Hi, I'm Tom.
I'm a quantitative strategist based in Singapore.
I develop trading strategies and risk management models for emerging market FX, precious metals, cryptocurrencies, and other markets where traditional quantitative approaches often fail. My work focuses on extracting signal from noisy data, modeling fragmented liquidity, and applying machine learning to sparse datasets.
Email
My expertise is frontier assets.
Frontier assets are markets characterized by fragmented liquidity, sparse data, and nascent infrastructure. Unlike established markets where decades of data enable reliable statistical models, frontier markets require different approaches. These environments demand specialized techniques for pricing, risk management, and signal extraction from noisy, incomplete data.
I have worked in both Europe and Asia.
I gained a unique perspective in my field, and have a deep understanding of the dynamics of financial markets in these regions. Working across London, Hong Kong, and Singapore informs my approach to frontier asset modeling and strategy development.
I teach and publish academic research.
I taught guest lectures in quantitative finance at universities in London and Hong Kong, focusing on mathematical finance, risk management, and frontier asset modeling. I also publish research in quantitative finance journals, contributing to the academic discourse on digital and illiquid asset management.
Academic Work
I trained as an electronics engineer before moving to quantitative finance.
This engineering foundation shapes how I approach financial problems: building robust systems, thinking in terms of signal processing, and questioning assumptions in uncertain environments.
Professional Bio
Tom is a quantitative analyst who specializes in research and risk management of electronic and illiquid assets. He has worked in London, Hong Kong and Singapore for years in foreign exchange, both in alpha research and market-making pricing as well as high-frequency trading.
In addition to his work, Tom has been invited as a guest lecturer in quantitative finance and is actively publishing articles in the field of digital and illiquid asset management.
Extended Bio
Tom J. Espel is a quantitative strategist with expertise in research and risk management of electronic and illiquid assets. He specializes in applied mathematics, market econometrics, and practical applications of machine learning to emerging financial markets, including digital assets.
He has worked in London, Hong Kong, and Singapore in currencies and commodities, focusing on alpha research, market making, and high-frequency trading. His work centers on frontier assets, markets characterized by scarce data and fragmented liquidity, which has given him deep insight into the challenges of quantitative modeling and risk management in nascent markets and DeFi. His current interests include market microstructure, liquidity and volatility modeling, and machine learning for sparse, noisy data.
Tom has been invited as a guest lecturer in quantitative finance at institutions in London and Hong Kong, sharing his expertise in mathematical finance and risk management. He has also published research in quantitative finance.
He holds an MSc in Mathematics and Finance from Imperial College London, an MEng in Electrical and Electronic Engineering from CentraleSupélec (Paris-Saclay University), and a BSc in Applied Economics from Paris Dauphine (PSL University).
In his spare time, Tom is a passionate photographer and enjoys various sports.
Professional Memberships
CISI
The Chartered Institute for Securities & Investment is the leading professional body for securities, investment, wealth and financial planning professionals.
MCSI (Member) No. 253637
CISI Level 3 Certificate in Derivatives in 2019
IEEE
IEEE is the world’s largest technical professional organization dedicated to advancing technology for the benefit of humanity. IEEE is a leading developer of international standards that underpin many of today's telecommunications, information technology, and power-generation products and services.
Member No. 93851893
Registered since 2015