37 research outputs found
Transakcje kryptowalutą bitcoin - wybrane zagrożenia
W artykule zwrócono uwagę na wybrane zagrożenia związane z internetowym systemem płatności za pomocą kryptowaluty bitcoin. Poruszone zostały zagadnienia związane z anoniowością w sieci Bitcoin, pozyskiwaniem bitcoinów, prawdopodobieństwem podwójnego wydania środków (ang. double spending), ryzykiem inwestycji w kryptowalutę oraz ryzykiem AML.Kinga Kądziołk
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
Wikipedia and Digital Currencies: Interplay Between Collective Attention and Market Performance
The production and consumption of information about Bitcoin and other digital-, or 'crypto'-, currencies have grown together with their market capitalisation. However, a systematic investigation of the relationship between online attention and market dynamics, across multiple digital currencies, is still lacking. Here, we quantify the interplay between the attention towards digital currencies in Wikipedia and their market performance. We consider the entire edit history of currency-related pages, and their view history from July 2015. First, we quantify the evolution of the cryptocurrency presence in Wikipedia by analysing the editorial activity and the network of co-edited pages. We find that a small community of tightly connected editors is responsible for most of the production of information about cryptocurrencies in Wikipedia. Then, we show that a simple trading strategy informed by Wikipedia views performs better, in terms of returns on investment, than classic baseline strategies for most of the covered period. Our results contribute to the recent literature on the interplay between online information and investment markets, and we anticipate it will be of interest for researchers as well as investors
Cryptocurrency market structure: connecting emotions and economics
We study the dependency and causality structure of the cryptocurrency market
investigating collective movements of both prices and social sentiment related
to almost two thousand cryptocurrencies traded during the first six months of
2018. This is the first study of the whole cryptocurrency market structure. It
introduces several rigorous innovative methodologies applicable to this and to
several other complex systems where a large number of variables interact in a
non-linear way, which is a distinctive feature of the digital economy. The
analysis of the dependency structure reveals that prices are significantly
correlated with sentiment. The major, most capitalised cryptocurrencies, such
as bitcoin, have a central role in the price correlation network but only a
marginal role in the sentiment network and in the network describing the
interactions between the two. The study of the causality structure reveals a
causality network that is consistently related with the correlation structures
and shows that both prices cause sentiment and sentiment cause prices across
currencies with the latter being stronger in size but smaller in number of
significative interactions. Overall our study uncovers a complex and rich
structure of interrelations where prices and sentiment influence each other
both instantaneously and with lead-lag causal relations. A major finding is
that minor currencies, with small capitalisation, play a crucial role in
shaping the overall dependency and causality structure. Despite the high level
of noise and the short time-series we verified that these networks are
significant with all links statistically validated and with a structural
organisation consistently reproduced across all networks.Comment: 17 pages, 5 figures, 2 table
Bitcoin-specific fear sentiment and bitcoin returns in the COVID-19 outbreak
This study aims to investigate the effect of fear sentiment with a novel data set on Bitcoin’s return, volatility and transaction volume. We divide the sample into two subperiods in order to capture the changing dynamics during the Covid-19 pandemic. We retrieve the novel fear sentiment data from Thomson Reuters MarketPsych Indices (TRMI). We denote the subperiods as pre- and post-COVID19 considering January 13th, 2020, when first Covid-19 confirmed case was reported outside China. We employ bivariate vector autoregressive (VAR) models given below with lag-length k, to investigate the dynamics between Bitcoin variables and fear sentiment.Bitcoin market measures have dissimilar dynamics before and after the Coronavirus outbreak. The results reveal that due to the excessive uncertainty led by the outbreak, an increase in fear sentiment negatively affects the Bitcoin returns more persistently and significantly. For the post-COVID-19 period, an increase in fear also results in more fluctuations in transaction volume while its initial and cumulative effects are both negative. Due to extreme uncertainty caused by the COVID-19 pandemic, investors may trade more aggressively in the initial phases of the shock
Twitter Attribute Classification with Q-Learning on Bitcoin Price Prediction
Aspiring to achieve an accurate Bitcoin price prediction based on people's
opinions on Twitter usually requires millions of tweets, using different text
mining techniques (preprocessing, tokenization, stemming, stop word removal),
and developing a machine learning model to perform the prediction. These
attempts lead to the employment of a significant amount of computer power,
central processing unit (CPU) utilization, random-access memory (RAM) usage,
and time. To address this issue, in this paper, we consider a classification of
tweet attributes that effects on price changes and computer resource usage
levels while obtaining an accurate price prediction. To classify tweet
attributes having a high effect on price movement, we collect all
Bitcoin-related tweets posted in a certain period and divide them into four
categories based on the following tweet attributes: the number of
followers of the tweet poster, the number of comments on the tweet,
the number of likes, and the number of retweets. We separately
train and test by using the Q-learning model with the above four categorized
sets of tweets and find the best accurate prediction among them. Especially, we
design several reward functions to improve the prediction accuracy of the
Q-leaning. We compare our approach with a classic approach where all
Bitcoin-related tweets are used as input data for the model, by analyzing the
CPU workloads, RAM usage, memory, time, and prediction accuracy. The results
show that tweets posted by users with the most followers have the most
influence on a future price, and their utilization leads to spending 80\% less
time, 88.8\% less CPU consumption, and 12.5\% more accurate predictions
compared with the classic approach.Comment: Submitted to a journa
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