Eyal Neuman is a mathematical finance researcher and co-director of the quantitative finance master’s program at Imperial College London. His work spans probability, stochastic control, market microstructure, price impact, optimal execution, multi-agent systems, and quantum computing in finance.
Eyal’s path began with industrial engineering at Tel Aviv University before his interest shifted toward mathematics and probability. He completed graduate research at the Technion, followed by postdoctoral work in Hong Kong and Rochester, New York. At Imperial College, he began combining theoretical research with practical financial problems through collaborations with academics, practitioners, and Capital Fund Management.
In this episode, Eyal joins us for an in-depth conversation about connecting academic mathematics with real financial markets, constructing useful models from complex trading problems, understanding market impact, and identifying where emerging technologies may offer genuine value.
Eyal explains why meaningful quantitative research often develops at the intersection of mathematical depth and practical relevance. Industry practitioners understand the problems they face but may lack the time to build rigorous models, while academic researchers can provide mathematical structure, analytical tools, and a deeper examination of the underlying trade-offs.
We discuss how complex market behavior can be translated into a tractable model. Eyal explains why researchers must identify the most important state variables without attempting to reproduce every detail of reality. Models that are too simple may miss the essential mechanism, while models that are too complicated can become impossible to solve, difficult to interpret, and vulnerable to overfitting.
The conversation explores stochastic control and its applications to portfolio construction, market making, and optimal execution. Eyal explains how researchers define an objective, model the evolution of relevant state variables, and derive a strategy that balances expected performance, trading costs, market impact, and risk.
We then examine price impact and the problem of executing large positions. Eyal discusses propagator models, statistical estimation, liquidity, execution horizons, and how previous trades can influence future price movements. We explore how an investor can divide a large order over time to reduce costs while still completing the required transaction.
The discussion goes deeper into multi-agent markets and signal crowding. When many participants trade using the same information, their collective order flow can reinforce the signal, increase market impact, and reduce the profitability available to each trader. Eyal explains how mathematical models can make these relationships more transparent and help quantify interactions that practitioners already recognize intuitively.
We also explore passive execution through limit orders. Instead of modeling every individual market participant, researchers can study the market’s average response to an order at a particular distance from the best bid or ask. This creates a more tractable framework for deciding how much liquidity to provide, where to place orders, and how to manage inventory and execution risk.
Eyal discusses the contribution of econophysics and empirical market laws, including the square-root relationship between traded quantity and price impact. We examine the difference between observing a statistical regularity and constructing a model that explains how the behavior of traders may generate it.
The conversation also covers quantitative finance education at Imperial College. Eyal explains how the program combines foundational subjects such as stochastic processes, derivatives pricing, and interest-rate models with newer areas including machine learning, deep learning, market microstructure, and quantum computing. Practitioner lectures, industry advisors, internships, and applied research help connect academic training with the skills required in professional quantitative roles.
Finally, we discuss the current state of quantum computing in finance. Eyal explains why practical use cases remain limited and uncertain, while highlighting potential applications in derivatives pricing, high-dimensional partial differential equations, optimization, and sampling rare distributions for risk scenarios.
A technical and thoughtful conversation on mathematical finance, industry collaboration, stochastic control, market impact, optimal execution, signal crowding, quantitative education, and the developing role of quantum computing in financial markets.
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The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.
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