Expert answer:Summarize two articles
Expert answer:1. Please read the two articles first2. Summarize each article and every summary needs include at least 150 words.
bitcoins_n3.docx
bitcoins_y2.docx
Unformatted Attachment Preview
Bitcoin Crypto-Currency Value Rising in 2017 as Demand Grows.
NEWS ANALYSIS: Three years after Bitcoin suffered a massive decline in value, the
cryptocurrency is once again valued at more than $1,000 USD
In the early days of 2017, Bitcoin is already reaching new heights as the value of the
cryptocurrency rises. As of 9 AM PT on January 3, Bitcoin was valued at $1,025 U.S
dollars, according to Coinbase, a level not seen since 2013.
Bitcoin’s rebound to once again be worth more than $1,000 is likely being driven by
multiple technical, security and geopolitical factors. The early promise of Bitcoin was to
provide a distributed system that relies on the power of the many to validate and process
transactions.
At the core of Bitcoin is the concept of the blockchain distributed database, which has now
found its way into mainstream enterprise IT. The open-source Hyperledger project which
is run by the Linux Foundation, is one of the leading proponents of the wider use of
blockchain, beyond just Bitcoin.
While financial organizations and IT vendors are beginning to embrace blockchain, no
doubt there has been a ‘halo-effect’ on Bitcoin’s value as well. That said, most blockchain
efforts in enterprise IT today have nothing to do with Bitcoin. In fact, in a 2016 eWEEK
video interview, Brian Behlendorf, executive director of the Hyperledger project,
emphasized that the development of any form of crypto-currency is not part of his group’s
efforts.
Bitcoin has also flourished thanks in part to some of the uncertainty raised by election of
Donald Trump in the U.S and the vote in Britain to exit (Brexit) from the European
Union. Bitcoin demand has also grown in China, which is playing a large role in the
crypto-currency’s rise in value.
On the security front, Bitcoin is the payment method of choice for the vast majority of all
ransomware demands. Multiple vendors in 2016 reported a significant rise in the volume
of ransomware attacks. SentinelOne reported that 50 percent of surveyed organizations
have responded to a ransomware campaign. According to SentinelOne, some organizations
have paid up to the equivalent of $20,000 U.S in Bitcoin to retrieve data lost to ransomware
attacks.
While Bitcoin is a core part of the ransomware landscape, there isn’t necessarily a direct
relationship
between
the
rise
of
ransomware
in
2016
and
the
increase
in Bitcoin’s value. Bitcoin like any other commodity has its value determined by market
economics of supply and demand. Perhaps as demand from organizations rises to
buy Bitcoin as a hedge against potential future ransomware attacks, that demand has
helped in some small way to raise the value of Bitcoin.
Another big factor in Bitcoin’s return to the $1,000 level is the fact that it has not had a
major failure in the past two years. When Bitcoin was last valued over $1,000, the
leading Bitcoin exchange was Mt. Gox.
On Feb. 28, 2014, Mt. Gox revealed that it had suffered a massive attack, losing
750,000 Bitcoins valued at approximately $473 million at the time. The Mt. Gox failure
no doubt had a strong impact on the Bitcoin market and in the overall confidence for the
crypto-currency market.
At the end of 2014, I wrote an analysis of the Bitcoin market at the time, which had
suffered its worst year ever. On Dec. 29, 2014, Bitcoin was valued at approximately $312
and seem likely to fall even lower. Since 2014, there has not been another Mt.Gox incident.
No major Bitcoin exchange has collapsed and while security incidents have occurred, none
have been on the same scale. Back in 2014, I had expected that Bitcoin would still
have value, but I didn’t predict a return to the $1,000 range.
Michael Moeser, Director of Payments Practice at Javelin Strategy and Research said that
he’s not surprised that the value of Bitcoin is now above $1,000. He noted
that Bitcoin’svalue has been steadily increasing since January 2016 when its value was
$420-$440.
“There are a number of factors influencing its growth, including tighter fiat currency
controls, stronger demand and usage and limited increases in Bitcoin supply,” Moesner
told eWEEK. “In terms of impact on the ecosystem, we should expect the price
of Bitcoin to remain strong and potentially increase in 2017.”
“This will be exacerbated even more so as we see greater adoption of Bitcoin being used
to solve cross-border payment challenges,” he said.
PHOTO (COLOR)
~~~~~~~~
By Sean Michael Kerner
Sean Michael Kerner is a senior editor at eWEEK and InternetNews.com. Follow him on
Twitter @TechJournalist.
