Professor Georgios Sermpinis

Professor of Finance (Accounting and Finance) (Accounting & Finance)

telephone: 01413307770
email: Georgios.Sermpinis@glasgow.ac.uk

Room 678D, University of Glasgow, Glasgow G12 8QQ

Biography

Georgios Sermpinis joined the Adam Smith Business School in September 2011. He holds degrees from the National Kapodistrian University of Athens and the Liverpool John Moores University. He previously worked at the University of Bedfordshire and Liverpool John Moores University.

Georgios has offered consultancy and provided seminars for major banks such as Goldman Sachs, BNP Paribas, Santander and Societe Generale.

Research interests

Georgios is a member of the Finance research cluster.

Areas of expertise:

Machine learning
Financial trading
Forecasting
Econometrics
Financial risk management
Operations research

Publications

List by: Type | Date

Jump to: 2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009

Number of items: 55.

2024

Feng, X., von Mettenheim, H.-J., Sermpinis, G. and Stasinakis, C. (2024) Sustainable portfolio construction via machine learning: ESG, SDG, and sentiment. European Financial Management, (doi: 10.1111/eufm.12531) (Early Online Publication)

Movaghari, H. , Serletis, A. and Sermpinis, G. (2024) Money demand stability: new evidence from transfer entropy. International Economics, 179, 100524. (doi: 10.1016/j.inteco.2024.100524)

Wei, M., Kyriakou, I., Sermpinis, G. and Stasinakis, C. (2024) Cryptocurrencies and Lucky Factors: the value of technical and fundamental analysis. International Journal of Finance and Economics, 29(4), pp. 4073-4104. (doi: 10.1002/ijfe.2863)

Sun, X., Stasinakis, C. and Sermpinis, G. (2024) Decentralization illusion in decentralized finance: evidence from tokenized voting in MakerDAO polls. Journal of Financial Stability, 73, 101286. (doi: 10.1016/j.jfs.2024.101286)

Zografopoulos, L., Iannino, M. C., Psaradellis, I. and Sermpinis, G. (2024) Industry return prediction via interpretable deep learning. European Journal of Operational Research, (doi: 10.1016/j.ejor.2024.08.032) (Early Online Publication)

Movaghari, H. and Sermpinis, G. (2024) Heterogeneous impact of cost of carry on corporate money demand. European Financial Management, (doi: 10.1111/eufm.12507) (Early Online Publication)

Da Silva Fernandes, F., Sermpinis, G. , Stasinakis, C. and Zhao, Y. (2024) Corporate social responsibility and firm survival: evidence from Chinese listed firms. British Journal of Management, 35(2), pp. 1014-1039. (doi: 10.1111/1467-8551.12750)

Hsu, P.-H., Kyriakou, I., Ma, T. and Sermpinis, G. (2024) Mutual funds’ conditional performance free of data snooping bias. Journal of Financial and Quantitative Analysis, (doi: 10.1017/S0022109024000097) (Early Online Publication)

2023

Wei, M., Sermpinis, G. and Stasinakis, C. (2023) Forecasting and trading Bitcoin with machine learning techniques and a hybrid volatility/sentiment leverage. Journal of Forecasting, 42(4), pp. 852-871. (doi: 10.1002/for.2922)

Sermpinis, G. , Tsoukas, S. and Zhang, Y. (2023) Modelling failure rates with machine-learning models: Evidence from a panel of UK firms. European Financial Management, 29(3), pp. 734-763. (doi: 10.1111/eufm.12369)

Nguyen, D. K., Sermpinis, G. and Stasinakis, C. (2023) Big data, artificial intelligence, and machine learning: a transformative symbiosis in favour of financial technology. European Financial Management, 29(2), pp. 517-548. (doi: 10.1111/eufm.12365)

Psaradellis, I., Laws, J., Pantelous, A. A. and Sermpinis, G. (2023) Technical analysis, spread trading, and data snooping control. International Journal of Forecasting, 39(1), pp. 178-191. (doi: 10.1016/j.ijforecast.2021.10.002)

