Profile
Dr Qian Li is a Teaching Fellow in the Institute for Digital Technologies at Loughborough University London, specialising in the application of AI and machine learning in economics and finance. Her academic background combines advanced macroeconomic modelling with data science. Her current research focuses on using AI to enhance financial forecasting and economic policy analysis, with particular interest in the housing market, labour market, foreign exchange, and commodity markets (including gold and oil). She is also interested in the macroeconomic implications of the green energy transition. Qian teaches across both economics and digital technology programmes and works closely with industry partners on applied, interdisciplinary research. Before joining Loughborough University London, she taught macroeconomics as a Postgraduate Teaching Associate at the University of Exeter and gained industry experience in the financial sector.
Academic Background
PhD of Economics, University of Exeter, 2023
MSc Financial Economics (Distinction), Cardiff University, 2017
BSc Public Finance, Zhongnan University of Economics and Law, 2015
Professional Experience
Teaching:
LLP104 Statistical Methods in Finance (2023-2025)
LLP252 Data Analytics Tools in Digital Economy (2023-2025)
LLP233 Advanced programming and data visualization (2023-2024)
LLP108 Collaborative Project (2023-2025)
LLP249 Digital Strategy (2023-2024)
Conferences:
2024:
- XXV Conference on International Economics and XII Meeting on International Economics, Chair Speaker, Spain
- The Socially Just Planning Doctoral Network Seminar Series, Panel Speaker, UCL
- The 2024 RCEA International Conference in Economics, Econometrics, and Finance. London,
- 24th Scottish Economic Society Annual Conference
2023:
- 17th South-Eastern European Economic Research Workshop of the Bank of Albania
- 29th International Conference Computing in Economics and Finance
Working papers:
- Li, Q (2024). Monetary Policy and the Housing Wealth Effect.
- Li, Q (2024). Unconventional Monetary Policy and the Chinese Housing Market: a DSGE Approach.
- Li, Q and Sabaj, E, (2024). The effects of oil prices on the US housing market.
- Li, Q (2024). How do Asset Price Shocks Effect the Housing and Stock Markets?
Research
Qian’s research lies at the intersection of housing markets, macroeconomics, financial market, and machine learning. She is particularly interested in applying AI and data-driven methods to enhance economic modelling and forecasting. Her current work explores integrating traditional frameworks—such as DSGE and OLG models—with machine learning, NLP, and deep reinforcement learning to better understand and predict complex economic dynamics in the digital era.
Current PhD /Research Supervisions
Qian is currently seeking prospective PhD students interested in research areas include but are not limited to:
- Machine learning and NLP applications in housing markets and macro-finance.
- Hybrid modelling: combining ML with DSGE, OLG, or econometric models.
- Dynamic optimisation in macroeconomic models using reinforcement learning.
- Data-driven analysis using structured and unstructured economic and financial data.
- Macroeconomic implications of the green energy transition.