New Age Cloaking

Historically cloaking was considered bad because a consumer would click expecting a particular piece of content or user experience while being delivered an experience which differed dramatically.

As publishers have become more aggressive with paywalls they’ve put their brands & user trust in the back seat in an attempt to increase revenue per visit.

user interest in news paywalls.

Below are 2 screenshots from one of the more extreme versions I have seen recently.

The first is a subscribe-now modal which shows by default when you visit the newspaper website.

The second is the page as it appears after you close the modal.

Basically all page content is cloaked other than ads and navigation.

The content is hidden – cloaked.

hidden content.

That sort of behavior would not only have a horrible impact on time on site metrics, but it would teach users not to click on their sites in the future, if users even have any recall of the publisher brand.

The sort of disdain that user experience earns will cause the publishers to lose relevancy even faster.

On the above screenshot I blurred out the logo of the brand on the initial popover, but when you look at the end article after that modal pop over you get a cloaked article with all the ads showing and the brand of the site is utterly invisible. A site which hides its brand except for when it is asking for money is unlikely to get many conversions.

Many news sites now look as awful as the ugly user created MySpace pages did back in the day. And outside of the MySpace pages that delivered malware the user experience is arguably worse.

a highly satisfied online offer, which does the needful.

Each news site which adopts this approach effectively increases user hate toward all websites adopting the approach.

It builds up. Then users eventually say screw this. And they are gone – forever.

a highly satisfied reader of online news articles.

Audiences will thus continue to migrate across from news sites to anywhere else that hosts their content like Google AMP, Facebook Instant Articles, Apple News, Twitter, Opera or Edge or Chrome mobile browser new article recommendations, MSN News, Yahoo News, etc.

Any lifetime customer value models built on assumptions around any early success with the above approach should consider churn as well as the brand impact the following experience will have on most users before going that aggressive.

hard close for the win.

One small positive note for news publishers is more countries are looking to have attention merchants pay for their content, though I suspect as the above sort of double modal paywall stuff gets normalized other revenue streams won’t make the practice go away, particularly as many local papers have been acquired by PE chop shops extracting all blood out of the operations through interest payments to themselves.

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Quantitative Update: Bitcoin vs. The Rest of the World

This post is meant to be an addition to what I said earlier this year. Here we compare, in the same historical period of existence of bitcoin, Bitcoin vs other assets: us stock market indexes, US stocks of different sectors and Gold.

Let’s start with this summary table, who follow me regularly should already know the meaning of Shannon’s probability, RMS, G yield and compounded annual G yield; for all the others I refer you to the end of the article.
The data have been sorted in descending order according to Compounded Yearly Gain  G.

Comparison Bitcoin vs. The rest of the world
July 17, 2010 – Dec 31, 2019

Asset RMS or Volatility Shannon Probability P Daily Gain G Compounded Yearly Gain  G Optimal Fraction of your capital to wage
Bitcoin               0.0567                       0.5219        1.00087 38% 4.4%
MasterCard               0.0155                       0.5265          1.0007 19% 5.3%
Visa               0.0144                       0.5241          1.0006 16% 4.8%
Amazon               0.0193                       0.5196        1.00057 15% 3.9%
Apple               0.0161                       0.5172        1.00042 11% 3.4%
Google               0.0148                       0.5167        1.00038 10% 3.3%
Microsoft               0.0143                       0.5169        1.00038 10% 3.4%
Nasdaq Composite Index               0.0106                       0.5179        1.00032 8% 3.6%
Standard & Poor’s 500 Index               0.0091                       0.5160        1.00025 6% 3.2%
McDonald               0.0098                       0.5119        1.00019 5% 2.4%
Berkshire Hathaway Inc. (W.Buffett)               0.0105                       0.5112        1.00018 5% 2.2%
Pfizer               0.0115                       0.5078        1.00011 3% 1.6%
Facebook               0.0226                       0.5080        1.00010 3% 1.6%
Tesla               0.0318                       0.5096        1.00010 3% 1.9%
JPMorgan               0.0155                       0.5070        1.00010 2% 1.4%
Intel               0.0153                       0.5040        1.00001 0% 0.8%
**Gold (XAUUSD)               0.0094                       0.4951        0.99986 -3% 0%
*Ethereum               0.0634                       0.5138        0.99974 -6% 0%
General Motors               0.0178                       0.4903        0.99950 -12% 0%
General Electric               0.0164                       0.4868        0.99943 -13% 0%

*Ethereum Data since Aug 7, 2015, source coinmarketcap.
**Gold since 1970 has been a bit better with +3% yearly compounded gain.

