Always respectful and encouraging to all.
David P. Newton is Professor of Finance and Head of the Accounting, Finance and Law Division in the School of Management at the University of Bath. In this leadership role, he oversees research and teaching across accounting, finance, and law, contributing to one of the UK's leading groups in these areas. His research specializations encompass derivatives, mathematical finance, asset pricing, and related topics, with a focus on advanced computational methods, risk assessment, and market dynamics. Newton's work integrates innovative techniques such as deep learning and quadrature methods to address complex financial modeling challenges, including option pricing under various stochastic processes and volatility forecasting.
Newton has an extensive publication record in prestigious journals, demonstrating significant impact in the field. Key publications include 'Deep Learning of Transition Probability Densities for Stochastic Differential Equations' (2024, with Haozhe Su and Michael V. Tretyakov), which develops neural network-based approximations for transition densities in high-dimensional settings; 'Firm ESG Reputation Risk and Debt Choice' (2024, with Steven Ongena, Ru Xie, and Binru Zhao), examining how environmental, social, and governance reputation risks influence corporate debt decisions; 'Leveraged Loans: Is High Leverage Risk Priced In?' (2025, with Steven Ongena, Ru Xie, and Binru Zhao), analyzing leverage risk premia in syndicated loans; 'Predicting Expected Idiosyncratic Volatility: Empirical Evidence from ARFIMA, HAR, and EGARCH Models' (2024, with Chuxuan Xiao and Winifred Huang); 'Option Pricing via QUAD: From Black-Scholes-Merton to Heston with Jumps' (with Haozhe Su and Ding Chen); and 'Tail-Heaviness, Asymmetry, and Profitability Forecasting by Quantile Regression' (2021, with Hui Tian and Andrew Yim). These contributions advance quantitative finance, machine learning applications in asset pricing, ESG integration in corporate finance, and empirical volatility modeling, influencing academic discourse and practical financial analysis.