A summary and/or evaluation: (150-200words)
BITCOIN VALUE ANALYSIS BASED ON CROSSCORRELATIONS
INTRODUCTION
Bitcoin, along with a set of methods of using peer-to-peer computer networks to generate
it, was first proposed as “a system for electronic transactions without relying on trust”,
and in the Internet paper signed by its author under the pseudonym of Nakamoto [1].
Early in its life, in 2010, bitcoin has survived an almost fatal software “bug”, after which
it has recovered to go up into a bubble during 2013-14. After the bubble was broken, its
price is seen to behave modestly to date. Figure 1 shows the rapid growth of bitcoin
transactions worldwide, while Figure 2 shows the evolution of the bitcoin-to-USD rates.
It would soon be characterized as the “world’s first completely decentralized digital
currency” [2-4]. It is exactly the decentralized capacity of bitcoin that has attracted
scientific, legislative and regulatory interest, as it has early been realized that
anonymous/pseudonymous use may cover legal as well as illegal transactions [5-8].
Bitcoins are created by the highly competitive “mining” process where “miners” create
and accumulate bitcoins in reward for services offered to the bitcoin network, such as
providing the hardware basis for secure transactions [2,3]. Today it is conceded that
bitcoin possesses “the characteristics of money (durability, portability, fungibility,
scarcity, divisibility and recognizability) based on the properties of mathematics rather
than relying on physical properties (like gold and silver) or trust in central authorities
(like fiat currencies) [9].
Major questions of practical consequences regarding bitcoin and its future as a (virtual)
“currency” include the following:
* What are the opportunities and threats related to bitcoin?
* Is there a relation or connection between bitcoin price and other commodity values or
indices?
* Is it possible to forecast bitcoin price?
A first aim of this paper is to explore the character of bitcoin through its correlation with
a series of other “established” economic factors, such as gold or crude oil prices or major
stock market indices. Cross-correlation between time series, such as the bitcoin price or
the prices of other goods or stock market indices, is a standard method to use when
searching for similarities shared by two different time series, possibly at lagged time
points. It can also be used to locate a given pattern, considered as a shorter time series,
within a longer time series. Cross-correlation or auto-correlation is exploited as a
measure of similarity in a variety of application fields such as image processing, seismic
and geophysical exploration, source localization, astronomical image alignment,
surveillance, 3-D imaging, biomedical engineering and many others [10,11].
For two real-valued, discrete-time deterministic signals {x(n)} and {y(n)}, crosscorrelation is given by the “inner product” operation at (integer) lag t:
… (1)
For two real-valued, discrete-time stochastic processes {x(n)} and {y(n)}, assumed to be
jointly wide-sense stationary (JWSS), cross-correlation is given by the cross-covariance
function, normalized by the standard deviations of the two processes:
… (2)
Where E{ } denotes the expected value and [ßx,ax} denote the mean and standard
deviation of {x(n)} and {y(n)}, respectively.
Time series can be treated as one-dimensional discrete-time signals with time being the
independent variable. Given the fact that observed (measured) time series values contain
signal as well as (additive) noise, time series are considered as stochastic processes and
cross-correlation is computed as in eq. (2) with expected values estimated via time
averages across observation records of finite length N (in number of samples):
… (3)
Where means and standard deviations are obtained as time rather than ensemble
averages:
… (4)
In order to use this last form to cross-correlate time series observation data, two practical
considerations arise, as to the time length and the spacing apart of the observations taken.
It turns out that it is not necessary that the observations of x(n) and y(n) be of equal
length N; in order for the results to be meaningful, however, it is important to make sure
that both time series are of the same “sampling frequency”. This practically means that
they contain observations taken at the same regular intervals of time (e.g., daily, monthly,
etc.). If necessary, this may be achieved by under-sampling one or both time series down
to a common sampling rate.
EXPERIMENTAL CROSS-CORRELATION AND SWOT BITCOIN ANALYSIS
Materials and Methods
The dataset used for experimental auto- and cross-correlation analysis comes from
http://bitcoincharts.com/ and https://blockchain.info/pl/charts. Three time series are
extracted from these URLs, namely, (i) the price of bitcoin, (ii) the number of bitcoin
transactions and (iii) the bitcoin transaction fees. Data correspond to the time period from
17-08-2010 to 25-01-2015, yielding N=1623 observations in each time series, taken on a
daily basis. Available data corresponding to the initial period of bitcoin existence, 03-012009 to 16-08-2010 or 591 observations (days), are not included because the zero bitcoin
value of that period would distort results if taken into the dataset. Analysis is carried out
in Matlab R2014a.
