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[–]gwern[S] 0 points1 point  (1 child)

In this paper, we identify factors associated with variations in cryptocurrencies’ market values. In the past, researchers argued that the “buzz” surrounding cryptocurrencies in online media explained their price variations. But this observation obfuscates the notion that cryptocurrencies, unlike fiat currencies, are technologies entailing a true innovation potential. By using, for the first time, a unique measure of innovation potential, we find that the latter is in fact the most important factor associated with increases in cryptocurrency returns. By contrast, we find that the buzz surrounding cryptocurrencies is negatively associated with returns after controlling for a variety of factors, such as supply growth and liquidity. Also interesting is our finding that a cryptocurrency’s association with fraudulent activity is not negatively associated with weekly returns—a result that further qualifies the media’s influence on cryptocurrencies. Finally, we find that an increase in supply is positively associated with weekly returns.

My first question is: why think that only buzz or 'innovation potential' can drive price changes? It's perfectly possible for price changes to drive buzz. I think of every other time that I visit /r/bitcoin... There was a massive amount of media coverage over the years for various milestones - $1b market cap, $100/bitcoin, $1000/bitcoin etc etc. And lots of people apparently only heard of and became interested in Bitcoin after reading articles about it, including of course a lot of speculators and lay people who probably shouldn't've been interested. You would normally think that it would be a feedback cycle: buzz can drive price changes and innovation potential (developers are people too! and cryptocurrency devs need jobs, which will be for successful cryptocurrencies, and even for the hobbyists - why work on a failing cryptocurrency?), and price changes can drive buzz and innovation potential, and of course innovation potential can also drive buzz and price changes. I don't see any obvious way to apportion causal responsibility unless you can find some sort of clever natural experiment like half the money supply being accidentally destroyed or some randomized bit of mass media coverage.

Also, the idea of controlling for supply is weird in the context of Bitcoin. One of the points of Bitcoin is that the supply increases at a stable pre-programmed rate; the only way to increase the supply faster than predicted is to bring more hashpower online (since the difficulty resets every few weeks, anyone who sets up a new mining farm temporarily causes blocks to happen faster than every 10 minutes) which is... something you would do if the price increases (because then the block reward increases in real value, encouraging miners to turn on more hashpower, mining more blocks faster, until the reset happens and the required electricity goes way up and makes the block reward no longer unusually profitable). This is why there are actually quite a few more bitcoins in existence than you would calculate starting from the first block in 2009 and multiplying by the expected number of blocks - miners have brought online so much hashpower that many rewards were mined before re-adjustments. So in Bitcoin and other PoW-based coins, a price increase can cause a supply increase, making it questionable to 'control' for deltas.

Many have warned against the “buzz factor” surrounding cryptocurrencies, which could cause large demand shocks in the short term [4, 6, 7]. For instance, bursts of media visibility [8] can attract waves of new users, and this movement can be partly anticipated by various market actors, such as cryptocurrency traders, thereby leading to price bubbles. For instance, a BBC writer speculated: “Has Bitcoin’s rising profile boosted its price? Michael Jackson, a partner at Mangrove Capital Partners says some changes in the price of Bitcoin have clearly been because of demand fuelled by media coverage” [9]. But the “buzz factor” hypothesis obfuscates the possibility that cryptocurrencies may also gain value and generate returns because they entail a true innovation potential. For instance, the technology underlying bitcoin enables fast international value transfers at very low fees (<1%) compared with the fees levied by banks and other payment-processing companies (e.g., Western Union can charge fees of up to 9%). Thus, cryptocurrencies are not only scarce but also potentially useful, which is likely to drive up their demand independently of short-term media cycles [10–11].

Well sure, of course that has value, but why would that drive any change in the price? Bitcoin, technologically speaking, has scarcely changed at all in the past few years. That is, in fact, its biggest problem - blocks are the same size; hardly anyone uses the few new features like nlocktime; lightning networks, segwitness, Rootstock, and sidechains in general are all still technologies of the future (and given the stalemate over block sizes, may always be technologies of the future). Since the technology is the same, the change in Bitcoin's value as a payment medium & store of value would seem to come from network effects... or buzz and greater adoption (as signaled by a rising price - more demand for a fixed supply)?

Ripple (XRP) represents an interesting case in point, with a team of developers managed by a for-profit organization called Ripple Labs, and a verification process that does not rely on mining to achieve consensus. The fifth wave, which started in 2014, consisted of cryptocurrencies seeking to combine advantages introduced in previous waves (e.g., instantaneous processing, use cases beyond payments) without compromising on openness and public auditability. Stellar (STR), created in August 2014, illustrates this endeavor well.

Stellar doesn't illustrate it at all. It's nothing but an explicit fork/clone of Ripple by Jed McCaleb after falling out with Ripple. It brings nothing to the table except getting rid of the Ripple Labs premine and is not an illustration of any wave. Also, no one uses it.

