurance companies are often described as “real money” accounts. They manage retirement savings and insurance premiums, and their primary obligation is capital preservation over very long horizons. They tend to be heavily regulated, highly diversified, and constrained by strict mandates. They are not typically chasing short-term trading profits; they are funding liabilities that may not come due for decades. Their presence in a stock signals patient, conservative capital.

Mutual funds and exchange-traded funds pool money from retail and institutional clients alike. They face daily liquidity demands—investors can redeem their units or shares at any time. This creates a structural vulnerability: if redemptions spike, the fund may be forced to sell assets regardless of the manager’s view. That forced selling is a real market force, and it is the reason mutual fund flows are watched closely as a sentiment indicator.

Hedge funds operate with far more flexibility. They can short, use leverage, and concentrate positions in ways that pension funds cannot. Their goal is absolute return, not relative performance against a benchmark. They are the institutional players most likely to act like aggressive short sellers, and their activity is often the source of the borrow demand and utilisation pressure that matters so much in low-float stocks. A hedge fund is not constrained by the same conservatism as a pension fund, but it is still constrained by its own mandate, investor agreements, and risk limits.

Sovereign wealth funds and university endowments occupy the far end of the time-horizon spectrum. These are pools of capital designed to last for generations. They can tolerate enormous short-term volatility because their liabilities are effectively infinite. Their investment decisions are often driven by macro themes and structural trends rather than quarterly earnings. Their presence in a market can be a powerful stabilising force, but their absence can also leave a vacuum.

Understanding this taxonomy matters because it prevents the retail trader from making a single, undifferentiated judgement about “institutional activity.” A hedge fund buying a stock is not the same as a pension fund buying the same stock. The hedge fund may be positioning for a short-term catalyst; the pension fund may be accumulating for a decade. The tape does not tell you which is which, but the context can. Learning to distinguish them is part of moving beyond surface reading.

3. The Mandate as Boundary

A mandate is a rulebook. It defines what the fund is permitted to hold, how concentrated a position can become, which sectors are allowed, and which risk profiles are off-limits. Some mandates are broad; others are narrow. All of them draw a boundary around the fund’s investable universe, and that boundary is not merely advisory. It is binding.

This is a boundary judgement in the systems thinking sense. A boundary judgement defines what is inside the system under consideration and what is left outside (Ulrich, 1983). The mandate is exactly such a judgement, made in advance, about what the fund will and will not consider. A pension fund may be prohibited from holding stocks below a certain market capitalisation. A fund of funds may be restricted to investment-grade bonds. An ESG mandate may exclude entire industries regardless of their financial attractiveness. These boundaries are not imposed because the excluded assets are bad. They are imposed because the fund’s objectives, risk tolerance, and legal obligations require them.

The consequence is that an institution can identify a genuinely attractive microcap stock, believe strongly in its prospects, and still be structurally unable to buy it. The absence of institutional buying in such a stock is not evidence that institutions disagree with the thesis. It is evidence that the thesis lies outside their mandate. Retail traders who interpret institutional absence as institutional disapproval are reading a boundary judgement as an opinion. That is a category error with real consequences for how they interpret market signals.

The mandate also creates path dependency. Once a fund is established with a particular mandate, its future actions are constrained by that original definition. Changing a mandate is difficult, requiring board approval, client consent, and often regulatory notification. The boundary becomes sticky. What starts as a narrow definition can persist for years, shaping the fund’s behaviour long after the original rationale has faded. The institution is not free to rethink its constraints each morning. It is locked into a structure that was designed for a different time and a different set of assumptions.

4. Size and the Problem of Execution

The second structural constraint is size. A fund moving tens of millions of pounds into a position cannot simply place one order. Doing so would move the price against itself before the order was even filled. The act of buying would alert the market, attract competitors, and drive the entry price higher. The larger the order, the greater the problem. This is not a minor technical nuisance. It is a fundamental constraint on how institutional capital can interact with the market.

Market microstructure theory formalises this. Kyle (1985) demonstrated that order flow has price impact, and that large orders must be broken into smaller pieces if the trader wishes to minimise the cost of that impact. The informed trader does not reveal their full position in a single transaction. They trade gradually, disguising their size within the ordinary flow of the market. That theoretical insight is now an everyday reality for institutional execution desks.

