![Anqi Liu](https://profiles-cardiff.imgix.net/attachments/191?w=200&h=200&auto=format&crop=faces&fit=crop&q=90")
Dr Anqi Liu
- Available for postgraduate supervision
Teams and roles for Anqi Liu
Senior Lecturer in Financial Mathematics
Overview
Research Group
Operational Research Group
Research Interests
- Hawkes process in finance.
- Financial networks.
- Financial technology (FinTech) such as cryptocurrencies, digital economy, new markets.
- Fractal activity time geometric Brownian motion (FATGBM) modelling for derivative pricing.
- Market microstructure and trading behaviour.
Research Grants
- Establishing a Cryptocurrency Volatility Indicator with Sentiment (CVIS). PI. Funder: UKFin+ Pilot Funding Stream £18,991. April 2025 - July 2025.
- Mitigating Stress and Confusion in Credit Contracts using Machine Learning and Simplification. PI. Funder: UKFin+ Agile Funding Stream £10,000. February 2025 - June 2025.
- Typology of Cryptocurrency Traders: Analyzing Cryptocurrency Trading utilizing In-depth Behavioural Patterns. Co-I. Funder: Wales Data Nation Accelerator (WDNA) Super Sprint Project £27,599. January 2022 - March 2022.
Publication
2025
- Leonenko, N., Liu, A. and Shchestyuk, N. 2025. Student models for a risky asset with dependence: Option pricing and Greeks. Austrian Journal of Statistics 54(1), pp. 138-165. (10.17713/ajs.v54i1.1952)
2024
- Wu, F., Liu, A., Chen, J. and Li, Y. 2024. Analysing network dynamics: The contagion effects of SVB's collapse on the US tech industry. Journal of Risk and Financial Management 17(10), article number: 427. (10.3390/jrfm17100427)
- Zhang, J., Wang, H., Chen, J. and Liu, A. 2024. Cryptocurrency price bubble detection using log-periodic power law model and wavelet analysis. IEEE Transactions on Engineering Management 71, pp. 11796-1812. (10.1109/TEM.2024.3427647)
2023
- Liu, A., Jahanshahloo, H., Chen, J. and Eshraghi, A. 2023. Trading patterns in the Bitcoin market. European Journal of Finance (10.1080/1351847X.2023.2241883)
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)
Articles
- Leonenko, N., Liu, A. and Shchestyuk, N. 2025. Student models for a risky asset with dependence: Option pricing and Greeks. Austrian Journal of Statistics 54(1), pp. 138-165. (10.17713/ajs.v54i1.1952)
- Wu, F., Liu, A., Chen, J. and Li, Y. 2024. Analysing network dynamics: The contagion effects of SVB's collapse on the US tech industry. Journal of Risk and Financial Management 17(10), article number: 427. (10.3390/jrfm17100427)
- Zhang, J., Wang, H., Chen, J. and Liu, A. 2024. Cryptocurrency price bubble detection using log-periodic power law model and wavelet analysis. IEEE Transactions on Engineering Management 71, pp. 11796-1812. (10.1109/TEM.2024.3427647)
- Liu, A., Jahanshahloo, H., Chen, J. and Eshraghi, A. 2023. Trading patterns in the Bitcoin market. European Journal of Finance (10.1080/1351847X.2023.2241883)
- 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)
- 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)
- 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)
- 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)
- 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)
Conferences
- 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.
- 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)
- 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)
Research
My research interests include time series models of financial returns, trading behavioral analysis and Hawkes process in finance. I have 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 existing pricing and risk evaluation framework, especially from the systemic perspective, for financial markets. I work on applications of a variety modelling techniques, such as time series and stochastic models, optimization strategies, machine learning algorithms, in finance. I am experienced in finance data analysis and computing techniques.
Teaching
- Finance II: Investment Management
- Market Microstructure and Trading Theory
- Trading, Market Design and Applications
Biography
Dr Anqi Liu holds a BSc in Mathematics and Applied Mathematics from the Northwest University, China; and an MSc and a PhD in Financial Engineering from Stevens Institute of Technology, USA. Her expertise lies in quantitative and computational finance, with a primary research focus on cryptocurrency markets. Her broader research interests include financial networks and system modelling, Hawkes processes in finance, financial time series models, and trading behaviour simulations. She has established multidisciplinary skills and a strong research record in empirical finance and AI-driven financial modelling. In recent years, she has led and co-led FinTech projects funded by the UKFin+ Network and the Alan Turing Institute, contributing to the development of meaningful academic-industrial collaborations. She has been Associated Editor for International Review of Economics and Finance, and Guest Editor for The Journal of Futures Markets.
Supervisions
- Cryptocurrency market microstructure.
- Hawkes processes applications in Finance.
- Financial networks and systemic risks.
Current supervision
Contact Details
LiuA5@cardiff.ac.uk
+44 29208 70908
Abacws, Room Abacus/4.49, Senghennydd Road, Cathays, Cardiff, CF24 4AG
+44 29208 70908
Abacws, Room Abacus/4.49, Senghennydd Road, Cathays, Cardiff, CF24 4AG
