Machine Learning for Bitcoin Pricing — A Structured Literature Review

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Cite as text

						@Select Types{,
							 
							 
							 
							 
							 
							Journal   = "Band-1",
							 Title= "Machine Learning for Bitcoin Pricing — A Structured Literature Review", 
							Author= "Patrick Jaquart, David Dann, and Carl Martin", 
							Doi= "https://doi.org/10.30844/wi_2020_b4-jaquart", 
							 Abstract= "Bitcoin, as the most popular cryptocurrency, has received increasing attention from both investors and researchers over recent years. One emerging branch of the research on bitcoin focuses on empirical bitcoin pricing. Machine learning methods are well suited for predictive problems, and researchers frequently apply these methods to predict bitcoin prices and returns. In this study, we analyze the existing body of literature on empirical bitcoin pricing via machine learning and structure it according to four different concepts. We show that research on this topic is highly diverse and that the results of several studies can only be compared to a limited extent. We further derive guidelines for future publications in the field to ensure a sufficient level of transparency and reproducibility.

", 
							 Keywords= "bitcoin, cryptocurrency, asset pricing, machine learning, prediction", 
							}
					
Patrick Jaquart, David Dann, and Carl Martin: Machine Learning for Bitcoin Pricing — A Structured Literature Review. Online: https://doi.org/10.30844/wi_2020_b4-jaquart (Abgerufen 20.08.22)

Abstract

Abstract

Bitcoin, as the most popular cryptocurrency, has received increasing attention from both investors and researchers over recent years. One emerging branch of the research on bitcoin focuses on empirical bitcoin pricing. Machine learning methods are well suited for predictive problems, and researchers frequently apply these methods to predict bitcoin prices and returns. In this study, we analyze the existing body of literature on empirical bitcoin pricing via machine learning and structure it according to four different concepts. We show that research on this topic is highly diverse and that the results of several studies can only be compared to a limited extent. We further derive guidelines for future publications in the field to ensure a sufficient level of transparency and reproducibility.

Keywords

Schlüsselwörter

bitcoin, cryptocurrency, asset pricing, machine learning, prediction

References

Referenzen

Leider ist der Eintrag nur auf Amerikanisches Englisch verfügbar.

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