The result is that institutional buying is rarely visible as a single, dramatic event. It appears as a slow grind: a stock drifting steadily higher or lower over hours, days, or even weeks, with no obvious news attached. Volume is elevated but not explosive. Price action is persistent but not parabolic. The market is absorbing institutional flow, and the flow is being managed to minimise its own footprint. For a retail trader watching the tape, this pattern can be puzzling. There is no catalyst, no headline, no obvious reason for the movement. The movement is the reason. It is capital being worked into position.

This patient, distributed execution has implications for how one reads a chart. A sharp move on news is often retail-driven—fast, emotional, and quickly reversed. A slow grind is often institutional—deliberate, persistent, and structurally significant. The distinction matters. The same price movement can have very different meanings depending on its tempo and texture. Learning to distinguish them is part of learning to read the tape.

5. The Institutional Footprint

The combination of mandates and size produces a distinctive market footprint. Institutional accumulation is often quiet, gradual, and easily overlooked. It does not announce itself with a single large candle. It announces itself through persistence. A stock that refuses to fall despite bad news, that grinds higher on no news, that absorbs selling without breaking down—these are the subtle signatures of institutional participation.

This is not a secret signal. It is simply what happens when patient capital operates under structural constraints. The institution cannot buy all at once, so it buys over time. It cannot reveal its hand, so it moves quietly. It cannot exceed its mandate, so it operates only within its defined universe. All of these constraints shape the resulting price pattern. The pattern is not an attempt to communicate. It is the by-product of a system doing what its structure requires.

For the retail trader, recognising this footprint is valuable. It suggests that the move is supported by capital with a longer time horizon than the typical day trader. It suggests that dips may be bought, not sold. It suggests that the underlying accumulation is real, even if its cause is not visible. But recognition requires humility. The retail trader sees only the surface. The institutional trader sees the structure. The difference is not intelligence; it is information. The institution knows its own mandate and its own order flow. The retail trader must infer both from the tape. That inference is possible, but it is always incomplete, and it is always fallible.

6. Institutional Absence as a Signal

Just as institutional presence is a signal, so is institutional absence. A stock with no institutional participation is not necessarily a bad stock. It may be too small, too volatile, or too illiquid to meet typical mandate requirements. Many excellent small-cap companies operate entirely without institutional ownership for exactly these reasons. The retail trader who assumes that institutional absence means institutional disapproval is making an error. The institution may simply be unable to participate, not unwilling.

This has a surprising consequence. The very stocks that offer the most explosive opportunities—tiny floats, low prices, thin liquidity—are often the ones that institutions cannot touch. The institutional constraint leaves room for retail capital to move the price. A stock that an institution would love to buy, but cannot, is a stock where retail flows can have outsized impact. That structural fact is central to many of the short squeeze and low-float dynamics that this course will explore in later modules. The absence of institutional liquidity is not a flaw in the stock. It is a condition of its volatility.

Understanding this changes how one interprets market data. A stock with zero institutional ownership is not a warning sign by itself. It is a structural classification. It tells you who is not playing, and therefore who might be playing when the move arrives. The retail trader who understands mandates and size can read that classification correctly. The one who doesn’t sees only a blank space where the institutions should be, and draws the wrong conclusion.

7. Conclusion: Capital Constrained by Structure

Institutional investors are not simply larger retail traders. They are actors embedded in a web of structural constraints that shape every decision they make. The mandate defines what they can do. Size defines how they can do it. Together, these constraints produce a distinctive market footprint: patient, persistent, and often invisible to those who only see the chart.

A systems perspective reveals the deeper truth. The institution is not a person with an opinion. It is a system operating within boundaries, responding to incentives, and producing outputs that are the predictable result of its structure. To understand institutional behaviour, one must understand the system. To understand the market, one must understand the institutions. The chart is only the shadow. The structure is the object.

The trader who learns to see institutional footprints—who recognises the slow grind, the patient accumulation, the quiet persistence—moves closer to trading the market as it actually operates. The trader who ignores these signals, who reads every move as either random noise or dramatic intent, remains trapped in the surface. The difference is not skill. It is perspective. And perspective, once gained, is not easily lost.

References

Kyle, A.S. (1985). ‘Continuous Auctions and Insider Trading’. Econometrica, 53(6), pp. 1315–1335.

Simon, H.A. (1957). Models of Man: Social and Rational. New York: Wiley.