2022

Li, W., Paraschiv, F. and Sermpinis, G. (2022) A data-driven explainable case-based reasoning approach for financial risk detection. Quantitative Finance, 22(12), pp. 2257-2274. (doi: 10.1080/14697688.2022.2118071)

Andreev, B., Sermpinis, G. and Stasinakis, C. (2022) Modelling financial markets during times of extreme volatility: evidence from the GameStop short squeeze. Forecasting, 4(3), pp. 654-673. (doi: 10.3390/forecast4030035)

Petropoulos, F. et al. (2022) Forecasting: theory and practice. International Journal of Forecasting, 38(3), pp. 705-871. (doi: 10.1016/j.ijforecast.2021.11.001)

2021

Hassanniakalager, A., Sermpinis, G. and Stasinakis, C. (2021) Trading the foreign exchange market with technical analysis and Bayesian statistics. Journal of Empirical Finance, 63, pp. 230-251. (doi: 10.1016/j.jempfin.2021.07.006)

Sermpinis, G. , Hassanniakalager, A., Stasinakis, C. and Psaradellis, I. (2021) Technical analysis profitability and persistence: a discrete false discovery approach on MSCI indices. Journal of International Financial Markets, Institutions and Money, 73, 101353. (doi: 10.1016/j.intfin.2021.101353)

Sermpinis, G. , Karathanasopoulos, A., Rosillo, R. and de la Fuente, D. (2021) Neural networks in financial trading. Annals of Operations Research, 297(1-2), pp. 293-308. (doi: 10.1007/s10479-019-03144-y)

2020

Hassanniakalager, A., Sermpinis, G. , Stasinakis, C. and Verousis, T. (2020) A conditional fuzzy inference approach in forecasting. European Journal of Operational Research, 283(1), pp. 196-216. (doi: 10.1016/j.ejor.2019.11.006)

2019

Sermpinis, G. , Tsoukas, S. and Zhang, P. (2019) What influences a bank’s decision to go public? International Journal of Finance and Economics, 24(4), pp. 1464-1485. (doi: 10.1002/ijfe.1740)

Zhao, Y., Stasinakis, C. , Sermpinis, G. and Da Silva Fernandes, F. (2019) Revisiting Fama-French factors’ predictability with Bayesian modelling and copula-based portfolio optimization. International Journal of Finance and Economics, 24(42), pp. 1443-1463. (doi: 10.1002/ijfe.1742)

Psaradellis, I., Laws, J., Pantelous, A. A. and Sermpinis, G. (2019) Performance of technical trading rules: evidence from the crude oil market. European Journal of Finance, 25(17), pp. 1793-1815. (doi: 10.1080/1351847X.2018.1552172)

2018

Sermpinis, G. , Tsoukas, S. and Zhang, P. (2018) Modelling market implied ratings using LASSO variable selection techniques. Journal of Empirical Finance, 48, pp. 19-35. (doi: 10.1016/j.jempfin.2018.05.001)

Zhao, Y., Stasinakis, C. , Sermpinis, G. and Shi, Y. (2018) Neural network copula portfolio optimization for exchange traded funds. Quantitative Finance, 18(5), pp. 761-775. (doi: 10.1080/14697688.2017.1414505)

Verousis, T., Perotti, P. and Sermpinis, G. (2018) One size fits all? High frequency trading, tick size changes and the implications for exchanges: market quality and market structure considerations. Review of Quantitative Finance and Accounting, 50(2), pp. 353-392. (doi: 10.1007/s11156-017-0632-2)

2017

Sermpinis, G. , Stasinakis, C. and Hassanniakalager, A. (2017) Reverse adaptive krill herd locally weighted support vector regression for forecasting and trading exchange traded funds. European Journal of Operational Research, 263(2), pp. 540-558. (doi: 10.1016/j.ejor.2017.06.019)

Sermpinis, G. , Stasinakis, C. , Rosillo, R. and de la Fuente, D. (2017) European exchange trading funds trading with locally weighted support vector regression. European Journal of Operational Research, 258(1), pp. 372-384. (doi: 10.1016/j.ejor.2016.09.005)

