Research
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Sun, X., Stasinakis, C. and Sermpinis, G., 2024. Decentralization illusion in Decentralized Finance: Evidence from tokenized voting in MakerDAO polls. Journal of Financial Stability (forthcoming).
Fernandes, F.D.S., Sermpinis, G., Stasinakis, C. and Zhao, Y., 2023. Corporate Social Responsibility and Firm Survival: Evidence from Chinese Listed Firms. British Journal of Management (forthcoming).
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.
Wei, M., Kyriakou, I., Sermpinis, G. and Stasinakis, C., 2023. Cryptocurrencies and Lucky Factors: The value of technical and fundamental analysis. International Journal of Finance & Economics (forthcoming).
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.
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.
Shi, Y., Stasinakis, C., Xu, Y., Yan, C. and Zhang, X., 2022. Stock price default boundary: A Black-Cox model approach. International Review of Financial Analysis, 83, p.102284.
Shi, Y., Stasinakis, C., Xu, Y. and Yan, C. (2022) Market Co-movement between Credit Default Swap Curves and Option Volatility Surfaces. International Review of Financial Analysis, 82, p.102192.
Li, Y., Stasinakis, C. and Yeo, W. M. (2022) A hybrid XGBoost-MLP model for credit risk assessment on Digital Supply Chain Finance. Forecasting, 4(1), pp. 184-207.
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.
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.
Zhao, Y., Stasinakis, C., Sermpinis, G. and Fernandes, F.D.S. (2019). Revisiting Fama–French factors' predictability with Bayesian modelling and copula‐based portfolio optimization. International Journal of Finance & Economics, 24 (4), 1443-1463.
Fernandes, F.D.S., Stasinakis, C. and Zekaite, Z. (2018). Forecasting government bond spreads with heuristic models: evidence from the Eurozone periphery. Annals of Operations Research, 1-32.
Fernandes, F.D.S., Stasinakis, C., & Bardarova, V. (2018). Two-stage DEA-Truncated Regression: Application in banking efficiency and financial development. Expert Systems with Applications, 96, 284-301.
Zhao, Y., Stasinakis, C., Sermpinis, G. and Shi, Y. (2018). Neural network copula portfolio optimization for exchange traded funds. Quantitative Finance, 18(5), 761-775.
Sermpinis, G., Stasinakis, C. and Hassanniakalager, A. (2017) Reverse Adaptive Krill Herd: Application with Locally Weighted Support Vector Regression for forecasting and trading Exchange Traded Funds, European Journal of Operational Research, 263 (2), 540-558.
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), 372-384.
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), 1901-1915.
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), 569-587.
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),1-846.
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), 1145-1163.
Sermpinis, G., Stasinakis, C., Theofilatos, K., and Karathanasopoulos, A. (2014) Inflation and unemployment forecasting with genetic support vector regression. Journal of Forecasting, 33(6), 471-487.
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, 22-36. ISBN 9780415636803
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), 21-54.
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), 316-329.
Impact / Income
Scottish Technology Ecosystem Review Fund (£5,000) – FinTech, Scottish Technology Sector and Higher Education
Wards grant (£3,000) – Organization of practitioners’ and academics’ workshop on Machine Learning and Financial Technology.
Wards grant (£2,955) - Basel III and Capital Market Union Plan: Evidence from banking performance and managerial sentiment
LKAS Collaborative Scholarships (£12,000) – Research project on Magnetomyography (MMG) Sensors
Grant (1950 euros) - workshop on Advances in Computational Finance, University of La Laguna, Tenerife
Erasmus teaching mobility grant (2,000 euros) promoting research led teaching in Leibniz University of Hannover, Germany- ‘International Investment Management: Computational Intelligent Techniques in Financial Forecasting Applications’