Cross-correlations between Bitcoin-related Factors
Cross-correlation results for the three time series mentioned above are shown here. In all
cross-correlation plots, values are normalized so that cross-correlation peaks at one
(Figure 3). Note that in contrast to auto-correlation, the cross-correlation does not always
peak at lag zero; moreover, its value at lag zero may be positive or negative.
Figure 3 shows pair-wise cross-correlations between (a) the bitcoin price, (b) the bitcoin
transactions fees and (c) the number of transactions in bitcoin. Cross-correlation between
bitcoin price and bitcoin transactions fees (Figure 3, upper plot) is seen to peak at lag t=0;
moreover, it retains high values for lags up to 250 days, approximately. Such behaviour
reveals that these two time series are indeed correlated, i.e. there is an almost linear
relation between the number of transactions and the bitcoin price. Cross-correlation
between bitcoin price and number of bitcoin transactions fees (Figure 3, middle plot) is
seen to peak around lag t=300; this also signifies a linear relation between the two
factors. Finally, cross-correlation between number of transactions and bitcoin transaction
fees (Figure 3, lower plot) is seen to peak around lag t=400 while it retains very high
values between lags t=0 and lag t=500, revealing the strongest connection. These results
are in agreement to existing research results, e.g., [12,13], where strong correlations
between various bitcoin related factors are experimentally revealed.
Cross-correlations between Bitcoin Price and Other Factors
A set of five (5) different economic factors of interest are selected for cross-analysis to
bitcoin price. These are
1. NASDAQ index,
2. DAX index,
3. S&P500 index,
4. Gold price and
5. Crude Oil price.
Data for these factors are obtained from URL http://www.investing.com/ for the time
period 29-08-2010 to 25-01-2015 (Figure 4). It must be noted that these are weekly
averages rather than daily values, yielding a record of 231 data points (observations) for
each factor.
Figure 4 shows the weekly averages of bitcoin price along with all the other five (5)
factors over time, for the aforementioned time period of 231 weeks.
The following Figures 5-9 show the pair-wise cross-correlations as functions of time lag t
in number of weeks, between the bitcoin price and each of the other five selected factors
of economic interest, namely, NASDAQ index, DAX index, S&P500 index, Gold price
and Crude Oil price.
Figures 5-7 show similarities in the general form of the cross-correlation function: In all
three cases, it peaks at zero lag (t=0) and remains above 0.6 (normalized values) or 60%
of the peak value (non-normalized values) for lags up to 150 weeks or approximately 3
years. This behaviour reveals strong correlation between contemporary bitcoin price and
major stock market indices.
Figures 8 and 9 show similarities between them, as well: Cross-correlation to gold price
peaks at lag t=95 weeks while that to crude oil price peaks at lag t=12 weeks. They both
remain above 0.8 or 80% of their respective peak values for lags up to 160 weeks or
approximately 3 years. This behaviour reveals strong correlation between lagged bitcoin
price and gold or crude oil prices.
SWOT Analysis of Bitcoin
The SWOT analysis of bitcoin combines results obtained from existing research, [24,8,14,15], with results obtained from the cross-correlation analysis presented above.
* Strengths: Worldwide use; increasing number of users; lack of brokers; low transaction
costs; transactions speed; ultimately constant amount of bitcoins in system (anti-inflation
mechanism); regulation by market processes; not possible to be regulated by large
holders; protection of personal data of all participants.
* Weaknesses: Highly dependent on participants’ trust in system; susceptible to
speculative bubbles, as that of 2014; no material form; high value fluctuations;
susceptible to user errors; decreasing reward for users providing computing power to the
system (“miners”); mining using CPU and GPU unprofitable.
* Opportunities: Strong cross-correlations between the numbers of bitcoin transactions
and transaction fees and the bitcoin price; very good cross-correlation of bitcoin price
with gold and crude oil price; correlation of bitcoin price with contemporary stock market
indices like NASDAQ, DAX and S&P500.
* Threats: Exchange rate is impossible to forecast with using standard methods such as
the Holt-Winters method or the Indicators method; bitcoin value shows no signs of
periodicity; number of users and transactions directly affect bitcoin price.
