2,498 research outputs found
Mutual-Excitation of Cryptocurrency Market Returns and Social Media Topics
Cryptocurrencies have recently experienced a new wave of price volatility and
interest; activity within social media communities relating to cryptocurrencies
has increased significantly. There is currently limited documented knowledge of
factors which could indicate future price movements. This paper aims to
decipher relationships between cryptocurrency price changes and topic
discussion on social media to provide, among other things, an understanding of
which topics are indicative of future price movements. To achieve this a
well-known dynamic topic modelling approach is applied to social media
communication to retrieve information about the temporal occurrence of various
topics. A Hawkes model is then applied to find interactions between topics and
cryptocurrency prices. The results show particular topics tend to precede
certain types of price movements, for example the discussion of 'risk and
investment vs trading' being indicative of price falls, the discussion of
'substantial price movements' being indicative of volatility, and the
discussion of 'fundamental cryptocurrency value' by technical communities being
indicative of price rises. The knowledge of topic relationships gained here
could be built into a real-time system, providing trading or alerting signals.Comment: 3rd International Conference on Knowledge Engineering and
Applications (ICKEA 2018) - Moscow, Russia (June 25-27 2018
A Petri Nets Model for Blockchain Analysis
A Blockchain is a global shared infrastructure where cryptocurrency
transactions among addresses are recorded, validated and made publicly
available in a peer- to-peer network. To date the best known and important
cryptocurrency is the bitcoin. In this paper we focus on this cryptocurrency
and in particular on the modeling of the Bitcoin Blockchain by using the Petri
Nets formalism. The proposed model allows us to quickly collect information
about identities owning Bitcoin addresses and to recover measures and
statistics on the Bitcoin network. By exploiting algebraic formalism, we
reconstructed an Entities network associated to Blockchain transactions
gathering together Bitcoin addresses into the single entity holding permits to
manage Bitcoins held by those addresses. The model allows also to identify a
set of behaviours typical of Bitcoin owners, like that of using an address only
once, and to reconstruct chains for this behaviour together with the rate of
firing. Our model is highly flexible and can easily be adapted to include
different features of the Bitcoin crypto-currency system
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