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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/

: +44 (0) 141 330 3993
+44 (0)141 330 4939
Adam Smith Building, Glasgow G12 8RT
RePEc:edi:dfglauk (more details at EDIRC)

Research output

as
Jump to: Articles

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.

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. 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, Open Access Journal, vol. 11(4), pages 1-18, February.
    6. 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.

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