Economy of expectations

Adoption of blockchain technology released an avalanche of new projects, which are mostly not backed by services and products yet. First adopters and investors considered Blockchain as a cherry on a cake of the next big industry disruption. Then protocol layer and value transfer use case matured, their market cap increased, and that led to the second wave of investors backing application layer and financial infrastructure. But while the technological shift has not been noticed, crypto economy relies on investors’ expectations rather than cashflow. Thus we refer to expectation economy as the source of value, not a pursue of consumer attention. The question that we try to answer in this post is what drives that value and how to extrapolate this knowledge to the future expectations.

Each day there are about five cryptoassets (new coins, forks, tokens) that emerge and impose sufficient liquidity: the trading volumes of some of them at the first day may exceed $10 million. Moreover, amongst more than 40 000 cryptoassets that were technically created, less than a thousand is actually traded and supported by development teams. In other words, empirical success ratio of the HODL strategy is less than 4%. Therefore, we hypothesize that if an asset is traded on some crypto-exchange, this ratio increases to about 40%. Here comes the so-called self-selection — the project team must be active enough to contact the exchange and have a website and a whitepaper of high quality to settle on a crypto-exchange. We support our findings with visualizations of cryptoassets performance that illustrate hypothetical yearly return of possession of composite cryptoasset — literally holding each of them for some period, i.e. 30 days. It is important that we intentionously avoid any speculation about how ICO parameters affect ongoing cryptoasset performance.

Thus, the underlying assumption is that most cryptoassets demonstrate a positive price dynamics from the moment of their appearance. At the same time, the quality of the cryptoasset affects its growth pattern insignificantly, as it is already embedded in the hype around the project and affects mainly the trade volumes.

The returns were calculated as if composite asset was bought on the first day and sold on the t-th day, and this relative difference was multiplied by 365/t days in order to be comparable between different days. The composite asset is price index of each cryptoasset that was listed in 2016, 17 of 18 year and had a trade volume at the listing day exceeding $1M. We can see that 2017 was the most successful year, probably because strongly positive market uptrend (not only Bitcoin made 15x). That was the time when the second wave of investors showed up.

As to the number of observations in composite asset, we can see that it were only four assets that formed 2016 year composite — namely Ethereum Classic, Zcash, FirstBlood Coin and E-Dinar Coin — which makes validity of this line rather questionable. 2017 and 2018 on the contrary have sufficient number of observations.

In attempt to ease the assumption of internal upward dynamics of every new cryptoasset we split data in three particular periods, depending on the performance of Bitcoin. Period between 2017–06–01 and 2017–10–04 is chosen as Stagnation, because Bitcoin price did not rise significantly nor was volatile. Comparing to the summer of 2017, lately in that year Bitcoin price experienced unstoppable appreciation, peaking from $4 000 to $20 000 by 17 of December. So as Rise period we took price changes from 2017–10–05 to 2017–12–17. It is notorious that times of prosperity were followed by severe correction when Bitcoin dumped to $6 500 hereafter. We selected period of 2017–12–18–2018–02–28 as Fall stage of the market. The main advantage of the following methodology is that continuous time series are considered, i. e. there are no gaps between three market stages. Besides, stages remain easily distinguishable from each other. We reference Bitcoin price instead of the global market capitalisation because the vast majority of trades occur in pair with Bitcoin, so as exchange to fiat funds.

The following chart illustrates dynamics of composite asset that was formed according to abovementioned market stage rule. It can be seen that investing in altcoins when market is depressed is better at medium term, when in long term all returns converge to some “normal” 1–3x per year.

Another way to increase prescision of project selection rule is to apply categories that were developed in our previous article. So the composite asset is now a group of alike assets. If Nietzsche were a millennial, he would say that cryptocurrency is dead. One possible explanation is that cryptocurrencies are usually distributed by airdrop and tend to lose its value significantly for the first months. However, any other sector shows healthy and suspiciously similar performance.

As opposed to the conventional positive relation between bid-ask spread and average returns (classical paper on the topic with 6500 citations — Amihud, Y. 2002. Illiquidity and stock returns: cross-section and time-series effects. Journal of financial markets, 5(1), 31–56.), cryptocurrency markets offer no premia for illiquidity. As proxi for bid-ask spread (which is unavailable in given data) we utilized trade 24h volumes.

Bitcoin family is not present in log-scaled plot because it has negatige aggregate returns.

Such unexpected results are probably caused by overall upward dynamics of crypto-markets, and the attempt to test that relation in different market stages is presented below. We look at the returns-liquidity relationship after grouping observations by market dynamics.

Without filtering cryptoassets by liquidity, intersectoral composite asset returns looks like the following. It is fascinating that assets which were not in a spotlight while being listed are quite successful as investment tools.

Chart below shows that number of projects in different sectors is diminishing with different velocity. Applications and Finance, as the youngest fields, has the fastest rates of decrease in number, whereas the first use-case of cryptography — transfer of value — has a stable number of projects.

In Finance sector it can be noted that Analytics&Communications, DEXes, Asset Management&Cryptofunds and Tokenized Real assets perform moderately in medium and long run, being volatile after listing just as other Finance projects.

In the long run Scalability solutions and Public blockchains are far from outperforming the market, in contrast to Private ones. Recent Cross-blockchain projects seemed up-and-coming, but after 130 day there is sole DotCoin to be plotted.

Identity&Personal Data projects seem to be least appreciated at all investment horizons, just as External Data solutions. No one said it would be easy to challenge centralized private and government-backed solutions. Another technologies that lie between protocol layer and application layer is DevTech and Distributed Computing, which has appeared to be better investment vehicle, according to the chart.

Since Cryptocurrencies are the oldest type of assets at the market, we consider it at longer time interval yet having enough assets to compose a bundle. It may come as surprise that Bitcoin family was good store of value and not as volatile as Secure coins and Other coins.

Applications show similar returns at horizon of one year. E-commerce and Social projects are leaders, followed by Gaming, Gambling and E-banking. Flat lines for LegalTech and Health illustrate that the first projects in this categories have apparently became inactive. It is also worth to notice that volatility and return tend to decrease in time for Applications sector.

To conclude, we can see that although the market shows fascinating returns, there are some sectors which deserve more thorough study and attention. Of course, each sector and subsector can be proud of one or two lucky project which experienced the returns one can never see at traditional financial markets. Nevertheless, it is always important to understand that the value of the project is driven not only by short-term indicators and market movements but mainly by the usefulness users get from the new project.