Unknown Unknowns in Financial Markets
The Risks We Cannot See, The Events We Cannot Predict, and the Limits of Human Knowledge
Most financial risk models begin with an assumption, the assumption is that the relevant risks are known.
Volatility can be measured. Correlations can be estimated. Economic scenarios can be constructed. Historical data can be analysed. Probabilities can be assigned to future outcomes. This framework works reasonably well for many forms of uncertainty.
However, it contains a profound limitation; not all risks are known, some risks exist outside current awareness entirely; these are the unknown unknowns.
Unlike ordinary uncertainty, where outcomes are uncertain but conceivable, unknown unknowns represent possibilities that market participants have not even considered. They are risks that do not appear in forecasts, models, stress tests, valuation frameworks, or consensus narratives because their existence is not yet recognised.
Most financial crises are not caused by risks that everyone understands, they are caused by risks that few people recognise until after the fact. At a deeper level, unknown unknowns reveal one of the most fundamental truths of investing:
The future is not merely uncertain, it is partially unknowable.
Understanding this reality is essential for navigating financial markets intelligently.
The Three Layers of Knowledge
To understand unknown unknowns, it is useful to distinguish between different categories of knowledge.
The first category consists of known knowns:
These are facts and relationships that participants understand reasonably well. Examples include current interest rates, reported earnings, observed inflation, and visible market prices.
The second category consists of known unknowns:
These are uncertainties that participants recognise but cannot predict precisely. Future GDP growth, election outcomes, corporate earnings, and central bank decisions often fall into this category.
Unknown unknowns represent a third category:
These are risks, events, relationships, and possibilities that remain outside awareness entirely.
The challenge is obvious, one cannot model what one does not know exists.
The Limits of Financial Models
Financial models are powerful tools; they help investors quantify risk, estimate value, optimise portfolios, and evaluate scenarios. However, every model operates within a predefined universe of assumptions.
Models require:
variables
parameters
relationships
probability distributions
historical observations
Unknown unknowns exist beyond these assumptions; because they remain outside the model's framework, they cannot be assigned probabilities easily. This creates a dangerous illusion, the precision of a model may create the appearance of comprehensive understanding when, in reality, significant uncertainty remains unobserved.
The greatest weakness of many risk models is not what they include, it is what they omit unknowingly.
Historical Lessons in Unknown Unknowns
Financial history provides numerous examples of unknown unknowns.
Before the global financial crisis of 2008, many institutions believed they understood mortgage risk reasonably well. Risk models incorporated historical default rates, housing market behaviour, and correlation assumptions.
What many models failed to recognise was the extent of systemic interconnectedness embedded within structured credit markets. Thus, the problem was not simply that probabilities were estimated incorrectly; the problem was that important structural vulnerabilities were not fully understood.
Similarly, before the COVID-19 pandemic, few market participants had explicitly modelled the possibility of a simultaneous global economic shutdown triggered by public health policy responses.
The event existed outside most conventional financial scenarios, these examples illustrate a crucial point:
Unknown unknowns frequently become obvious only after they occur.
Complexity and Emergent Risk
Financial markets are complex adaptive systems.
Complex systems generate behaviour that cannot always be predicted from their individual components. Interactions matter, feedback loops matter, network effects matter, small disturbances can sometimes produce disproportionately large outcomes.
This creates emergent risk.
Emergent risks arise not because individual components are inherently dangerous, but because interactions between components generate unexpected behaviour. Unknown unknowns often emerge from these interactions, the complexity of the system itself becomes a source of uncertainty.
The Problem of Historical Data
Much of finance relies on historical analysis. Historical data provides valuable information regarding how markets behaved previously.
However, unknown unknowns create an unavoidable limitation:
The future may contain events that have no direct historical precedent.
This does not mean history lacks value, it means history cannot capture every possibility. The future is not constrained solely by past observations. Novel conditions emerge, technologies evolve, institutions change, behaviour adapts.
As a result, historical datasets inevitably possess blind spots; unknown unknowns often emerge precisely within those blind spots.
Fat Tails and Extreme Events
One reason unknown unknowns matter so much is their relationship to extreme outcomes, many financial models assume relatively stable probability distributions. Unknown unknowns frequently produce outcomes residing far beyond these assumptions.