The selection of 5 coins (Bitcoin/Litecoin/Peercoin/Ripple/Stellar) is a little odd. Why Stellar, and not Doge or Ethereum or Litecoin or Monero? Doge was the epitome of hype and media-driven cryptocurrency growth, while Ethereum has evolved frantically, making it the epitome of a 'innovation potential' driven coin and Monero has a major selling point in being anonymous and Litecoin is one of the oldest large active cryptocurrencies & introduced several innovations of its own (scrypt-based PoW and faster blocks, the former of which was bungled and proven to be a flawed or useless change). But there must be dozens of cryptocurrencies which had larger market caps and more use than Stellar, which is redundant with their inclusion of Ripple as well.

Table 1 below summarizes basic information about the five cryptocurrencies examined in this study, which together account for more than 90% of the total market capitalization of all cryptocurrencies tradeable online as of 27 September 2015 [13].

Well, yes, that's because Bitcoin represents like 90%+ of the total market capitalization of all cryptocurrencies. Why even bother including the others? How on earth did they pick these? The selection seems totally arbitrary. (Serious question: was there ever any media coverage or community interest which could be measured for Peercoin? If the other coins were thrown out, a lot of measurement issues would be avoided, and the time series could be extended back years.)

For these reasons, our dependent variable, weekly returns, is computed as [Pricet+1 –Price t]/ Price t. In the next section, we model weekly returns as a linear combination of various supply- and demand-side variables.

The time-series model is very simple, so I can't complain about that. It's just unrealistic and unmotivated. How did they decide on using only 1-week lags? And no moving average? Most time-series will have more lags than that (the price is totally independent of increases/decreases from two weeks ago? there's no lag or momentum? really? despite all the Bitcoin bubbles?) and moving averages are very effective at forecasting (this is why it's ARIMA models).

We acquired data from CoinGecko.com, a leading source of information on cryptocurrencies. CoinGecko systematically collects data on various cryptocurrencies, including information on trading volume, price, market capitalization, and quantity in circulation. CoinGecko founders also developed and validated four longitudinal, multidimensional indicators to capture liquidity, developer activity, community support, and public interest [18]. For instance, the CoinGecko web application connects to the official application program interfaces (APIs) from Reddit, Facebook, Twitter, Github, and Bitbucket to continuously update the values taken by its indicators over time. For price and volume data, the API of a third-party price data provider is used. Market capitalization data were obtained from Coinmarketcap.com. Finally, we used the Factiva database to collect media coverage data on each cryptocurrency...We then captured cryptocurrencies’ innovation potential using eight indicators of technological development available in our CoinGecko data, including the number of unique collaborators contributing code to the project, the number of proposals merged in the core codebase, the number of issues raised by the community about the code and fixed by the developers, or the number of forks (for a full list of indicators, see Empirical Analyses below).

[–]gwern[S] 0 points1 point  (0 children)

Never heard of CoinGecko, and the supposed 'validation' reference isn't online, but Github is an imperfect measurement of developer activity and an even more imperfect measure of technical innovation. Especially since, you know, none of that actually measures innovations, just churn in the codebase and public talk. (Plus, if they're using Coinmarketcap, they could've used tons of cryptocoins, not such a weirdo selection.) When I look at Table 3, the correlations of their various variables, why does 'negative publicity' correlate r=.59 with 'technological development' and r=.92 with 'alternative interest (community)'? And liquidity is highly correlates with 'public interest', 'negative publicity', 'technological development', 'alternative liquidity' and 'alternative interest'. What this tells me is that despite their attempts to split out 'public interest', 'negative publicity', and 'technological development', all I see is a single buzz/activity variable. I mean, what else are we supposed to think? That the general public and programmers hear about something like MtGox or Shrem being arrested and go, 'oh, Bitcoin! that sounds awesome! I want to buy/program some of that'? A factor analysis would likely bear it out, showing that those load on a single factor, and then are independent of weekly returns/supply growth, which are correlated. A multicollinearity test does not address this issue; it merely means that splitting the publicity factor over multiple variables doesn't make the estimates unstable, but it does mean that which variable gets assigned the most variance is fairly arbitrary.

Note that CoinGecko weighted each of the eight indicators of technological development to reflect each indicator’s relative importance. In addition, more weight is given to indicators that would be more difficult to manipulate. Due to a confidentiality agreement with CoinGecko, we are unable to reveal the exact weightings, which they consider to be proprietary information.

Oy vey. So the validation is probably meaningless anyway, as they won't give out 'proprietary information'.

As mentioned earlier, the evolution of supply for each cryptocurrency comprises a large predictable component, which can easily be anticipated by market participants and thus should not affect price or returns. However, for mineable cryptocurrencies such as BTC, LTC, and PPC, the mining difficulty is adjusted periodically to maintain a target for block validation (e.g., 10 minutes for BTC) that is independent of the intensity of mining activity (e.g., new mining rigs entering or exiting the market). These adjustments go in hand in hand with temporary deviations from the average block validation time, which cause unexpected variations in supply in the short term. For non-mineable cryptocurrencies such as XRP and STR, the surprise element comes from the previously unannounced distribution of coins by the developers’ team, which can also have short-term effects on price and returns. To capture the unexpected variations in supply, we computed supply growth as [Supplyt+1 –Supply t]/ Supply t, using CoinGecko’s indicator of “Supply,” which measures the number of coins actually in circulation at any point in time.

