Social signals and algorithmic trading of Bitcoin

dc.contributor.author	Garcia, David	en_US
dc.contributor.author	Schweitzer, Frank	en_US
dc.date.accessioned	2016-10-11T05:37:51Z
dc.date.available	2016-10-11T05:37:51Z
dc.date.issued	2015	en_US
dc.identifier.other	HPU4160597	en_US
dc.identifier.uri	https://lib.hpu.edu.vn/handle/123456789/23679
dc.description.abstract	The availability of data on digital traces is growing to unprecedented sizes, but inferring actionable knowledge from large-scale data is far from being trivial. This is especially important for computational finance, where digital traces of human behaviour offer a great potential to drive trading strategies. We contribute to this by providing a consistent approach that integrates various datasources in the design of algorithmic traders.	en_US
dc.format.extent	13 p.	en_US
dc.format.mimetype	application/pdf
dc.language.iso	en	en_US
dc.subject	Computer science	en_US
dc.subject	E-science	en_US
dc.subject	Bitcoin	en_US
dc.subject	Computational social science	en_US
dc.subject	Algorithmic trading	en_US
dc.subject	Polarization	en_US
dc.subject	Sentiment	en_US
dc.subject	Prediction	en_US
dc.title	Social signals and algorithmic trading of Bitcoin	en_US
dc.type	Article	en_US
dc.size	777KB	en_US
dc.department	Education	en_US