Charalampos Stasinakis

Personal Details

First Name:	Charalampos
Middle Name:
Last Name:	Stasinakis
Suffix:
RePEc Short-ID:	pst570
	http://www.gla.ac.uk/schools/business/staff/charalamposstasinakis/
Terminal Degree:	2013 Adam Smith Business School; University of Glasgow (from RePEc Genealogy)

Affiliation

Department of Accounting and Finance
Adam Smith Business School
University of Glasgow

Glasgow, United Kingdom
http://www.gla.ac.uk/subjects/accountingfinance/
RePEc:edi:dfglauk (more details at EDIRC)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

1. Sermpinis, Georgios & Stasinakis, Charalampos & Dunis, Christian, 2014. "Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 30(C), pages 21-54.

   Cited by:

   1. Christoph Gleue & Dennis Eilers & Hans-Jörg Mettenheim & Michael H. Breitner, 2019. "Decision Support for the Automotive Industry," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(4), pages 385-397, August.
   2. Omer Berat Sezer & Mehmet Ugur Gudelek & Ahmet Murat Ozbayoglu, 2019. "Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019," Papers 1911.13288, arXiv.org.
   3. Shin, Ki-Hong & Baek, Woonhak & Kim, Kyungsik & You, Cheol-Hwan & Chang, Ki-Ho & Lee, Dong-In & Yum, Seong Soo, 2019. "Neural network and regression methods for optimizations between two meteorological factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 778-796.
   4. Wang, Jie & Wang, Jun, 2016. "Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations," Energy, Elsevier, vol. 102(C), pages 365-374.
   5. Mingzhe Wei & Georgios Sermpinis & Charalampos Stasinakis, 2023. "Forecasting and trading Bitcoin with machine learning techniques and a hybrid volatility/sentiment leverage," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 852-871, July.
   6. Wang, Bin & Wang, Jun, 2020. "Energy futures and spots prices forecasting by hybrid SW-GRU with EMD and error evaluation," Energy Economics, Elsevier, vol. 90(C).
   7. Georgios Sermpinis & Andreas Karathanasopoulos & Rafael Rosillo & David Fuente, 2021. "Neural networks in financial trading," Annals of Operations Research, Springer, vol. 297(1), pages 293-308, February.
   8. Wenquan Jin & Israr Ullah & Shabir Ahmad & Dohyeun Kim, 2019. "Occupant Comfort Management Based on Energy Optimization Using an Environment Prediction Model in Smart Homes," Sustainability, MDPI, vol. 11(4), pages 1-18, February.
   9. Panopoulou, Ekaterini & Souropanis, Ioannis, 2019. "The role of technical indicators in exchange rate forecasting," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 197-221.

More information

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Statistics

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Co-authorship network on CollEc

Corrections

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