Greg Fisher, PhD, CFA

My work centres on what I call Complexity Arbitrage — applying insights from complexity science to help asset managers achieve better outcomes in real financial markets. This means moving beyond conventional frameworks to see markets as evolving, uncertain, and patterned systems.

I work at the intersection of complexity science and finance, exploring how prices emerge from interacting narratives, how uncertainty can be managed alongside risk, and how non-ergodicity challenges traditional investment models. My work seeks to enhance how investment professionals navigate fundamental uncertainty, combining clarity of thought with practical action.

My early career was in economics and markets at the Bank of England, after earning a degree in economics from Cambridge. At the Bank, I was part of the team managing the staff pension fund, with responsibilities spanning portfolio management and strategic asset allocation. I later joined a macro hedge fund, focusing on translating macroeconomic insights into portfolio strategy, and qualified as a CFA charterholder.

Over time, I became increasingly sceptical of standard approaches in economics and finance — particularly their reliance on mechanistic thinking. This led me to pursue a PhD in complexity science, where I studied the emergence of informal institutions such as social norms and their influence on economic behaviour. Along the way, I developed strong coding skills for modelling and data analysis — capabilities that now underpin much of my applied work. The concepts I explored in the PhD — including emergence, feedback, path dependence, and uncertainty — have clear relevance for investment practice, including risk management.

Financial systems are often approached as if they behave like stable, predictable machines. In reality, markets are complex: dynamic and interdependent, shaped by feedback, shifting narratives, episodic regimes, and continual change.

My work is grounded in the idea that understanding such systems requires more than technical skill — it requires a deep ontology, pattern recognition, adaptability, and epistemic humility. These qualities are central to how I think about investing, modelling, and decision-making under uncertainty.

This perspective underpins all my work — and I invite you to explore how it might inform your own thinking about  decision-making under uncertainty.