Reflexivity offers a powerful perspective on financial markets as systems in which perception and reality interact through feedback loops. At MorMag, this understanding complements probabilistic modelling and quantitative analysis, providing a more complete framework for interpreting complex market behaviour.

Game theory offers a powerful lens through which financial markets can be understood as systems of strategic interaction. At MorMag, this perspective informs a broader framework in which markets are viewed as complex systems shaped by both statistical structure and strategic interaction.

Chess offers a valuable framework for understanding financial markets as systems of structured complexity, sequential decision-making, and strategic interaction. At MorMag, this perspective complements quantitative modelling and probabilistic analysis, providing a broader framework for navigating complex financial systems.

Behavioural biases play a central role in shaping financial markets. They influence how information is interpreted, how risk is perceived, and how decisions are made. At MorMag, understanding these dynamics complements probabilistic modelling and systematic analysis.

The Ellsberg Paradox highlights the role of ambiguity in decision-making. In financial markets, where ambiguity is often present, this has implications for both modelling and interpretation. At MorMag, recognising the distinction between risk and ambiguity informs a more comprehensive approach to uncertainty.

Black Swan events highlight the limits of prediction and the presence of uncertainty beyond measurable risk. At MorMag, this understanding informs the design of investment frameworks that prioritise resilience, asymmetry, and disciplined risk management.

Anti-fragility provides an alternative framework for portfolio construction in uncertain environments. Rather than relying solely on optimisation, it focuses on building systems that can withstand and adapt to variability and extreme events. At MorMag, this perspective supports a more resilient approach to investing.

Knightian uncertainty and Black Swan theory highlight the limits of probabilistic modelling in financial markets. Understanding these limitations is essential for navigating complex systems.

Black Swan theory emphasises the presence of rare, high-impact events that lie outside standard expectations. At MorMag, this understanding informs a focus on robustness, disciplined risk management, and the recognition that uncertainty extends beyond what can be modelled.

Knightian uncertainty highlights a fundamental constraint in financial markets: not all uncertainty can be measured. At MorMag, recognising this informs how models are used, how risk is managed, and how decisions are made.

Evaluating performance in financial markets is inherently complex, with returns alone provide an incomplete picture. At MorMag, performance evaluation is structured as a multi-dimensional framework that integrates return, risk, and process.

Risk-adjusted performance metrics provide essential tools for evaluating opportunities in financial markets. By relating returns to different forms of risk, the Sharpe, Sortino, and Calmar ratios offer complementary perspectives on performance.

Advanced quantitative systems provide powerful tools for analysing financial markets, but they do not eliminate uncertainty. Their outputs depend on assumptions, data, and changing conditions, all of which introduce limitations. Understanding these constraints is essential for using quantitative methods effectively.

Advanced sampling methods enable the MorMag Quant Lab to move beyond static modelling and toward a dynamic, probabilistic framework for analysing financial markets.

MDPs provide a formal structure for decision-making under uncertainty, aligning with probabilistic investment frameworks. POMDPs extend this into a more realistic domain, accounting for incomplete information and noisy observations.

Gibbs sampling provides a structured approach to sampling in multi-variable systems. This is done by by sampling each variable conditionally.

MALA provides a practical middle ground between simplicity and efficiency in probabilistic sampling. This improves sampling efficiency compared to purely random proposals.

Standard MCMC methods can struggle in high-dimensional spaces. HMC provides a more efficient alternative to traditional MCMC, particularly in complex modelling environments.

In some models, the likelihood function cannot be evaluated directly. Pseudo-marginal methods extend the reach of probabilistic modelling, allowing inference in complex environments where exact solutions are unavailable.

Many probabilistic models require sampling from distributions that cannot be evaluated directly. The Metropolis–Hastings algorithm provides a method for generating such samples through a structured acceptance process.