Dr Anqi Liu
Lecturer in Financial Mathematics
- liua5@cardiff.ac.uk
- +44 29208 70908
- Abacws/4.49, Abacws, Senghennydd Road, Cathays, Cardiff, CF24 4AG
- Available for postgraduate supervision
Overview
Research Group
Operational Research Group
Research Interests
- Market microstructure and trading behavoiral patterns.
- Time series models of financial returns.
- Application of Hawkes process in financial markets.
- Fractal activity time geometric Brownian motion modelling for derivative pricing.
- Financial technology (FinTech) such as cryptocurrencies, digital economy, new markets.
Biography
Dr Anqi Liu holds BSc in Mathematics and Applied Mathematics from the Northwest University, China; MSc and PhD in Financial Engineering from Stevens Institute of Technology, USA. She received Stevens Innovation and Entrepreneurship Scholarship for 4 years during her PhD.
Anqi’s research interests include trading behavioral analysis, Hawkes process in finance and fractal activity time geometric Brownian motion (FATGBM) pricing models. She has been collaborating with a number of financial researchers in the area of quantitative finance and computational finance, and has published a series of papers in international journals and conferences. The overall goal of her research is to improve pricing and risk modelling framework for financial markets. Recently, she mainly focuses on trading behavoiral patterns in crypto markets and blockchain-based financial network reactions to systemic events.
Publications
2021
- Chen, J. and Liu, A. 2021. Information transition in trading and its effect on market efficiency: an entropy approach. Presented at: 1st International Forum on Financial Mathematics and FinTech, Beijing, China, 29 June - 2 July 2019Proceeding of the First International Academic Forum on Financial Mathematics and Financial Technology. Financial Mathematics and Fintech Springer pp. 59-77.
2020
- Liu, A., Chen, J., Yang, S. Y. and Hawkes, A. G. 2020. The flow of information in trading: an entropy approach to market regimes. Entropy 22(9), article number: 1064. (10.3390/e22091064)
- Liu, A., Paddrik, M., Yang, S. Y. and Zhang, X. 2020. Interbank contagion: an agent-based model approach to endogenously formed networks. Journal of Banking and Finance 112, article number: 105191. (10.1016/j.jbankfin.2017.08.008)
2018
- Liu, A., Mo, C. Y. J., Paddrik, M. E. and Yang, S. Y. 2018. An agent-based approach to interbank market lending decisions and risk implications. Information 9(6), pp. 1-18., article number: 132. (10.3390/info9060132)
- Yang, S. Y., Liu, A., Chen, J. and Hawkes, A. G. 2018. Applications of multi-variate Hawkes process to joint modelling of sentiment and market return events. Quantitative Finance 18(2), pp. 295-310. (10.1080/14697688.2017.1403156)
2017
- Yang, S. Y., Mo, S. Y. K., Liu, A. and Kirilenko, A. A. 2017. Genetic programming optimization for a sentiment feedback strength based trading strategy. Neurocomputing 264, pp. 29-41. (10.1016/j.neucom.2016.10.103)
- Song, Q., Liu, A. and Yang, S. 2017. Stock portfolio selection using learning-to-rank algorithms with news sentiment. Neurocomputing 264, pp. 20-28. (10.1016/j.neucom.2017.02.097)
2016
- Mo, S. Y. K., Liu, A. and Yang, S. Y. 2016. News sentiment to market impact and its feedback effect. Environment Systems and Decisions 36(2), pp. 158-166. (10.1007/s10669-016-9590-9)
- Song, Q., Liu, A., Yang, S. Y., Deane, A. and Datta, K. 2016. An extreme firm-specific news sentiment asymmetry based trading strategy. Presented at: 2015 IEEE Symposium on Computational Intelligence, Cape Town, South Africa, 7-10 December 20152015 IEEE Symposium Series on Computational Intelligence. IEEE pp. 898., (10.1109/SSCI.2015.132)
2015
- Yang, S. Y., Mo, S. Y. K. and Liu, A. 2015. Twitter financial community sentiment and its predictive relationship to stock market movement. Quantitative Finance 15(10), pp. 1637-1656. (10.1080/14697688.2015.1071078)
2014
- Yang, S. Y., Liu, A. and Mo, S. Y. K. 2014. Twitter financial community modeling using agent based simulation. Presented at: 2014 Computational Intelligence for Financial Engineering & Economics (CIFEr), London, UK, 27-28 March 20142014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr). IEEE, (10.1109/CIFEr.2014.6924055)
Teaching
- Finance II: Investment Management
- Market Microstructure and Trading Theory
- Trading, Market Design and Applications
My research interests include time series models of financial returns, trading behavioral analysis and Hawkes process in finance. I has been collaborating with a number of financial researchers in the area of quantitative finance and computational finance, and has published a series of papers in international journals and conferences. The overall goal of this research line is to improve the existing pricing and risk modelling framework for financial markets. I implement a variaty of modelling techniques, such as time series models, stochastic models, machine learning algorithms, in analysis of financial markets. I am experienced in using advanced computing techniques to explore valuable insights in financial market data.
Supervision
- Cryptocurrency market microstructure.
- Hawkes processes applications in Finance.
- Agent-based modelling in behavioural finance.