Ulrich, W. (1983). Critical Heuristics of Social Planning: A New Approach to Practical Philosophy. Bern: Haupt.

Regards,

Russell Larke

BA (Hons) Business Management | MSc Candidate (Systems Thinking)

Trading Beyond Charts

Why Being Wrong Early Is Better Than Being Right Late: Holding Pattern and Mechanics Together Under Uncertainty

Trading education typically presents the challenge of inconsistency as a problem to be solved — find the right pattern, apply more discipline, eliminate emotional interference. This essay argues that the discomfort traders experience when a setup fails is not a sign of inadequate discipline but a predictable consequence of holding two necessary but often conflicting perspectives simultaneously: the surface pattern and the structural mechanics beneath it. Drawing on bounded rationality, loss aversion, reflexivity, and the logic of falsification, the essay contends that the ability to be wrong early — to cut a position before a thesis has demonstrably failed — is not a concession to uncertainty but a structural discipline grounded in the limits of what any single model can capture. The argument connects the cognitive barriers to early loss-cutting with the systemic incentives that sustain pattern-based trading culture, and proposes that comfort with unresolved tension, rather than false certainty, is the appropriate epistemic stance for a practitioner operating in a complex, adaptive system.

(direct video / playlist)

1. Introduction: The Reproducibility Problem Revisited

Every trader with enough screen time has encountered the same phenomenon. A setup is identified, a position is taken, and the trade works. Weeks later, what appears to be the same setup produces a loss. The standard attribution in retail trading culture is psychological: the trader lacked discipline, let emotions interfere, or deviated from the plan. The possibility that the pattern itself contains no stable predictive structure — that the initial success and subsequent failure were both consistent with a process that is not reliably forecastable — is rarely entertained (Kahneman & Tversky, 1979).

The preceding modules in this series established two foundational points. First, that bounded rationality is the inescapable starting condition: no market participant has the full picture, and all decision-making occurs under constraints of incomplete information, limited cognitive capacity, and finite time (Simon, 1957). Second, that chart patterns are best understood not as causes but as symptoms — the visible shadows cast by underlying structural dynamics of liquidity, positioning, and reflexive feedback (Soros, 1987). The present essay addresses the practical and psychological consequence of holding both perspectives simultaneously. The pattern is real information. The structure is the deeper explanation. Neither is sufficient alone, and they do not always agree. The discomfort this produces is not a bug in the trader’s psychology. It is the appropriate response to a complex system that cannot be fully resolved by any single framework.

2. The Inescapable Tension: Holding Two Things That Do Not Fully Agree

A trader who has internalised the structural critique of technical analysis faces a specific cognitive bind. The chart shows a clean setup — a breakout, a retest, a level that has held multiple times. The structural conditions, however, tell a more ambiguous story: borrow availability is tighter than it appears, the macro backdrop has shifted, or the mix of market participants is different from the last time the pattern worked. Neither signal is definitive. The pattern suggests a trade. The structure suggests caution. The correct action is not to wait for one to overrule the other — that resolution may never arrive — but to act with the understanding that the thesis is provisional and likely to be wrong in ways that cannot be fully anticipated in advance.

This is the central discipline of the approach. It is not about achieving certainty. It is about maintaining what might be called structural humility: the willingness to act on incomplete information while simultaneously holding open the possibility that the entire frame of analysis may need to be discarded. This is psychologically demanding. It runs counter to the human preference for coherence and resolution (Kahneman, 2011). It is precisely the skill that retail trading culture, with its emphasis on confident pattern recitation and definitive calls, systematically fails to teach.

3. Cognitive Barriers to Being Wrong Early

If structural humility is the appropriate epistemic stance, why is it so rarely practised? The answer lies partly in the cognitive architecture that all decision-makers bring to uncertain environments.

Loss Aversion and the Disposition Effect. Prospect theory established that losses are experienced roughly twice as intensely as equivalent gains (Kahneman & Tversky, 1979). In trading, this asymmetry produces the disposition effect: the tendency to sell winning positions too early and hold losing positions too long (Shefrin & Statman, 1985). Closing a losing trade crystallises the loss and forces the trader to confront being wrong. Holding the position open preserves the possibility — however remote — of being proven right. Being wrong early means accepting the loss now, which is precisely what loss aversion makes most painful.