The first comparison to make is with the main competitors of bitcoin, credit cards. I’m surprised to see how good are quantitative parameters of Mastercard and Visa, on the other hand they are monopolies, perhaps that’s why the CEO of mastercard hates so much Bitcoin, he sees it as a strong threat. Even Amazon has worse parameters compared to Visa and MC.

I included only Ethereum  in the comparison because in terms of market cap is second to Bitcoin, its yearly yield G is negative and i’m not surprised because I remind you that volatility reduces by far the yield G and in the case of all altcoins, not only Ethereum, the volatility reaches very high levels and therefore as an investment vehicle altcoins in general are absolutely not recommended, can eventually be considered as purely speculative assets for short-term trading.

Unfortunately for Mr.P.Schiff, in the last ten years Gold performed badly, for your curiosity i computed Gold parameters using available daily data since January 1970 and its yearly gain G or yield has been +3%, nothing exceptional, basically Gold protected you against inflation in the last fifty years but nothing more then this.

As i said 20 days ago Bitcoin volatility is dropping but it remains very high compared to other assets, despite this Bitcoin yearly compounded gain G is an astonishing +38% and it’s the best investment vehicle of the world.
Compared to other bitcoin price models this value is not much, ten years from now compounding 38% yearly bitcoin should be at around 200k usd while, for example, the stock to flow model has a forecast of 10 millions usd after 2028 halving, this is the equivalent of 144% yearly compounded gain instead of 38%.
Let me know what you think, does the stock to flow model price return appear realistic to you or not? Personally i prefer to rely on numbers and they say a clear “no” to me. This is why i’m a bit skeptic about also the bitcoin price model i developed on tradingview but i’m curious to see how it’ll end in a couple of years.

Tech Addendum

The concept of entropic analysis of equity prices is old and it was first proposed by Louis Bachelier in his “theory of speculation”, this thesis anticipated many of the mathematical discoveries made later by Wiener and Markov underlying the importance of these ideas in today’s financial markets. Then in the mid 1940’s we have had the information theory developed by Claude Shannon , theory that is applicable to the analysis and optimization of speculative endeavors and it is exactly what i’ve done just applied to bitcoin and the other assets considered in the above table, especially using the Shannon Probability or entropy that in terms of information theory, entropy is considered to be a measure of the uncertainty in a message.
To put it intuitively, suppose p=0, at this probability, the event is certain never to occur, and so there is no uncertainty at all, leading to an entropy of 0; at the same time if p=1 the result is again certain, so the entropy is 0 here as well. When p=1/2 or 0.50 the uncertainty is at a maximum or basically there is no information and only noise.

Applying this entropy concept to an equity like a stock or a commodity or even bitcoin itself common values for P are 0.52 that can be interpreted as a slightly persistence or tendency to go up, this means that for example stock markets aren’t totally random and up to some extend they are exploitable, same for btc.
Knowing the entropy level of bitcoin/usd is crucial if we want to compute its main quantitative characteristics, as i explained in the technical background of my blog this process is quickly doable once you have all the formulas, the process is as follows:

To compute the Shannon Probability P you should follow these steps:

  1. compute natural logarithm of data increments (today price / yesterday price)
  2. compute the mean for all data increment computed in step 1
  3. compute RMS (root mean square) of all data increments, squaring each data increment and sum all togheter
  4. Compute price momentum probability with the formula P = (((avg / rms) – (1 / sqrt (n))) + 1) / 2
    where avg = data computed in step 2, rms = data computed in step 3, n = total samples of your dataset. If the resulting probability is above 0.5 then there is positive momentum, otherwise under 0.5 negative momentum

To compute the Gain Factor use the following formula:

G = ((1+RMS)^P*((1-RMS)^(1-P))

To compute the yearly gain G or growth just raise daily gain G to the 365th power for Bitcon or 252 for stocks (252 trading days in a year).

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Coronavirus ebook news roundup

Here are a few coronavirus-related ebook news stories I ran across today.

Library ebook lending service ProQuest has announced that its Ebook Central program has partnered with over 150 publishers to provide unlimited library lending access to their titles for the duration of the virus epidemic.