2016

Psaradellis, I. and Sermpinis, G. (2016) Modelling and trading the U.S. implied volatility indices: evidence from the VIX, VXN and VXD indices. International Journal of Forecasting, 32(4), pp. 1268-1283. (doi: 10.1016/j.ijforecast.2016.05.004)

Stasinakis, C. , Sermpinis, G. , Psaradellis, I. and Verousis, T. (2016) Krill herd support vector regression and heterogeneous autoregressive leverage: evidence from forecasting and trading commodities. Quantitative Finance, 16(102), pp. 1901-1915. (doi: 10.1080/14697688.2016.1211800)

Stasinakis, C. , Sermpinis, G. , Theofilatos, K. and Karathanasopoulos, A. (2016) Forecasting US unemployment with radial basis neural networks, kalman filters and support vector regressions. Computational Economics, 47(4), pp. 569-587. (doi: 10.1007/s10614-014-9479-y)

Sermpinis, G. , Verousis, T. and Theofilatos, K. (2016) Adaptive evolutionary neural networks for forecasting and trading without a data-snooping bias. Journal of Forecasting, 35(1), pp. 1-12. (doi: 10.1002/for.2338)

Karathanasopoulos, A., Theofilatos, K. A., Sermpinis, G. , Dunis, C., Mitra, S. and Stasinakis, C. (2016) Stock market prediction using evolutionary support vector machines: an application to the ASE20 index. European Journal of Finance, 22(12), pp. 1145-1163. (doi: 10.1080/1351847X.2015.1040167)

2015

Sermpinis, G. , Stasinakis, C. , Theofilatos, K. and Karathanasopoulos, A. (2015) Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms: support vector regression forecast combinations. European Journal of Operational Research, 247(3), pp. 831-846. (doi: 10.1016/j.ejor.2015.06.052)

Mitra, S., Karathanasopoulos, A., Sermpinis, G. and Dunis, C. (2015) Operational risk: emerging markets, sectors and measurement. European Journal of Operational Research, 241(1), pp. 122-132. (doi: 10.1016/j.ejor.2014.08.021)

2014

Karathanasopoulos, A., Sermpinis, G. , Laws, J. and Dunis, C. (2014) Modelling and trading the Greek stock market with gene expression and genetic programing algorithms. Journal of Forecasting, 33(8), pp. 596-610. (doi: 10.1002/for.2290)

Sermpinis, G. , Stasinakis, C., Theofilatos, K. and Karathanasopoulos, A. (2014) Inflation and unemployment forecasting with genetic support vector regression. Journal of Forecasting, 33(6), pp. 471-487. (doi: 10.1002/for.2296)

Sermpinis, G. , Stasinakis, C. and Dunis, C. (2014) Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects. Journal of International Financial Markets, Institutions and Money, 30(1), pp. 21-54. (doi: 10.1016/j.intfin.2014.01.006)

Sermpinis, G. , Laws, J. and Dunis, C.L. (2014) Modelling commodity value at risk with Psi Sigma neural networks using open–high–low–close data. European Journal of Finance, 21(4), pp. 316-336. (doi: 10.1080/1351847X.2012.744763)

Dunis, C., Likothanassis, S., Karathanasopoulos, A., Sermpinis, G. and Theofilatos, K. (Eds.) (2014) Computational Intelligence Techniques for Trading and Investment. Series: Routledge advances in experimental and computable economics. Routledge. ISBN 9780415636803

Stasinakis, C. and Sermpinis, G. (2014) Financial forecasting and trading strategies: a survey. In: Dunis, C., Likothanassis, S., Karathanasopoulos, A., Sermpinis, G. and Theofilatos, K. (eds.) Computational Intelligence Techniques for Trading and Investment. Routledge: Abindgon, pp. 22-36. ISBN 9780415636803

