Expected Value and Decision-Making

Evaluating Opportunities Under Uncertainty

Financial markets present a continuous stream of opportunities, each characterised by uncertainty.

No outcome is guaranteed. Even the most well-supported investment thesis may fail to materialise, while seemingly unlikely scenarios can occur.

In such an environment, decision-making cannot rely on certainty. Instead, it must be grounded in the evaluation of probabilities and outcomes. Expected value provides a framework for doing so.

The Concept of Expected Value

At its core, expected value represents the weighted average of possible outcomes, where each outcome is multiplied by its probability.

Rather than focusing on what is most likely to happen, expected value answers a different question:

what is the average outcome over time, given the distribution of possibilities?

This distinction is critical. An outcome that is unlikely may still be valuable if its payoff is sufficiently large, while a highly probable outcome may be unattractive if its potential return is limited.

Beyond Most Likely Outcomes

In financial markets, the most likely outcome is not always the most relevant.

Consider two opportunities:

• one with a high probability of small gains

• another with a lower probability of significantly larger gains

A focus on probability alone would favour the first. A focus on payoff alone would favour the second. Expected value integrates both dimensions. This allows for a more balanced evaluation of opportunities.

Distributions and Asymmetry

Expected value is closely linked to the concept of distributions.

Returns are not symmetric. They often exhibit:

• skewness, where upside and downside are uneven

• fat tails, where extreme outcomes occur more frequently than expected

Understanding the shape of the distribution is therefore essential.

At MorMag, opportunities are evaluated not only in terms of expected return, but also:

• the asymmetry between upside and downside

• the probability of extreme outcomes

• the stability of the distribution across conditions

This provides a more complete view of potential outcomes.

Conditional Expected Value

Expected value in financial markets is not static.

It depends on context:

• macroeconomic conditions

• market regimes

• liquidity and volatility environments

This leads to the concept of conditional expected value:

what is the expected outcome, given current conditions?

Within the MorMag framework, this is reflected in:

• regime-aware modelling

• dynamic probability estimates

• context-driven signal evaluation

This ensures that expected value is continuously updated rather than fixed.

Expected Value and the Market Scanner

The MorMag Market Scanner applies expected value concepts across a broad universe of securities.

Rather than selecting isolated ideas, the system evaluates:

• relative expected returns

• probability-adjusted outcomes

• risk-adjusted rankings

This allows for the comparison of opportunities on a consistent basis. The result is a structured view of the market in which capital can be allocated toward the most attractive expected value profiles.

Risk and Expected Value

Expected value does not eliminate risk. An opportunity with a positive expected value may still produce negative outcomes in the short term.

For this reason, expected value must be considered alongside:

• variability of outcomes

• downside risk

• exposure to extreme events

This reinforces the importance of combining expected value with broader risk management principles.

Discipline and Time Horizon

The effectiveness of expected value depends on time. Over short periods, outcomes may deviate significantly from expectations due to randomness. Over longer horizons, the distribution of outcomes becomes more relevant.

This highlights the importance of:

• maintaining discipline

• avoiding reaction to short-term noise

• allowing probabilistic edges to play out over time

Expected value is not about being correct in every instance, but about making decisions that are favourable on average.

From Theory to Practice

In practice, expected value is not calculated with perfect precision.

Estimates are based on:

• model outputs

• historical data

• probabilistic assumptions

These inputs are inherently uncertain. The objective is therefore not to calculate exact values, but to use expected value as a framework for structuring decisions.

Conclusion

Expected value provides a foundation for decision-making in uncertain environments. By integrating probabilities and outcomes, it allows investors to evaluate opportunities in a structured and disciplined way. At MorMag, expected value is not used as a precise calculation, but as a guiding principle.

It reflects a broader approach to markets:

decisions should not be based on certainty, but on the balance of probabilities and outcomes over time.