Overconfidence and the Illusion of Control. The evidence that individual traders trade too much and systematically underperform the market is well documented (Odean, 1999; Barber & Odean, 2001). Overconfidence leads traders to overestimate the precision of their information and the reliability of their judgements. A trader who believes they have identified a high-probability setup is unlikely to cut the position early on ambiguous evidence, precisely because overconfidence suppresses the perception of ambiguity. Being wrong early requires a calibration of confidence that most participants do not naturally possess.

Confirmation Bias. Once a position is taken, the mind preferentially seeks evidence that supports the thesis and discounts evidence that contradicts it (Nickerson, 1998). This is not a character flaw; it is a well-replicated feature of human cognition. The longer a position is held, the more mental effort has been invested in justifying it, and the harder it becomes to reverse the decision without experiencing cognitive dissonance. Being wrong early short-circuits this process before the investment of ego makes reversal disproportionately costly.

4. Bounded Rationality and the Seduction of Simple Heuristics

Herbert Simon’s concept of bounded rationality explains why pattern-based trading persists despite its unreliability. Decision-makers under constraints do not optimise; they satisfice — seeking solutions that are good enough rather than optimal (Simon, 1957). A chart pattern is a satisficing heuristic. It compresses a vast, multi-dimensional problem — the interaction of order flow, positioning, liquidity, sentiment, and macro conditions — into a manageable visual form. The compression is not useless. It allows fast decisions under pressure. But it is necessarily incomplete, and the incompleteness is invisible to the trader who has not been trained to look for what the pattern leaves out.

The structural approach does not discard the heuristic. It supplements it with a second, slower layer of analysis that asks what the pattern might be hiding. This is not a more efficient form of pattern recognition. It is a more demanding one, and it offers less immediate gratification. The pattern alone produces a clean, actionable signal. The structural overlay introduces ambiguity, delay, and the discomfort of unresolved tension. The market for trading education, which rewards confidence and simplicity, systematically selects against this kind of complexity.

5. Reflexivity: Why the Act of Trading Changes What Is Being Traded

George Soros’s theory of reflexivity provides a further reason why early loss-cutting is structurally rational rather than psychologically weak. In reflexive systems, participants’ perceptions shape their actions, and those actions reshape the fundamentals that perceptions are attempting to assess (Soros, 1987). A trader who identifies a pattern and acts on it is not a neutral observer. The act of trading changes the order book, influences the price, and alters the conditions that other participants are responding to. The pattern is not a fixed landscape; it is a moving target, partially constituted by the very behaviour it is supposed to predict.

This means that a thesis can be valid at the moment of entry and become invalid as a direct result of the entry itself — or of other participants’ reactions to it. Holding a losing position in the hope that the original thesis will eventually be vindicated misunderstands the nature of the system. The thesis is not a statement about a stable underlying reality. It is a contingent assessment of a dynamic, reflexive process that can shift for reasons that have nothing to do with the trader’s original analysis. Being wrong early acknowledges this contingency. Being right late denies it — and often compounds the loss in the process.

6. The Discipline of Falsification: Why Structural Humility Outperforms Conviction

Karl Popper’s principle of falsification holds that a scientific theory cannot be proven true, only tested and provisionally accepted until it is falsified by evidence (Popper, 1959). A trading thesis is not a scientific theory, but the same logic applies. A setup that cannot be falsified — that can be retrospectively explained no matter what the outcome — is not analysis. It is narrative construction after the fact. Chart patterns, as conventionally taught, are unfalsifiable: any failure can be attributed to poor execution, emotional interference, or a subtle nuance of the pattern that the trader missed. The framework itself is never questioned.

The structural approach, by contrast, demands that a thesis specify in advance what would disconfirm it. If the borrow conditions are supportive, the macro backdrop is neutral, and the catalyst is approaching, the thesis is that a squeeze is possible. If the catalyst passes and the price does not move, the thesis is wrong. Not the trader’s discipline. Not the execution. The thesis itself. Being wrong early means treating the absence of expected movement as information — information that the structural conditions were not, in this instance, sufficient to produce the anticipated outcome. That information is valuable. It refines the model for the next trade. Holding the position in the hope of being proven right delays the learning and increases the cost of acquiring it.

7. Conclusion: Comfort With Unresolved Tension

The argument of this essay can be stated plainly. The discomfort a trader feels when a setup and its underlying structure do not fully agree is not a problem to be eliminated. It is the appropriate cognitive state for a practitioner operating in a complex, adaptive, reflexive system where uncertainty is irreducible. The goal is not to resolve the tension — to find a way of making the pattern and the mechanics agree — but to become skilled at acting within it.

