|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
Burguillo, Juan C.
Matos Dias, Joana
|Title:||Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era|
|Citation:||Zamuda A, Crescimanna V, Burguillo JC, Matos Dias J, Wegrzyn-Wolska K, Rached I, González-Vélez H, Senkerik R, Pop C, Cioara T, Salomie I & Bracciali A (2019) Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era. In: Kołodziej J & González-Vélez H (eds.) High-Performance Modelling and Simulation for Big Data Applications. Lecture Notes in Computer Science, 11400. ICT COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications (cHiPSet), Vilnius, Lithuania, 28.03.2019-29.03.2019. Cham, Switzerland: Springer pp. 325-349. https://doi.org/10.1007/978-3-030-16272-6_12|
|Series/Report no.:||Lecture Notes in Computer Science, 11400|
|Conference Name:||ICT COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)|
|Conference Dates:||2019-03-28 - 2019-03-29|
|Conference Location:||Vilnius, Lithuania|
|Abstract:||This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking Initial Coin Offering (ICO) values for incoming cryptocurrencies, trading strategies employing the new Sentiment Analysis metrics, and risk aversion in cryptocurrencies trading through a multi-objective portfolio selection. Our perspective is rationalized on the perspective on elastic demand of computational resources for cloud infrastructures.|
|Status:||VoR - Version of Record|
|Rights:||This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.|
|Zamuda et al-2019-chapter.pdf||Fulltext - Published Version||382.63 kB||Adobe PDF||View/Open|
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