Examples include:
sudden liquidity collapses
systemic contagion
geopolitical shocks
technological disruption
regulatory transformation
unexpected feedback loops
These events often occupy the tails of financial distributions; while individually rare, their impact can be enormous. As a result, unknown unknowns contribute significantly to tail risk.
Reflexivity and Self-Created Surprises
Financial markets are reflexive systems. Participant beliefs influence actions, these actions influence prices, these prices influence beliefs. This creates feedback loops capable of generating outcomes that would not occur otherwise.
Importantly, reflexive systems can create surprises internally; namely, the market itself may generate unknown unknowns.
Examples include:
speculative bubbles
leverage spirals
liquidity crises
crowding cascades
These phenomena emerge through interaction rather than external shocks, the system creates risks that were previously invisible.
Why Unknown Unknowns Cannot Be Eliminated
A common mistake among investors is believing that sufficient data, computing power, or analytical sophistication can eliminate uncertainty. Unknown unknowns demonstrate why this is impossible. The challenge is not computational, it is epistemological; as there are inherent limits to what can be known.
No amount of analysis can fully anticipate every possible future state because the future itself is continuously evolving. Innovation creates new possibilities, human behaviour creates new dynamics, complex systems create new interactions.
Uncertainty therefore remains an unavoidable feature of reality.
Managing the Unmanageable
If unknown unknowns cannot be predicted, how should investors respond?
The answer is not prediction, instead the answer is resilience. As rather than attempting to forecast every possible event, robust frameworks focus on surviving unforeseen events.
This often involves:
maintaining liquidity
limiting leverage
diversifying intelligently
preserving optionality
avoiding fragility
The objective becomes reducing vulnerability to surprise. This represents a fundamentally different philosophy of risk management; as instead of asking, "What will happen?" The question becomes, "Can I survive what I have not imagined?"
The Role of Margin of Safety
The concept of margin of safety becomes especially important when dealing with unknown unknowns. Because not all risks can be identified, investors require buffers against uncertainty itself.
These buffers may take many forms:
valuation discounts
excess liquidity
conservative leverage
robust portfolio construction
flexible capital allocation
Margin of safety exists precisely because the future cannot be fully understood; its purpose is not to eliminate uncertainty, its purpose is to absorb uncertainty.
Unknown Unknowns and Adaptive Systems
Evolutionary systems offer an important lesson regarding unknown unknowns.
Biological organisms do not survive because they predict every threat perfectly, they survive because they possess adaptability. The same principle applies to financial markets; investors capable of adapting rapidly to changing conditions often outperform those relying solely on prediction.
Adaptability transforms uncertainty from a threat into a manageable challenge. In environments characterised by unknown unknowns, adaptability frequently matters more than forecasting accuracy.
The MorMag Perspective
At MorMag, unknown unknowns are viewed as an unavoidable feature of financial markets rather than a temporary analytical challenge.
Markets are interpreted as complex adaptive systems shaped by:
uncertainty
behavioural interaction
technological evolution
information asymmetry
systemic feedback loops
Within this framework, risk management extends beyond measurable volatility and known scenarios; it incorporates the possibility that important risks remain undiscovered.
This perspective influences:
portfolio construction
liquidity management
fragility analysis
regime assessment
capital preservation
The objective is not to eliminate uncertainty, instead the objective is to remain resilient despite uncertainty.
Intellectual Humility and Financial Survival
Perhaps the most important lesson of unknown unknowns is intellectual humility. Financial markets repeatedly demonstrate that even the most sophisticated participants possess incomplete knowledge.
Confidence and understanding are not identical.
The recognition that unseen risks may exist encourages caution, flexibility, and continuous learning. This humility is not weakness, it is realism. The most dangerous risks are often the ones nobody is discussing.
Conclusion
Unknown unknowns represent one of the deepest challenges within finance because they exist beyond conventional models, forecasts, and risk frameworks.
Unlike known uncertainties, they cannot be assigned probabilities easily because their existence has not yet been recognised. They emerge from complexity, adaptation, innovation, interaction, and the inherent unpredictability of evolving systems. Their significance extends beyond risk management, they reveal the limits of human knowledge itself.
At MorMag, this perspective forms part of a broader philosophy grounded in probabilistic thinking, complexity science, adaptive systems theory, and resilience-focused investing.
Financial markets will always contain surprises. Therefore, the objective is not to predict every surprise before it occurs, the objective is to build frameworks capable of surviving the surprises that nobody sees coming.
