Electricity Price Risk Exposure and Equity Valuations in Europe University of Strathclyde in United Kingdom

This PhD project at the University of Strathclyde Business School investigates how exposure to electricity price risk influences the valuation of European companies. The research aims to construct novel electricity risk indices for Europe using advanced textual analysis and financial econometrics, providing timely, country-specific indicators that link electricity-market risk to firm-level outcomes.

Recent disruptions in energy markets and regulatory changes have heightened electricity price volatility, impacting costs, profitability, investment, and market valuations for European firms. Despite its importance, there is currently no robust indicator that captures electricity-market risk and connects it to the financial performance of individual companies.

The project will leverage large language models, natural language processing, and extensive collections of newspaper articles from leading publications in the UK, Germany, France, Italy, and Spain. By generating comparative, cross-country measures of economic risk from multilingual text, the research addresses key challenges in economics and finance, such as detecting meaningful content across languages, classifying risk-related discussions, and constructing robust indicators from unstructured data.

Combining modern NLP tools with financial econometric methods, the student will assess whether these electricity risk indices accurately reflect firms’ exposure to energy-related risk and whether more exposed firms are valued differently by investors and experience distinct stock return dynamics. The project offers a unique opportunity to contribute to a highly topical research area at the intersection of energy economics, sustainable finance, text analytics, and empirical asset pricing.

Funding is available for UK students, covering all university tuition fees and providing an annual tax-free stipend for three years. International students are eligible to apply but must secure funding for the difference between home and international tuition fees unless they are exceptionally qualified, in which case additional funding may be provided.

Applicants should have a strong academic background in economics, finance, statistics, or a related quantitative discipline. Experience with econometric methods, machine learning, or natural language processing is desirable. The application deadline is April 30, 2026.

To apply, submit your application online via the provided FindAPhD project link. Prepare your CV, academic transcripts, and a cover letter outlining your suitability for the project. Contact the supervisor for further information if needed.