Performance Evaluation at MorMag

A Framework for Assessing Returns, Risk, and Process

Evaluating performance in financial markets is inherently complex.

Returns alone provide an incomplete picture. Strategies may generate strong performance over certain periods while exposing capital to significant risk. Conversely, more stable approaches may produce lower returns but preserve capital more effectively over time.

At MorMag, performance evaluation is structured as a multi-dimensional framework that integrates return, risk, and process. The objective is not simply to measure outcomes, but to understand how those outcomes are generated and whether they are sustainable.

Beyond Returns

Raw returns are the most visible measure of performance, but they are insufficient on their own.

Two strategies with similar returns may differ significantly in terms of:

• volatility

• drawdowns

• consistency

• exposure to extreme events

For this reason, performance evaluation must extend beyond absolute returns to include the quality and characteristics of those returns.

Risk-Adjusted Metrics

A central component of the MorMag framework is the use of risk-adjusted performance metrics.

These include:

• Sharpe ratio: measuring return relative to total volatility

• Sortino ratio: focusing on downside risk

• Calmar ratio: evaluating return relative to drawdown

Each metric captures a different dimension of risk. Used together, they provide a more complete view of how efficiently returns are generated. Within the Market Scanner, these metrics are incorporated into the evaluation and ranking of opportunities, ensuring that risk is considered alongside return.

Distribution-Based Evaluation

Performance is not viewed as a single outcome, but as a distribution of possible outcomes. This reflects the probabilistic nature of financial markets.

At MorMag, evaluation considers:

• expected returns

• variability of outcomes

• tail risks and extreme scenarios

This approach aligns with the broader use of probabilistic modelling within the Quant Lab. Rather than asking whether a strategy performs well on average, the focus is on understanding the full distribution of outcomes.

Drawdowns and Path Dependency

Returns do not occur in isolation. The path taken to achieve them matters.

Large drawdowns can:

• reduce capital available for future investment

• introduce behavioural pressures

• disrupt long-term compounding

For this reason, drawdown analysis is a critical component of performance evaluation. Metrics such as maximum drawdown and the Calmar ratio provide insight into how strategies behave under stress.

Consistency and Stability

Sustainable performance requires consistency. Short periods of strong returns may be driven by favourable conditions rather than robust processes.

At MorMag, evaluation includes:

• stability of returns over time

• performance across different market regimes

• sensitivity to changing conditions

This helps distinguish between structural performance and temporary effects.

Regime-Aware Evaluation

Markets evolve across different environments. Strategies that perform well in one regime may struggle in another.

The MorMag framework incorporates regime awareness by:

• analysing performance across different volatility environments

• evaluating behaviour during market stress

• assessing robustness under changing conditions

This ensures that performance is not evaluated in isolation from the broader market context.

Process Over Outcomes

A key principle at MorMag is the distinction between process and outcome.

In probabilistic systems:

• good decisions can produce poor short-term outcomes

• poor decisions can produce favourable results

Performance evaluation therefore considers not only outcomes, but the quality of the decision-making process.

This includes:

• adherence to systematic frameworks

• consistency in applying models

• discipline in risk management

Integration with the Quant Stack

Performance evaluation is integrated across the MorMag Quant Stack.

• probabilistic modelling informs expected outcomes

• regime detection provides context

• the Market Scanner generates rankings and signals

Outputs from these components are evaluated collectively, ensuring that performance is assessed within the same framework used to generate decisions.

Continuous Feedback and Adaptation

Evaluation is not a static process. As new data becomes available, performance is reassessed.

This includes:

• updating metrics

• refining models

• adjusting assumptions

This feedback loop allows the system to evolve and improve over time.

Limitations of Evaluation

Performance metrics are based on historical data and model assumptions.

They are therefore subject to:

• non-stationarity

• model risk

• incomplete representation of future conditions

For this reason, evaluation is treated as a tool for understanding, rather than a definitive measure of future performance.

Conclusion

Performance evaluation at MorMag is structured as a multi-dimensional framework that integrates returns, risk, and process. By combining risk-adjusted metrics, probabilistic analysis, and regime-aware evaluation, the framework provides a comprehensive view of how performance is generated and whether it is sustainable.

In financial markets, outcomes are uncertain. The objective is not to measure performance with absolute precision, but to evaluate it in a way that supports disciplined, informed decision-making over time.