Mehrshad Motahari is a Lecturer in Finance at Bayes Business School (formerly Cass). His research interests lie primarily in the area of empirical asset pricing looking at drivers of market mispricing from both rational and behavioural perspectives. He also actively works on the implications of machine learning (ML) and firm environmental, social, and corporate governance (ESG) performance for asset pricing.
Mehrshad holds a PhD in Finance and an MSc in Accounting and Finance from Warwick Business School, the University of Warwick. Prior to joining Bayes Business School, he was a Research Associate in Finance at Judge Business School, the University of Cambridge, where he currently holds an Honorary Associate position.
Insights drawn from Mehrshad's research have been used to devise trading strategies by various major asset managers in the City. He also actively engages in consultancy projects related to systematic equity strategies and machine learning applications in asset management.
- PhD in Finance, University of Warwick, United Kingdom, Sep 2014 – Jul 2019
- MSc in Accounting and Finance, University of Warwick, United Kingdom, Sep 2012 – Sep 2013
- Certificate in Quantitative Finance (CQF), CQF Institute - Fitch Learning, United Kingdom
- Lecturer in Finance, Bayes Business School, Sep 2021 – present
- Honorary Associate, University of Cambridge, Aug 2021 – present
- Research Associate in Finance, University of Cambridge, Aug 2019 – Sep 2021
Memberships of professional organisations
- Founder and President, Cambridge Endowment for Research in Finance Alumni Society (CERFAS), Sep 2021 – present
English (can read, write, speak, understand spoken and peer review) and Persian (can read, write, speak, understand spoken and peer review).
- Asset Pricing
- Capital Markets
- Financial Institutions
- Financial Markets
- Investment Management
- financial services
- investment banking
- Bartram, S.M., Branke, J. and Motahari, M. (2020). Artificial Intelligence in Asset Management. CFA Institute Research Foundation. ISBN 978-1-952927-03-4.