Being wrong early is the practical expression of this stance. It is the admission that the thesis was provisional, that the information available at entry was incomplete, and that the market has provided new data that contradicts the original premise. It is not a failure of conviction. It is a discipline of epistemic honesty — one that protects capital, accelerates learning, and keeps the trader alive long enough to encounter the conditions where the thesis is right.

The alternative — being right late — is seductive, culturally reinforced, and structurally dangerous. It preserves the illusion of competence at the cost of accumulating losses. It feeds the very biases — loss aversion, overconfidence, confirmation — that retail trading culture mistakenly treats as correctable through discipline alone, rather than as features of cognition that must be structurally managed. A framework that does not teach traders to be wrong early is not preparing them for uncertainty. It is preparing them to be right in their own minds, long after the market has told them otherwise.

References

Barber, B.M. & Odean, T. (2001). ‘Boys Will Be Boys: Gender, Overconfidence, and Common Stock Investment’. Quarterly Journal of Economics, 116(1), pp. 261–292.

Kahneman, D. (2011). Thinking, Fast and Slow. New York: Farrar, Straus and Giroux.

Kahneman, D. & Tversky, A. (1979). ‘Prospect Theory: An Analysis of Decision under Risk’. Econometrica, 47(2), pp. 263–291.

Nickerson, R.S. (1998). ‘Confirmation Bias: A Ubiquitous Phenomenon in Many Guises’. Review of General Psychology, 2(2), pp. 175–220.

Odean, T. (1999). ‘Do Investors Trade Too Much?’ American Economic Review, 89(5), pp. 1279–1298.

Popper, K. (1959). The Logic of Scientific Discovery. London: Hutchinson.

Shefrin, H. & Statman, M. (1985). ‘The Disposition to Sell Winners Too Early and Ride Losers Too Long: Theory and Evidence’. Journal of Finance, 40(3), pp. 777–790.

Simon, H.A. (1957). Models of Man: Social and Rational. New York: Wiley.

Soros, G. (1987). The Alchemy of Finance. New York: Simon & Schuster.

Regards,

Russell Larke

BA (Hons) Business Management | MSc Candidate (Systems Thinking)
Trading Beyond Charts

Bounded Rationality and the Boundary of the Market System

Herbert Simon's concept of bounded rationality holds that decision-makers do not — and cannot — process all available information before acting. Time, cognitive capacity, and the complexity of the system in front of them impose hard limits. Rather than optimising, actors satisfice: they select a decision that is good enough given what they can actually know, not the theoretically perfect one. This is the standard reading, and it is not wrong. It is, however, incomplete, because it treats "bounded" as a property of the individual mind rather than a property of a system boundary that the individual — often without noticing — has already drawn. 

(direct video / playlist)

Simon developed the concept in the 1950s as a corrective to the assumption, standard in classical economics, that agents are fully rational optimisers with complete information and unlimited processing power. His argument was not that people are irrational, but that the demand classical theory placed on human cognition was never realistic to begin with. Decision-makers, in Simon's account, act as satisficers: they search until they find an option that clears a threshold of "good enough," and then stop searching, rather than continuing until they have located the genuinely optimal choice. This distinction between satisficing and optimising is often flattened in popular use of the term, where "bounded rationality" becomes shorthand for "people make mistakes because they don't have enough information." That flattening loses almost everything useful about the concept.

It is also worth separating Simon's formulation from the behavioural economics tradition that followed it, particularly the heuristics-and-biases work associated with Kahneman and Tversky. That later tradition catalogues the specific, often predictable ways bounded reasoning deviates from a normative rational standard — anchoring, availability, loss aversion, and so on. It is a psychology of error. Simon's original concept is closer to a structural claim: given finite time and finite processing capacity, no useful theory of decision-making can assume unlimited information-gathering, regardless of whether the resulting decision happens to be "biased" in the technical sense. The systems reading developed here sits closer to Simon's original structural claim than to the biases literature, because the interesting move is not cataloguing where traders go wrong, but asking what determines the boundary of what any given trader is even in a position to consider.

