Classification of cryptocurrency coins and tokens by the dynamics of their market capitalisations

We empirically verify that the market capitalisations of coins and tokens in the cryptocurrency universe follow power-law distributions with significantly different values, with the tail exponent falling between 0.5 and 0.7 for coins, and between 1.0 and 1.3 for tokens. We provide a rationale for this, based on a simple proportional growth with birth & death model previously employed to describe the size distribution of firms, cities, webpages, etc. We empirically validate the model and its main predictions, in terms of proportional growth (Gibrat's law) of the coins and tokens. Estimating the main parameters of the model, the theoretical predictions for the power-law exponents of coin and token distributions are in remarkable agreement with the empirical estimations, given the simplicity of the model. Our results clearly characterize coins as being "entrenched incumbents" and tokens as an "explosive immature ecosystem", largely due to massive and exuberant Initial Coin Offering activity in the token space. The theory predicts that the exponent for tokens should converge to 1 in the future, reflecting a more reasonable rate of new entrants associated with genuine technological innovations

Your Sentiment Matters: A Machine Learning Approach for Predicting Regime Changes in the Cryptocurrency Market

Research suggests that a significant number of those investing in cryptocurrencies do not follow what we might call rational, profit-maximizing behavior. We also know that with the progressive lowering of entry barriers to online trading platforms, an increasing number of inexperienced investors are investing in cryptocurrencies. Increasingly, the behavior of investors contradicts the predictions made by traditional financial models and challenges the assumptions on which such models have previously relied when anticipating returns on cryptocurrency investments. To overcome this issue we develop a random forest model which we train with features stemming from a sentiment analysis performed on data generated by cryptocurrency enthusiasts using Twitter, Google Trends, and Reddit. Our findings show that such features have an important role to play in capturing the behavior of cryptocurrency investors and increase our model’s ability to anticipate regime changes in the cryptocurrency market. Our model outperforms the predictive ability of the Log-Periodic Power Law model—currently, the model most widely-used to predict regime changes in financial markets. These results imply that scholars and practitioners aiming to understand and predict the development of cryptocurrency markets stand to benefit from analyzing social media data generated by cryptocurrency enthusiasts

Рекурентна ентропія та фінансові кризи

Entropy is one of the most frequently and effectively used measure of the complexity of systems of various nature. And if the Shannon's canonical entropy is more a measure of the randomness of the system, then the approximate, sample, permutation and other new type entropy that have appeared recently, exploiting the Shannon entropy form have allowed us to quantify the complexity of the systems in question using fast and efficient algorithms. For the first time, a new type of recurrence entropy is used to analyze the dynamics of financial time series under crashes conditions. It is shown that recurrent entropy can be used as the indicator-predictor of financial crashes.Ентропія є одним з найбільш часто і ефективно використовуваних показників складності систем різної природи. І якщо канонічна ентропія Шеннона є скоріше мірою мірою випадковості системи, то наближена, вибіркова, перестановки і інша ентропія нового типу, що з'явилася недавно з використанням форми ентропії Шеннона, дозволили нам кількісно оцінити складність систем в Питання з використанням швидких і ефективних алгоритмів. Вперше новий тип рекуррентной ентропії використовується для аналізу динаміки фінансових часових рядів в умовах краху. Показано, що рекуррентную ентропію можна використовувати як індикатор-передвісник фінансових катастроф

An Experimental Study of Cryptocurrency Market Dynamics

As cryptocurrencies gain popularity and credibility, marketplaces for cryptocurrencies are growing in importance. Understanding the dynamics of these markets can help to assess how viable the cryptocurrnency ecosystem is and how design choices affect market behavior. One existential threat to cryptocurrencies is dramatic fluctuations in traders' willingness to buy or sell. Using a novel experimental methodology, we conducted an online experiment to study how susceptible traders in these markets are to peer influence from trading behavior. We created bots that executed over one hundred thousand trades costing less than a penny each in 217 cryptocurrencies over the course of six months. We find that individual "buy" actions led to short-term increases in subsequent buy-side activity hundreds of times the size of our interventions. From a design perspective, we note that the design choices of the exchange we study may have promoted this and other peer influence effects, which highlights the potential social and economic impact of HCI in the design of digital institutions.Comment: CHI 201

Порівняльний аналіз криптовалюти та фондових ринків з використанням теорії випадкової матриці

This article demonstrates the comparative possibility of constructing indicators of critical and crash phenomena in the volatile market of cryptocurrency and developed stock market. Then, combining the empirical cross-correlation matrix with the Random Matrix Theory, we mainly examine the statistical properties of cross-correlation coefficients, the evolution of the distribution of eigenvalues and corresponding eigenvectors in both markets using the daily returns of price time series. The result has indicated that the largest eigenvalue reflects a collective effect of the whole market, and is very sensitive to the crash phenomena. It has been shown that introduced the largest eigenvalue of the matrix of correlations can act like indicators-predictors of falls in both markets.Ця стаття демонструє порівняльну можливість побудови показників критичних та крашних явищ на мінливому ринку криптовалюти та розвиненому фондовому ринку. Потім, поєднуючи емпіричну матрицю перехресної кореляції з теорією випадкової матриці, ми в основному вивчаємо статистичні властивості коефіцієнтів перехресної кореляції, еволюцію розподілу власних значень та відповідних власних векторів на обох ринках, використовуючи щоденні доходи часового ряду цін. Результат показав, що найбільше власне значення відображає колективний ефект усього ринку і є дуже чутливим до явищ краху. Показано, що впроваджене найбільше власне значення матриці кореляцій може діяти як показники-провісники падіння обох ринків

Two Models of Speculative Bubbles Dynamics for Cryptocurrency Prices

The problem of investing into a cryptocurrency market requires good understanding of the processes that regulate the price of the currency. In this paper we offer a view of the cryptocurrency market as an environment for realization of self-organized speculative schemes that result in the formation of a characteristic price bubble. We use a microscale, agent-based model to simulate the system behavior and derive a macroscale ordinary differential equation (ODE) model to estimate the price and the return rates observed in the simulated agent-based model. We provide a formula for the total risk of the system expressed as a sum of two independent components, one being characteristic of the price bubble and the other of the investor behavior