2013

Sermpinis, G. , Theofilatos, K., Karathanasopoulos, A. and Dunis, C. (2013) Forecasting foreign exchange rates with adaptive neural networks using radial basis functions and particle swarm optimization. European Journal of Operational Research, 225(3), pp. 528-540. (doi: 10.1016/j.ejor.2012.10.020)

Dunis, C.L., Likothanassis, S.D., Karathanasopoulos, A.S., Sermpinis, G.S. and Theofilatos, K.A. (2013) A hybrid genetic algorithm–support vector machine approach in the task of forecasting and trading. Journal of Asset Management, 14(1), pp. 52-71. (doi: 10.1057/jam.2013.2)

Dimitrakopoulos, C., Karathanasopoulos, A., Sermpinis, G. and Likothanassis, S. (2013) Adaptive filtering on forecasting financial derivatives indices. In: Dunis, C., Likothanassis, S., Karathanasopoulos, A., Sermpinis, G. and Theofilatos, K. (eds.) Computational Intelligence Techniques for Trading and Investment. Series: Routledge advances in experimental and computable economics (6). Routledge: Abingdon, pp. 66-78. ISBN 9780415636803 (doi: 10.4324/9780203084984)

Dunis, C., Sermpinis, G. and Karampelia, M.F. (2013) Stock market linkages among new EMU members and the Euro area: implications for financial integration and portfolio diversification. Studies in Economics and Finance, 30(4), pp. 370-388. (doi: 10.1108/SEF-04-2012-0048)

Sermpinis, G., Laws, J. and Dunis, C.L. (2013) Modelling and trading the realised volatility of the FTSE100 futures with higher order neural networks. European Journal of Finance, 19(3), pp. 165-179. (doi: 10.1080/1351847X.2011.606990)

Sermpinis, G. , Stasinakis, C. and Karathanasopoulos, A. (2013) Kalman filter and SVR combinations in forecasting US unemployment. Artificial Intelligence Applications and Innovations, 412, pp. 506-515. (doi: 10.1007/978-3-642-41142-7_51)

Sermpinis, G. , Fountouli, A., Theofilatos, K. and Karathanasopoulos, A. (2013) Gene expression programming and trading strategies. Artificial Intelligence Applications and Innovations, 412, pp. 497-505. (doi: 10.1007/978-3-642-41142-7_50)

Theofilatos, K., Amorgianiotis, T., Karathanasopoulos, A., Sermpinis, G. , Georgopoulos, E. and Likothanassis, S. (2013) Advanced short-term forecasting and trading deploying neural networks optimized with adaptive evolutionary algorith. In: Dunis, C., Likothanassis, S., Karathanasopoulos, A., Sermpinis, G. and Theofilatos, K. (eds.) Computational Intelligence Techniques for Trading and Investment. Series: Routledge advances in experimental and computable economics (6). Routledge: Abingdon, pp. 133-145. ISBN 9780415636803 (doi: 10.4324/9780203084984)

2012

Sermpinis, G. , Dunis, C., Laws, J. and Stasinakis, C. (2012) Forecasting and trading the EUR/USD exchange rate with stochastic Neural Network combination and time-varying leverage. Decision Support Systems, 54(1), pp. 316-329. (doi: 10.1016/j.dss.2012.05.039)

Sermpinis, G. , Laws, J., Karathanasopoulos, A. and Dunis, C.L. (2012) Forecasting and trading the EUR/USD exchange rate with Gene Expression and Psi Sigma Neural Networks. Expert Systems with Applications, 39(10), pp. 8865-8877. (doi: 10.1016/j.eswa.2012.02.022)

Sermpinis, G. , Dunis, C., Laws, J. and Stasinakis, C. (2012) Kalman filters and neural networks in forecasting and trading. In: Jayne, C., Yue, S. and Iliadis, L. (eds.) Engineering Applications of Neural Networks, 13th International Conference EANN 2012 Proceedings, EANN 2012, CCIS 311. Series: Communications in Computer and Information Science (311). Springer Berlin Heidelberg: Berlin Heidelberg, pp. 433-442. ISBN 9783642329081 (doi: 10.1007/978-3-642-32909-8_44)