ProQuest Ebook Central customers impacted by COVID-19 get unlimited access to all owned titles from these publishers through mid-June. This means that all licenses – including single-user and three-user models – have automatically converted to unlimited access for that period, helping librarians bridge the gap for their patrons in this rapidly changing environment. The unlimited access also applies to additional titles purchased through mid-June.

Image-1.jpgUK paper The Sun reports that Apple is providing free ebooks and audiobooks to US and UK residents via the bookstore of Apple’s own Apple Books app. I checked it out myself, and there are indeed a lot of free titles there. I’m sure that many of them were already free as promotions even before the coronavirus epidemic, much the same way that Baen’s Free Library is, but still, any good source of free reading material can only help people who need to find more things to do to fill up their time.

South Korean ebook startup Millie’s Library is offering free access to its 50,000 titles for two months, as a way of giving back to society. The service normally costs $8 per month. It is also working with South Korea’s government to provide access to its service through the government’s smartphone app for residents under quarantine.

Meanwhile, just as crisis situations bring out the best in some, they can bring out the worst in others. Independent reports that Amazon has seen a huge uptick in plagiarized self-published works about the coronavirus epidemic, as unscrupulous operators copy and paste online news stories or make ebooks of short articles on corona-related subjects in order to make a quick buck (or pound) from worried readers.

Not all the new corona-related ebooks are for profiteering. The UK paper Express & Star reports that Cornish mother and children’s book author Ellie Jackson was so worried about the impact of the virus on children that she was moved to write a children’s ebook about it. The book, The Little Corona King, seems to be available on Apple Books and Google Play Books but I don’t see it anywhere else at the moment. (I wonder if any preventative measures Amazon might have imposed to try to stop coronavirus profiteering are keeping it off Amazon.)

Photo by Anna Shvets on Pexels.com


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My Bitcoin Price Model Part II

For those who follow me on twitter know that my bitcoin price model v1.1 that I presented on this blog last September 2019 has been invalidated by the recent low of March 13 at $3850.  I use 95% confidence level bands around my model forecast and that day the lower confidence level has been violated thus invalidating my model.
Since that day I have at various times pondered how to improve my old model and I recycled an idea that came to my mind last year when I presented the first model.
This idea is not to use the time factor to calculate the price of bitcoin but instead use the number of existing bitcoins that as you know grows over time and halves about every 4 years (until now it happened in 2012,2016 and 2020).
In doing so I discovered that there is a fairly strong linear relationship between the logarithm of the bitcoin price and the number of existing bitcoins at that particular moment.

All the important bitcoin bottoms are inside the 95% confidence bands (dotted lines)

With the software i use isn’t complicated to find a formula that approximate all the selected bitcoin bottoms.
This is the dataset used to compute the model:

Date Low Bitcoin Supply
2010/07/17 $0.05 3436900
2010/10/08 $0.06 4205200
2010/12/07 $0.17 4812650
2011/04/04 $0.56 5835300
2011/11/23 $1.99 7686200
2012/06/02 $5.21 9135150
2013/01/08 $13.20 10643750
2015/08/26 $198.19 14536950
2015/09/22 $224.08 14637300
2016/04/17 $414.61 15439525
2016/05/25 $444.63 15582350
2016/10/23 $650.32 15943563
2017/03/25 $889.08 16235100
2019/02/08 $3,350.49 17525700
2018/12/15 $3,124.00 17423175
2019/03/25 $3,855.21 17608213
2020/03/13 $3,850.00 18270000

The Formula is a very simple one, a first order price regression  between log(Low) and Bitcoin supply:

Where:
FPL = expected line where bitcoin is fairly priced
intercept = a costant
c1 = another coefficient that defines the slope of the Bitcoin supply input.

Here’s the resulting model after computing the parameters of the above formula.

This is the new bitcoin price model “FPL Line” v1.3 applied to a monthly bitcoin/usd chart:

Next Step: Computing the formula for the TopLine

The formula for computing the Top is:


Where:
TopLine= is the forecasted price where the next long term top might be.
intercept = a costant
c1 = another coefficient that defines at which pow the bitcoin supply is elevated

This formula is different from the one used to compute the FPL or bottom line. I’ve seen that there is not a strong linear relationship betweel the logarithm of important Bitcoin Tops and the Bitcoin supply, so i decided to switch to the formula used for the old model and it works better.

