High-Frequency Technical Trading: The Importance of Speed
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Cited by:
- Adamantios Ntakaris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2019. "Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators," Papers 1907.09452, arXiv.org.
- Adamantios Ntakaris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2020. "Mid-price prediction based on machine learning methods with technical and quantitative indicators," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-39, June.
- Giancarlo Salirrosas Mart'inez, 2016. "Biased Roulette Wheel: A Quantitative Trading Strategy Approach," Papers 1609.09601, arXiv.org.
- Scholtus, Martin & van Dijk, Dick & Frijns, Bart, 2014.
"Speed, algorithmic trading, and market quality around macroeconomic news announcements,"
Journal of Banking & Finance, Elsevier, vol. 38(C), pages 89-105.
- Martin L. Scholtus & Dick van Dijk & Bart Frijns, 2012. "Speed, Algorithmic Trading, and Market Quality around Macroeconomic News Announcements," Tinbergen Institute Discussion Papers 12-121/III, Tinbergen Institute.
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More about this item
Keywords
Technical Trading; High-Frequency Trading; Latency Costs; Trading Speed; Market Activity;All these keywords.
JEL classification:
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
- G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
- G20 - Financial Economics - - Financial Institutions and Services - - - General
NEP fields
This paper has been announced in the following NEP Reports:- NEP-MST-2012-08-23 (Market Microstructure)
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