BUIP043: Exploring the Bitcoin Network Proposer: Dr Saralees Nadarajah, @S Nadarajah Sponsor: Peter Rizun Submitted: 2016-12-23 Status: closed
Project Title:’’’ Exploring the Bitcoin Network.
Bitcoin Address: 12w4PcnfSC13yPvTxDDATjeYJ8qj742Bhb
Motivation: Over the past few months there has been a significant
growing notable interest in Bitcoin. For example, the UK government is
considering paying out research grants in Bitcoin; an increasing number
of IT companies are stockpiling Bitcoin to defend against ransomware;
growing numbers in China are buying into Bitcoin and seeing it as an
investment opportunity. Perhaps most significantly, the Chair of the
Board of Governors of the US Federal Reserve has been encouraging
central bankers to study new innovations in the financial industry. In
particular, they expressed a need to learn more about financial
innovations, including Bitcoin, Blockchain, and distributed ledger
technologies. With this recent surge in interest, we believe that now is
the time to start studying Bitcoin as a key piece of financial
technology, and not just as a novelty.
Objectives: Expand on existing research and analysis of the Bitcoin
network. The focus will be on three main objectives: i) analyse the
distribution of the Bitcoin network - distribution of degrees,
transaction frequency, transaction sizes, costs, scalability, etc; ii)
investigate using Extreme value and quantile regression methods which
could be used to detect fraudulent transactions and anomalies in the
network, by examining characteristics of Bitcoin addresses; iii) analyse
speculative behaviour in the Bitcoin network, Bitcoin transactions, and
Project Duration: We expect the project to be completed within 12
Project Team: Dr Saralees Nadarajah, Senior Lecturer, School of
Mathematics, University of Manchester, M13 9PL, UK; Dr Stephen Chan,
EPSRC Doctoral prize Fellow, School of Mathematics, University of
Manchester, M13 9PL, UK; Jeffrey Chu, PhD research student, School of
Mathematics, University of Manchester, M13 9PL, UK.
Summary of Current Work: We have already performed a preliminary
statistical analysis of the exchange rate of Bitcoin against the US
dollar, using a wide range of known parametric distributions in finance.
We believe it is the most comprehensive using parametric distributions
for any kind of exchange rate data. This was motivated by the fact that
there exist many studies investigating the best fitting distributions
for the exchange rates of major currencies; however, there are none
(that we are aware of) for the exchange rate of Bitcoin. In addition,
the exchange rate of Bitcoin versus the US dollar appears to behave very
differently to the exchange rates of other major currencies. Using daily
Bitcoin exchange rate data from September 2011 to May 2014
(approximately two and a half years) from the Bitstamp exchange, our
results showed that the generalised hyperbolic distribution gave the
best fit to the data, being consistent with the observation that Bitcoin
exchange rates have somewhat complicated dynamics. Given our preliminary
results, we believe that there is great scope to extend this analysis
through more complex mathematical and computational methods.
Description of Activities: To achieve the objectives stated above,
we will complete the following activities:
Review existing literature on approaches to scaling of Bitcoin.
Collect the complete Bitcoin network data from its inception to
present. This should include all Bitcoin addresses and
transactions since Bitcoin was created.
Collect the data on the cost of setting up a bitcoin node and the
ongoing running and maintenance costs.
Sort and clean data, creating specific data sets containing the
degrees of each Bitcoin address, number of transactions in and out
of each address, the sizes of all transactions etc.
Fit a wide range of parametric distributions to each of the data
sets, find the most appropriate fit.
Analyse and estimate the cost of running a node for different
periods in Bitcoins history (Expected to finish by month 3-4).
Analyse the Bitcoin transaction graph, and model the number, size
and time of transactions, and the price of Bitcoin to examine
whether individuals buy into Bitcoin to profit from its high
Prediction and forecasting of the costs of running nodes in the
future, based on the results of the analysis in the above tasks
(Expected to finish by month 4-5).
Review existing literature on anomaly detection, and its application
to financial markets.
Analyse the Bitcoin network graph to identify any patterns in
transactions which may indicate money laundering behaviour —
e.g. when one user in the network performs transactions with many
other users, who then each perform transactions with another
Examine Bitcoin addresses with significantly different
characteristics from others: transaction frequency or number of
times an address pays or receives Bitcoins over a fixed time
period; node degree or the number of users an address performs
transactions with; transaction volume or the value of the
transactions that an address is involved in.
If these characteristics are significantly different then they could
indicate anomalies, and could give an indication of the overall
health of the Bitcoin system and whether there are attacks on the
Bitcoin network (Expected to finish by month 5-8).
Investigate appropriate methods in operational research which can be
utilised in determining the optimal time to set scaling in the
context with price. Also utilise quantile regression methods to
analyse the transactional quantiles and provide an indication of
when to scale.
Spatial analysis to study nodes globally and in regions of
particular interest (Expected to finish by month 8-12).
Anticipated Challenges and Uncertainties:
We require the latest Bitcoin network data, however, we will need to
determine a cut-off point as new Bitcoin transactions will be
Obtaining the whole Bitcoin data set may take significant time, in
addition to modelling and constructing the Bitcoin network from
the data. Analysing this graph will be time consuming due to the
size of the graph and data.
Modelling the Bitcoin transactions and price of Bitcoin will require
the analysis of high frequency Bitcoin transaction data, as it is
assumed that trading of Bitcoin for profit will be similar to the
that of traditional financial securities.
Obtaining and estimating the exact cost for running node may be
complex as some costs such as time, effort, and utility may not
have specifically defined values. These value themselves may need
to be estimated based on real data.
Budget: The total amount requested for the proposed work is $15,000.
We anticipate for results produced by this funding to be published in
relevant leading journals. $1000 will cover the potential publication
fees for journals. We will attend and present our results at one UK
conference. The corresponding costs for the UK conference are 3 x $300
for travel; 3 x $200 for accommodation/subsistence; 3 x $300 for
registration fees. The $11,600 would cover the compensation for the
research time of Research Assistants (RA), over a 12-month academic
period. The main objectives of the RA will be to obtain all the relevant
Bitcoin data and conduct the analysis and estimations. I will be
overseeing the project management and involved in the research itself.
The total compensation for the RAs is costed at the basic salary,
starting level for this grade.
Impact: We believe that our proposed work would have a positive
benefit for academics and also the Bitcoin community (miners and
industry). We feel that our work could contribute to discussions on the
scalability of Bitcoin unlimited from the perspective of the cost of
running Bitcoin nodes, identifying optimal time for scaling, fraud
detection and many others factors.
[edit renaming the BUIP to a temporary name until sponsorship is