In the financial market, the word "risk" is often used in a general way, but behind it lies two completely different states: quantifiable risk (Risk) and unquantifiable uncertainty (Uncertainty). This distinction was first proposed by economist Frank H. Knight in 1921 and remains a core framework for understanding market behavior and the limitations of decision-making. For traders, confusing the two will not only lead to strategy failure, but may also lead to systematic misjudgments.

1. Knightian distinction: risk has probability, uncertainty has no distribution

Risk (Risk): Refers to the fact that although the future outcome is unknown, its probability distribution is known or can be estimated. For example, the probability of heads when tossing a coin is 50%; historical volatility can be used to estimate the future price range of an asset. In this scenario, it can be managed through tools such as insurance, hedging, and diversification. Uncertainty (Uncertainty): It means that the possible outcomes of future events and their probabilities are unknown. For example, whether geopolitical conflicts will escalate, whether the central bank will suddenly change its policy framework, and whether new technologies will disrupt the industry structure. There is no historical precedent for such an event, and it cannot be reliably predicted using statistical models.

The financial market spends most of its time in the "risk" field, which can be managed with tools such as VaR and stress testing; but what really causes major losses are those black swan events that are misjudged as "high risk" but are actually "deep uncertainty".

2. The blind spot of modern financial models: simplifying uncertainty into risk

Mainstream financial theories (such as CAPM and Black-Scholes model) are based on the premise of "measurable risk" and assume that prices obey a known distribution (such as normal distribution). However, the real market frequently suffers from the phenomenon of Fat Tails - extreme events occur far more frequently than expected by the model.

Events such as the 2008 financial crisis, negative crude oil prices in 2020, and the British pension crisis in 2022 all result from models misclassifying structural uncertainties (such as leverage chain breaks and policy changes) as quantifiable risks. When all participants rely on the same set of "risk controllable" logic, the system becomes more vulnerable to the impact of uncertainty.

3. Differences in behavioral responses: Facing risk vs. Facing uncertainty

Psychological research shows that humans have completely different coping mechanisms for the two types of states:

Facing risk: Tend to use probabilistic thinking, accepting small losses in order to gain big profits (such as buying insurance, setting stop losses); Facing uncertainty: The brain enters the state of "Ambiguity Aversion", either being overly conservative (complete avoidance), or turning to narrative drive (such as believing that "this time is different") and giving up probability judgment.

Retail traders often fall into the latter situation during major policy changes or geopolitical crises: unable to model and unwilling to leave the market, they instead rely on media narratives or community sentiment to make decisions, resulting in maximum exposure when they should be most cautious.

4. Building resilience strategies: surviving in uncertainty

Since uncertainty cannot be eliminated, rational strategies should focus on enhancing system resilience (Resilience) rather than pursuing prediction accuracy:

Recognize cognitive boundaries: Make it clear which variables can be modeled (such as historical volatility) and which cannot (such as political mutations); Retain redundant capital: Avoid full positions and ensure the ability to re-engage after extreme events; Adopt anti-brittleness Weak structure: As Taleb said, design a "small loss but big profit" strategy - small losses most of the time, and non-linear gains in a few uncertain events; Reduce decision-making frequency: The higher the uncertainty, the less active intervention should be done to avoid forcibly interpreting the market during the information vacuum period.

True professionalism is not about "seeing through the future", but about staying humble in the face of the unknown and leaving room for the worst case scenario.

Conclusion: Uncertainty is the essence of the market, not a flaw

The reason why excess return opportunities exist in financial markets is precisely because uncertainty cannot be completely priced. But this also means that any system that claims to “completely control risk” implicitly ignores uncertainty. Wmax always emphasizes: Understanding the boundary between "knowable" and "unknowable" is the first step to long-term survival. Traders don’t have to eliminate uncertainty, they just need to make sure they don’t let it disappear.