This is the dataset used to compute the model:

Date Price Bitcoin Supply
2010/07/17  $      0.05 3436900
2011/06/08  $      31.91 6471200
2013/11/30  $      1,163.00 12058375
2013/12/04  $      1,153.27 12076500
2017/12/19  $    19,245.59 16750613

Here’s the resulting model after computing the parameters of the above formula.

This is the new bitcoin price model “Top Line” v1.3 applied to a monthly bitcoin/usd chart:

95% Confidence Error Bands

With the indicator that i give you for TradingView i included also the error bands.
This are the error bands for the TopLine:

And for the bottom line or FPL (FairPriceLine)

It is quite obvious that with fewer points available the error bands for the TopLine are wider and less accurate compared to the FPL error bands where I have more points (17 instead of 5).

TradingView Indicator

I have also included an indicator for TradingView to give you the opportunity to experience the concepts and model illustrated in this update. You can also check the code and/or modify it as you like.

On April 10th, 2020 tradingview staff decided to censor my indicator and threatened to close my account, because of this i publish here the code so you can create your own indicator by yourself.

Bitcoin Model v1.3 Sourcecode:

Code is also available at pastebin

Remember to add a “TAB” key once before stock (line 10 and 13), in the process of copying and pasting data back and forth from tradingview the tab key is gone probably because there is not a tab code in HTML.

//@version=2

study(“Bitcoin Price Model v1.3”, overlay=true)

//stock = security(stock, period, close)
stock = security(“QUANDL:BCHAIN/TOTBC”,’M’, close)

if(isweekly)
//insert “TAB” key before stock
stock = security(“QUANDL:BCHAIN/TOTBC”,’W’, close)
if(isdaily)
//insert “TAB” key before stock
stock = security(“QUANDL:BCHAIN/TOTBC”,’D’, close)

FairPriceLine = exp(-5.48389898381523+stock*0.000000759937156985051)

FairPriceLineLoConfLimit = exp(-5.86270418884089+stock*0.000000759937156985051)
FairPriceLineUpConfLimit = exp(-5.10509377878956+stock*0.000000759937156985051)

FairPriceLineLoConfLimit1 = exp(-5.66669176679684+stock*0.000000759937156985051)
FairPriceLineUpConfLimit1 = exp(-5.30110620083361+stock*0.000000759937156985051)

plot(FairPriceLine, color=gray, title=”FairPriceLine”, linewidth=4)

show_FPLErrorBands = input(true, type=bool, title = “Show Fair Price Line Error Bands 95% Confidence 2St.Dev.”)
plot(show_FPLErrorBands ? FairPriceLineLoConfLimit : na, color=gray, title=”FairPriceLine Lower Limit”, linewidth=2)
plot(show_FPLErrorBands ? FairPriceLineUpConfLimit : na, color=gray, title=”FairPriceLine Upper Limit”, linewidth=2)

show_FPLErrorBands1 = input(false, type=bool, title = “Show Fair Price Line Error Bands 68% Confidence 1St.Dev.”)
plot(show_FPLErrorBands1 ? FairPriceLineLoConfLimit1 : na, color=gray, title=”FairPriceLine Lower Limit”, linewidth=1)
plot(show_FPLErrorBands1 ? FairPriceLineUpConfLimit1 : na, color=gray, title=”FairPriceLine Upper Limit”, linewidth=1)

TopPriceLine = exp(-30.1874869318185+pow(stock,0.221847047326554))
TopPriceLineLoConfLimit = exp(-30.780909776998+pow(stock,0.220955789986605))
TopPriceLineUpConfLimit = exp(-29.5940640866389+pow(stock,0.222738304666504))

TopPriceLineLoConfLimit1 = exp(-30.3683801339907+pow(stock,0.221575365176983))
TopPriceLineUpConfLimit1 = exp(-30.0065937296462+pow(stock,0.222118729476125))

plot(TopPriceLine, color=white, title=”TopPriceLine”, linewidth=2)

show_TOPErrorBands = input(false, type=bool, title = “Show Top Price Line Error Bands 95% Confidence 1St.Dev.”)
plot(show_TOPErrorBands ? TopPriceLineLoConfLimit : na, color=white, title=”TopPriceLine Lower Limit”, linewidth=1)
plot(show_TOPErrorBands ? TopPriceLineUpConfLimit : na, color=white, title=”TopPriceLine Upper Limit”, linewidth=1)

show_TOPErrorBands1 = input(false, type=bool, title = “Show Top Price Line Error Bands 68% Confidence 1St.Dev.”)
plot(show_TOPErrorBands1 ? TopPriceLineLoConfLimit1 : na, color=white, title=”TopPriceLine Lower Limit”, linewidth=1)
plot(show_TOPErrorBands1 ? TopPriceLineUpConfLimit1 : na, color=white, title=”TopPriceLine Upper Limit”, linewidth=1)

