Dr John Fry

Dr John Fry

Senior Lecturer in Applied Mathematics

Faculty and Department

  • Faculty of Science and Engineering
  • School of Natural Sciences

Summary

John Fry is Senior Lecturer in Applied Mathematics having previously worked at a number of different business schools and mathematics departments in the UK.

John has a PhD in Mathematical Finance (Econophysics) from the University of Sheffield, an MSc in Statistics from the University of Sheffield and a BSc in Mathematics and Statistics from the University of Newcastle-upon-Tyne. John has research interests spanning econophysics, mathematical finance, operations research, statistics and sports modelling.

Some of John's past work modelling Bitcoin markets has been very highly cited. John jointly won the Tom Fetherstone prize in 2019 for the best paper published in International Review of Financial Analysis in 2016.

John is available to supervise PhD students across applied mathematical finance, finance, sports modelling or business applications of mathematics and statistics.

Recent outputs

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Journal Article

Quantifying speculative-bubble effects in major European soccer leagues

Fry, J., & Binner, J. (2025). Quantifying speculative-bubble effects in major European soccer leagues. Economics letters, 248, Article 112208. https://doi.org/10.1016/j.econlet.2025.112208

Elementary econometric and strategic analysis of curling matches

Fry, J., Austin, M., & Fanzon, S. (online). Elementary econometric and strategic analysis of curling matches. Managerial Finance, https://doi.org/10.1108/MF-06-2024-0467

Customer satisfaction scores: new models to estimate

Fry, J., & Brint, A. (2025). Customer satisfaction scores: new models to estimate. Tourism Management, 106, Article 105030. https://doi.org/10.1016/j.tourman.2024.105030

An options-pricing approach to forecasting the French presidential election

Fry, J., Hastings, T., & Binner, J. (online). An options-pricing approach to forecasting the French presidential election. Journal of the Operational Research Society, https://doi.org/10.1080/01605682.2024.2334339

Faster identification of faster Formula 1 drivers via time-rank duality

Fry, J., Brighton, T., & Fanzon, S. (2024). Faster identification of faster Formula 1 drivers via time-rank duality. Economics letters, 237, Article 111671. https://doi.org/10.1016/j.econlet.2024.111671

Research interests

Econophysics; Mathematical Finance; Operations Research; Statistics

Awards and prizes

Tom Fetherstone Prize

2019 - 2019

Jointly awarded the 2019 Tom Fetherstone prize for the best paper published in International Review of Financial Analysis in 2016

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