From cognitive limit to boundary judgement

Read through a systems lens, bounded rationality stops being a fact about cognition and becomes a question about boundaries. An actor is only ever bounded relative to a system they have implicitly defined for themselves, and that definition is a choice, not a given, even when it is made unconsciously. This is the move Werner Ulrich's critical systems heuristics makes explicit: before asking whether a decision was rational, ask what was drawn inside the boundary of "the system" for that decision, and — just as importantly — what was deliberately or unconsciously left outside it. Ulrich's boundary questions were developed for social planning and public policy, where the stakes of a badly drawn boundary are visible and often severe: whose interests count, whose knowledge is treated as legitimate, who bears the consequences of a plan that never consulted them. They translate cleanly to markets, because a market is nothing more than a set of actors each operating with a different boundary around what counts as "the system" they are trading inside, and each treating their own boundary as though it were simply "the market," rather than one partial construction of it among several.

This reframing matters because it changes what "more information" can actually do. Under the popular reading of bounded rationality, the fix for a poor decision is more research — read the filing, check the float, watch the tape more closely. Under the boundary reading, more research inside an unexamined boundary just produces a more detailed picture of the same partial system. It does not correct the boundary itself. A trader who spends three hours refining their read of price action has not necessarily become less bounded; they have become more thoroughly informed about the specific, narrow slice of the system their boundary already permitted them to see. The boundary, not the depth of research within it, is very often the actual constraint.

Whose boundary, drawn where

Apply that to a trade. Most retail analysis draws the boundary around price, volume, and float — the visible mechanics of the chart. Bounded rationality, on that boundary, looks like a data problem: the trader didn't have enough information about the stock, and more research would have closed the gap. But widen the boundary to include the broker's margin requirements, the market maker's inventory risk, and the regulator's disclosure rules, and a different picture appears. Each of those actors is bounded too, by a boundary drawn around their role, not the trader's. The retail trader's "irrational" entry and the market maker's "rational" hedge can both be locally sound decisions, made by actors whose system boundaries simply don't overlap. Neither actor is short of information in any absolute sense. They are short of information relative to a boundary they never chose to widen.

This is closer to a SODA-style reading — Ackermann and Eden's Strategic Options Development and Analysis — than a purely economic one. SODA's premise is that group decision problems are rarely disagreements about facts; they are disagreements between individually coherent cognitive maps that were never reconciled, because nobody surfaced the boundary judgements each party was quietly operating under. The trader who says "the fundamentals didn't justify that move" and the market maker who says "the move was a rational response to short-dated hedging pressure" are not disagreeing about the facts. They are working from boundary judgements that never converge, because neither actor drew the system the same way — and, critically, neither one is aware that a boundary judgement was made at all. That invisibility is usually the actual problem, not the rationality of either party. A cognitive map, in SODA's sense, is not a distortion of the "real" system; it is a legitimate, internally coherent construction of it, built from a particular vantage point. There is no neutral vantage point from which to declare one map correct and the other mistaken. There is only the question of which boundary each map was drawn within, and whether that boundary was ever made explicit.

Why the boundary itself is not static

There is a further complication that a purely economic reading of bounded rationality tends to miss: the boundary that defines what an actor considers "the system" is not fixed for the duration of a trade. It moves as the situation develops, in the same way a Viable System Model's System 4 function is meant to continuously scan the environment and revise the organisation's model of what's relevant to it (Hoverstadt, 2020). System 4, in VSM terms, exists precisely because a viable system cannot survive on a single, fixed model of its environment; it needs a standing function whose job is to notice when the environment has changed in ways the current model does not account for, and to feed that back into how the system organises itself. An individual trader rarely has a formal System 4 function in this sense, but the absence of one is exactly the failure mode this section is describing: a thesis formed on Monday's boundary, and never revisited, behaves like a viable system that has switched off its environmental scanning.

A thesis built on Monday's boundary — this stock, this float, this catalyst — is not obliged to still hold by Wednesday, because the boundary itself has likely shifted, pulling in actors, constraints, or information that weren't inside the frame when the position was opened. New short interest data is published. A market maker's hedging book changes shape. A regulator opens an inquiry that was never part of the original picture. None of this necessarily falsifies the original thesis in the way a single piece of contradictory information would; it does something more structural, which is to change what the relevant system even consists of. Treating an entry decision as a fixed, defended position, rather than a boundary judgement open to revision as the system reveals more of itself, is one of the more expensive misreadings of what bounded rationality actually implies for practice. It converts a structural feature of decision-making under uncertainty — that the boundary moves — into a personal failure to have been right at the outset.