Forecast up to 2032

Bitcoin Model 1.3

This is a forecast up to 2032 halving, price will saturate between 27,000$ and 130,000$ with a maximum possible peak at 450,000$ in case of a strong bubble.

Conclusions

This model is clearly experimental, we will see in the future how it will behave. It is probably questionable my choice to use the existing bitcoin supply instead of using time as a main input for the model, I’m curious to know your opinion about it. Thank you.

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The Internet Archive’s ‘National Emergency Library’ expands on Open Library’s copyright violations

A few days ago, the Internet Archive announced a change to its Open Library ebook lending program. Until the announcement, this program had worked by treating treating ebooks as substitutes for the paper books the Archive held, and only lending out one electronic copy for each paper copy it had on hand—a practice called Controlled Digital Lending.

During the coronavirus epidemic crisis, however, the Archive announced it would suspend this limitation. Under the new terms, each user can check out up to ten ebooks at a time, but there is no limitation on how many copies of each ebook can be checked out at once by different users. Calling this the “National Emergency Library,” the Archive explained it was meant to benefit students and the general public during this time of crisis.

This announcement came in for a good amount of uncritical news coverage from sources like Forbes and NPR. However, is this really as praiseworthy as it seems? Ars Technica pays a little more attention to the issues in its own story, pointing out much the same thing that I did several years ago: this “ebook lending library” is cheerfully violating copyrights left and right.

Some publishers and authors are encouraging greater fair use of their works during the corona crisis, but the Internet Archive’s actions here are considerably more extreme. While it might have given a nod to the idea of fair use by only lending one ebook at a time for each paper copy it owns, there is no precedent for fair use rights to permit controlled digital lending of copyrighted content—and the Archive has now discarded even that fig leaf with its new temporary “borrow as much as you want” regime.

Just over two years ago, the Authors Guild finally noticed what was going on and issued warnings and complaints, but didn’t take any further legal action. It has now issued another announcement, viewing this new development with alarm.

IA is using a global crisis to advance a copyright ideology that violates current federal law and hurts most authors. It has misrepresented the nature and legality of the project through a deceptive publicity campaign. Despite giving off the impression that it is expanding access to older and public domain books, a large proportion of the books on Open Library are in fact recent in-copyright books that publishers and authors rely on for critical revenue. Acting as a piracy site—of which there already are too many—the Internet Archive tramples on authors’ rights by giving away their books to the world.

In its National Emergency Library FAQ list, the Archive provides a contact email for authors or publishers to request that their books be removed. It does not address the question of legality. As I noted in my previous piece, Internet Archive founder Brewster Kahle is a long-time copyright reform advocate, and one of the ways such people push for reform is by staking out a position just outside what the law permits and trying to push as far forward as they can.

The odd thing is that in all this time, nobody has yet filed any copyright lawsuits over it. Even the Authors Guild hasn’t gone to court despite knowing about what the IA was doing for at least two years now—and this is the same organization that was so incensed over Google daring to scan copyrighted books for search indexing purposes that it took Google and its partner university all the way to the Supreme Court (and ultimately lost both cases).

But lawsuits do cost a lot of money, and the Authors Guild’s legal fees in the Google dispute couldn’t have been cheap. Is the Authors Guild still stinging over those defeats? Does it think that if it lost a fair use case against a commercial entity like Google, it would have even less hope of prevailing over a nonprofit like the Internet Archive? Or maybe it’s just that there are more pressing matters for the courts right now than copyright lawsuits.

In any case, as The Digital Reader points out, plenty of authors aren’t happy that the Internet Archive is providing access to their works for free when they are being hit hard by the epidemic right along with everybody else. There are many other legitimate methods of obtaining ebooks for free, via library-related services like Overdrive and Hoopla Digital, that will earn the authors and publishers money.


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