Quantitative FinTech (QFIN)

The Quantitative FinTech (QFIN) research group at Sussex provides independent, customized and responsible solutions to promote innovation in financial markets. Our flexible approach resolves problems ranging from small boutique model development to advice on implementing complex financial systems.

QFIN aims to provide excellent research on issues currently faced by financial markets and promote stronger links between academic institutions, business and industries.

QFIN is one of two research groups in the Department of Accounting and Finance in the 海角社区 Business School. We aim to provide excellent research on issues currently faced by financial markets (including digital assets and their derivatives such as bitcoin swaps, futures and options, climate change finance and risk management). The problems that our researchers study require a data-driven quantitative approach including the analysis of big data sets derived from trade or order book data at ultra-high frequency.

QFIN has experts in quantitative finance, climate change, crypto asset market microstructure, big data analysis, machine learning and computer science. The network’s core and key associated members all have strong research backgrounds with publications in the top academic journals and some also have significant industry experience having held various roles in top-tier investment banks, hedge funds and relevant industries.

QFIN aims to promote stronger links between academic institutions and business and industries. We work and collaborate with business and industry on research initiatives and projects. We provide bespoke consultancy services, design and deliver tailor-made training courses as well as teach students to meet the challenges for a highly-skilled labour force that the industry requires.

Research Seminars


Wednesday 1 February: Sam Rosen – Temple University

Investor Experience Matters: Evidence from Generative Art Collections on the Blockchain

Wednesday 8 February: Marc-Oliver Pohle – Goethe University, Frankfurt

Generalised Correlation

Wednesday 15 February: Grigory Vilkov – Frankfurt School of Finance and Management

Media Narratives and Price Informativeness

Wednesday 22 February: Jie Cao – Hong Kong Polytechnic University

Forecasting Option Returns with News

Wednesday 8 March: Bing Han - Rotman School of Management, University of Toronto

Idiosyncratic Volatility and the ICAPM

Wednesday 22 March: Carole Bernard – Grenoble Ecole de Management

Option-Implied Dependence and Correlation Risk Premium

Wednesday 29 March: Raman Uppal – EDHEC Business School

What is Missing in Asset-Pricing Factor Models

 

Visit the Business School's Research Events page to register for any of the above Spring 2023 QFIN seminars.

Highlights of Recent Research

 

Alexander, C., Deng, J., and Zou, B. (2022) . European Journal of Operational Research. pp. 1-16. ISSN 0377-2217.

Alexander, C., Deng, J., Feng, J. and Wan, H. (2022) . Journal of Financial Markets. a100764. ISSN 1386-4181.

Alexander, C., Han, Y., and Meng, X. (2022) . International Journal of Forecasting. pp. 2-19. ISSN 0169-2070.

Alexander, C., Meng, X., and Wei, W. . European Journal of Operational Research, 299 (2022): 362-376.

Alexander, C. and M. Dakos (2022). . Quantitative Finance. ISSN 1469-7688.

Barunik, J., Bevilacqua, M., and Tunaru, R. (2022). . Review of Economics and Statistics, 104(6), 1304-1316.

Kaeck, A., van Kervel, V., and Seeger, N. J. (2022).
. Journal of Financial Markets, 59, 100675.

Meng, X. and Taylor, J. W. . International Journal of Forecasting, 38 (2022): 267-281.