In summary, SWOT analysis shows that bitcoin has more benefits than risks. It is used
worldwide; yet, it is banned from the economies of certain countries. Attempts to regulate
the price of bitcoin have all failed. Moreover, it was proved to be hard to predict bitcoin
price in future periods using standard models for forecasting, [16]. A noticeable fact is
that bitcoin price is very susceptible to its very popularity. Whenever bitcoin would get
publicity in the media, its price would increase. This is one characteristic of a speculative
bubble. Bitcoin price is also closely related to the amount of users, number of
transactions and the amount of transaction fees.
CONCLUSIONS
Bitcoin is a promising yet neither fully understood nor fully matured (virtual) “currency”.
As such, it concentrates an increasing research interest coming both from the (secure)
computer networking world and the economic world. A major concern is that bitcoin
price and bitcoin exchange rate to existing currencies have not been predictable to date.
As a contribution to the discussion on the emerging bitcoin economy, cross-correlation
analysis is carried out in this study between bitcoin-related variables and other standard
economic factors like stock market indices or prices of goods. SWOT analysis of bitcoin,
based on these as well as on existing research results, shows that benefits are more than
risks while the strong correlations revealed in this study constitute considerable
opportunities to be further explored towards the goal of forecasting bitcoin price, number
of transactions or level of transaction fees in the future.
REFERENCES
1. Nakamoto S (2008) Bitcoin: A Peer-to-Peer Electronic Cash System. Available at:
http ://bitco in. org/bitco in.pdf
2. Brito J, Castillo A (2013) Bitcoin: A Primer for Policymakers. Mercator Center,
George Mason Univ, uSa.
3. Velde FR (2013) Bitcoin: A primer. Chicago Fed Letter 317: 1-4.
4. Garcia D, Tessone C, Mavrodiev P, Perony N (2014) The digital traces of bubbles:
feedback cycles between socio-economic signals in the Bitcoin economy. ETH Risk
Center – Working Paper Series 14: 1-16.
5. Barratt Mj (2012) Silk road: Ebay for drugs. Addiction 107: 683.
6. Jacobs E (2011) Bitcoin: A Bit Too Far? Journal of Internet Banking and Commerce
16: 1-4.
7. Barber S, Bayen X, Shi E, Uzun E (2012) Bitter to Better – How to Make Bitcoin a
Better Currency. In: Financial Cryptography and Data Security. Springer 399-414.
8. Reid F, Harrigan M (2013) An Analysis of Anonymity in the Bitcoin System. In:
Security and Privacy in Social Networks. Springer 197-223.
9. We Use Coins (2015) Available at: https://www.weusecoins.com/en/questions/
10. Gouedard P, Stehly L, Brenguier F, Campillo M, Colin de Vardiere Y, et al. (2008)
Cross-Correlation of random fields: Mathematical Approach and Applications.
Geophysical Prospecting 56: 375-393.
11. Lewis JP (1995) Fast Normalized Cross-Correlation. Vision Interface 120-126.
12. Kristoufek L (2013) BitCoin meets Google Trends and Wikipedia: Quantifying the
relationship between phenomena of the Internet era. Scientific Reports 3: 3415.
13. Kristoufek L (2014) What are the main drivers of the Bitcoin price? Evidence from
wavelet coherence analysis, Available at: https://arxiv.org/pdf/1406.0268
14. Bornholdt S, Sneppen K (2014) Do Bitcoins make the world go around? On the
dynamics of competing crypto-currencies. Available at: http://arxiv.org/abs/1403.6378
15. Kondor D, Posfai M, Csabai I, Vattay G (2014) Do the rich get richer? An empirical
analysis of the Bitcoin transaction network. PLoS ONE 9: e86197.
16. Billings SA (2013) Nonlinear System Identification: NARMAX Methods in the
Time, Frequency and Spatio-Temporal Domains. Wiley, ISBN 978-1-118-53556-1.
A summary and/or evaluation: (150-200words)
…
Purchase answer to see full
attachment
How it works
- Paste your instructions in the instructions box. You can also attach an instructions file
- Select the writer category, deadline, education level and review the instructions
- Make a payment for the order to be assignment to a writer
- Download the paper after the writer uploads it
Will the writer plagiarize my essay?
You will get a plagiarism-free paper and you can get an originality report upon request.
Is this service safe?
All the personal information is confidential and we have 100% safe payment methods. We also guarantee good grades