Intricacy of cryptocurrency returns

URN to cite this document:: urn:nbn:de:bvb:355-epub-583186
DOI to cite this document:: 10.5283/epub.58318

Nagl, Maximilian

License: Creative Commons Attribution 4.0
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Date of publication of this fulltext: 28 May 2024 07:44

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Abstract

This paper quantifies the intricacy, i.e., non-linearity and interactions of predictor variables, in explaining cryptocurrency returns. Using data from several thousand cryptocurrencies spanning 2014 to 2022, we observe a notably high level of intricacy. This provides a quantitative measure why linear models are often outperformed by machine learning algorithms in predicting cryptocurrency ...