The claim about Bitcoin's supply being fixed is wrong, as I've previously commented. The attribution to random noise in blocks makes no sense: they are working on weekly timescales, and over a week there will be 10*6*24*7=10,080 blocks on average and by the law of large numbers, the random fluctuations will be tiny; and this tiny deviation would measured by simply counting the blocks! What is this about measuring 'the number of coins actually in circulation at any point in time'? Are they using a count of Bitcoin total transaction volume as the measure of supply?! (Wow, I can't imagine any reason why the amount of coins flying around would have anything to do with price increases... /s). So through both miners bringing hashpower online and by measuring velocity/total transaction turnover, as including "unexpected variations in supply" you are also measuring public demand/buzz and hence hiding away its influence.

To capture negative publicity, we hired a graduate research assistant to count how many media articles were published each week that associated the name of a given cryptocurrency with some form of suspicious or fraudulent activity, using appropriate keyword searches in the Factiva database (i.e., “Bitcoin” AND (“fraud*” OR “hacked” OR “Ponzi” OR “scam” OR “theft”)...We wanted to further validate our use of CoinGecko’s public interest indicators and of our own negative publicity variable to capture the “buzz factor” surrounding cryptocurrencies. To that end, we collected from the Factiva database the total weekly number of articles mentioning each cryptocurrency—arguably a good measure of media visibility (i.e. Factiva combines more than 36,000 media sources). Table 7 below shows how our two primary indicators of the “buzz factor”, public interest and negative publicity, correlate with such media visibility, as well as with the alternative indicator of interest we termed community interest. Pairwise correlations range between 0.86 and 0.93, indicating high levels of internal validity.

By definition, their 'total mentions' number will correlate with their 'negative publicity' number, because the former is made up of the latter + positive publicity. After all, the number of Bitcoin articles cannot be less than the number of negative Bitcoin articles.

Surprisingly, negative publicity is not significantly associated with returns. Put simply, we do not find any evidence that bad press affects price. However, given that negative publicity is highly correlated with public interest, we reran the full model without negative publicity to see whether the channel through which the latter affects returns is related to public interest...As shown in Table 6‘s model 7 below, the effect of public interest remains substantially the same with or without negative publicity, which indicates that the two are largely independent.

Wrong. Adding a variable to a regression model is not adding a mediation and cannot tell you if the correlation is mediated through that channel or not. You may not be interested in structural equation models, but SEMs are interested in you...

The plethora of models and tests and lack of pre-registered plan or any coherent approach also raises the usual questions about p-values and 'the garden of forking paths'. Does anyone think that if they set out to do a conceptually similar study - grab some price series from Coinmarketcap, traffic data from Twitter and Reddit, # of commits per day on Github - that they would wind up getting remotely similar results? I don't.

We see at least two mechanisms that set cryptocurrencies apart and may result in the observed positive association between supply and returns. First, a short-term increase in supply may incite existing cryptocurrency holders to reinforce their position aggressively, and such display of confidence may, in turn, induce outsiders without prior awareness of cryptocurrencies to participate and buy coins. Second, an increased supply in the short term is likely the result of a spike in mining intensity, which could be interpreted as a signal of the cryptocurrency’s increasing potential to become a widely used medium of exchange.

Miners don't turn on hashpower as a 'signal' or to speculate on Bitcoin (if they wanted to do that, they would simply not sell their incoming coins), they turn it on in response to higher prices, as higher prices mean it becomes profitable to spend more electricity trying to mine blocks.

Our study also departs empirically from prior work in two important dimensions. First, we look at a representative panel of cryptocurrencies, and not just bitcoin [3, 4, 5, 9, 10, 38, 39, 40, 41, 42, 43, 44]. Second, we look at a recent time period, no longer characterized by the massive volatility and price bubbles of the early bitcoin years (i.e., 2009 to 2013).

There is nothing representative about this panel, and it is a bug not a feature to ignore the time period in which Bitcoin saw the majority of its growth!

So to sum up my problems with this analysis, the big ones are that it

  • uses an unrepresentative and redundant set of cryptocurrencies
  • over a short and unrepresentative time period
  • to investigate a model which ignores all feedbacks and interactions between variables and returns, implicitly ignoring all possibility of network effects & bubbles

  • using opaque proprietary 'measurements' of various latent constructs unlikely to measure well what they claim to measure

    • which heavily intercorrelate or literally measure the same data, indicating a single 'publicity' factor which is ignored

  • to make causal claims which are not and cannot be supported by the model and data

  • in support of an interpretation (Bitcoin's returns September 2014 - August 2015 are driven by technological 'innovation potential') which lack any face validity (what technological innovation September 2014 - August 2015?)

Maybe buzz and hype and the media matter a lot less than most people think to Bitcoin's growth. But this paper doesn't affect my beliefs on the matter one bit.