Modelling Determinants of Cryptocurrency Prices: A Bayesian Network
Approach
- URL: http://arxiv.org/abs/2303.16148v1
- Date: Sun, 26 Mar 2023 21:54:41 GMT
- Title: Modelling Determinants of Cryptocurrency Prices: A Bayesian Network
Approach
- Authors: Rasoul Amirzadeh, Asef Nazari, Dhananjay Thiruvady and Mong Shan Ee
- Abstract summary: Social media is the most significant influencing factor of the prices of cryptocurrencies.
It is not possible to generalise the coins' reactions against the changes in the factors.
The coins need to be studied separately for a particular price movement investigation.
- Score: 0.8602553195689513
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The growth of market capitalisation and the number of altcoins
(cryptocurrencies other than Bitcoin) provide investment opportunities and
complicate the prediction of their price movements. A significant challenge in
this volatile and relatively immature market is the problem of predicting
cryptocurrency prices which needs to identify the factors influencing these
prices. The focus of this study is to investigate the factors influencing
altcoin prices, and these factors have been investigated from a causal analysis
perspective using Bayesian networks. In particular, studying the nature of
interactions between five leading altcoins, traditional financial assets
including gold, oil, and S\&P 500, and social media is the research question.
To provide an answer to the question, we create causal networks which are built
from the historic price data of five traditional financial assets, social media
data, and price data of altcoins. The ensuing networks are used for causal
reasoning and diagnosis, and the results indicate that social media (in
particular Twitter data in this study) is the most significant influencing
factor of the prices of altcoins. Furthermore, it is not possible to generalise
the coins' reactions against the changes in the factors. Consequently, the
coins need to be studied separately for a particular price movement
investigation.
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