Boundary judgements and the pattern itself

This also explains something about chart patterns that is easy to miss from inside a purely technical framing. A chart pattern is, among other things, a record of what was left inside a particular boundary — price and volume, nothing else — compressed into a visual shape. It is not that the pattern is false. It is that the pattern is a boundary judgement rendered as a picture, and it inherits every limitation of the boundary that produced it. Two setups can look identical on a chart while sitting inside entirely different wider systems: different borrow availability, a different macro backdrop, a different mix of institutional participants. The pattern repeats because the visual boundary — price against time — is narrow enough to produce the same shape from genuinely different underlying conditions. Reading the pattern without asking what has been excluded from the frame that produced it is, in effect, mistaking one actor's boundary judgement for a complete description of the system.

The practical consequence is specific, not merely philosophical. Widening your own boundary judgement — deliberately asking who else's constraints are shaping this price, and why their bounded rationality might look nothing like yours — does more to explain an apparently "irrational" move than assuming someone, somewhere, simply lacked information. The information usually existed. It just sat inside a different actor's boundary, governed by a different set of constraints, and was never going to be visible from where the original decision was made. The discipline worth building is not "gather more data." It is "ask whose boundary you are currently trapped inside, and what you would see if you deliberately moved it" — and to keep asking that question for as long as the position is open, not only at the moment it was opened.

How bounded decisions aggregate into a feedback loop

None of this means the individual boundary judgement is the end of the story. Boundaries matter for a second reason that a purely cognitive account of bounded rationality has no real way to capture: bounded decisions do not stay isolated. They aggregate, and the way they aggregate can produce dynamics that no individual actor intended or fully understood while it was happening. A short squeeze is the clearest illustration available in markets. No single short seller decides to trigger a squeeze. Each one is making a locally bounded decision — cover now, at this price, given this margin call, this borrow cost, this risk limit — using information and constraints specific to their own boundary. None of them is reasoning about the aggregate effect of everyone else doing the same thing at roughly the same time. And yet the sum of those individually bounded, individually reasonable decisions produces exactly the reinforcing feedback loop that defines a squeeze: covering pushes price up, which forces more covering, which pushes price up further, each step driven by actors who were never modelling the loop itself, only their own narrow slice of it.

This is a genuinely systemic property, not a cognitive one. It cannot be explained by saying any individual actor was insufficiently rational, because each actor's decision was perfectly sound relative to their own boundary. The loop emerges from the structure of how many separately bounded actors are coupled to the same price, not from any failure of reasoning inside a single mind. This is precisely the kind of phenomenon that a boundary-based reading of bounded rationality is equipped to explain and a purely cognitive one is not: the interesting object of analysis stops being any individual trader's decision quality, and becomes the structure connecting many differently bounded decisions to one another. Understanding a squeeze, in other words, is less about diagnosing anyone's rationality and more about mapping whose boundaries are coupled to whose, and how tightly.

A working boundary audit

Turned into something usable rather than only descriptive, Ulrich's boundary questions suggest a short, repeatable audit that can be run against a position, ideally more than once across its life rather than only at entry. Four questions do most of the work. Who is the boundary of this analysis actually built around — is it drawn around the chart, around the company, or around the full set of actors with a stake in the price? What has been placed outside that boundary that could plausibly move the price regardless — borrow availability, a regulator's attention, a market maker's inventory position, a fund's mandate constraints? Whose bounded rationality is currently doing the most work in this move, and is it the same actor whose bounded rationality was doing the work when the thesis was first formed? And finally, if the boundary were deliberately widened to include the actor currently most affected by this price and least visible from the original vantage point, what would the position look like from there?

None of these questions produce a number, and none of them replace the mechanical analysis — the float, the borrow, the catalyst — that any position still needs. What they do is make the boundary itself an object of deliberate attention rather than something absorbed unconsciously along with whichever framework was used to first find the setup. Given that the boundary, not the depth of analysis inside it, is very often the actual constraint on a bounded-rational decision, that shift in attention is not a peripheral addition to the analysis. It is closer to the analysis that was missing.

This is explored further, with direct application to trading decisions, in the video above.

Regards,

Russell Larke

BA (Hons) Business Management | MSc Candidate (Systems Thinking)
Trading Beyond Charts