The large-scale application of blockchain technology is an expected to be an inevitable trend. This study revolves around published papers and articles related to blockchain technology, relevance analysis and sorting through the retrieved documents with six core layers of blockchain: Application Layer, Contract Layer, Actuator Layer, Consensus Layer, Network Layer and Data Layer. Based on the analysis results, this study found that China's research is more towards the preference and application of landing and industry and smart cities with blockchain as the underlying technology. International research is more focused on the research of finance as the underlying technology of blockchain and tries to combine crypto assets with real industries, such as crypted assets and payment systems for traditional industries. This paper studies the impact of monetary entropy on cryptocurrencies in smart cities and uses the monetary entropy formula to measure the crypto-economic entropy. We use Kolmogorov entropy to describe the degree of chaos in the cryptocurrency market in a smart city. The study illustrates the current status of blockchain technology and applications from the perspective of cryptocurrency in a smart city. We find that smart cities and cryptocurrencies have a mutually reinforcing effect.
Explaining changes in bitcoin's price and predicting its future have been the foci of many research studies. In contrast, far less attention has been paid to the relationship between bitcoin's mining costs and its price. One popular notion is the cost of bitcoin creation provides a support level below which this cryptocurrency's price should never fall because if it did, mining would become unprofitable and threaten the maintenance of bitcoin's public ledger. Other research has used mining costs to explain or forecast bitcoin's price movements. Competing econometric analyses have debunked this idea, showing that changes in mining costs follow changes in bitcoin's price rather than preceding them, but the reason for this behavior remains unexplained in these analyses. This research aims to employ economic theory to explain why econometric studies have failed to predict bitcoin prices and why mining costs follow movements in bitcoin prices rather than precede them. We do so by explaining the chain of causality connecting a bitcoin's price to its mining costs.
Cryptocurrencies, such as Bitcoin and Ethereum, have recently become a conversation topic among the general population. This paper will explore the information available in Reddit regarding crypto assets. Unlike other social platforms, Reddit allows analyzing the general population sentiment while conveniently organizing information by topic. We study the benefit of sentiment variables derived from Reddit's crypto forums to forecast volatilities and returns. While volatility forecasts seem to benefit from Reddit sentiment variables consistently, results are not statistically different from a benchmark. In contrast, returns present mixed forecasting results but show statistical differences from the proposed benchmark. We also offer evidence that the Reddit variables gain importance in market-wide and asset-specific events.
Anticipating price developments in financial markets is a topic of continued interest in forecasting. Funneled by advancements in deep learning and natural language processing (NLP) together with the availability of vast amounts of textual data in form of news articles, social media postings, etc., an increasing number of studies incorporate text-based predictors in forecasting models. We contribute to this literature by introducing weak learning, a recently proposed NLP approach to address the problem that text data is unlabeled. Without a dependent variable, it is not possible to finetune pretrained NLP models on a custom corpus. We confirm that finetuning using weak labels enhances the predictive value of text-based features and raises forecast accuracy in the context of predicting cryptocurrency returns. More fundamentally, the modeling paradigm we present, weak labeling domain-specific text and finetuning pretrained NLP models, is universally applicable in (financial) forecasting and unlocks new ways to leverage text data.
Abstract Purpose In this paper, we define the concept of user spectrum and adopt it to classify Ethereum users based on their behavior. Design/methodology/approach Given a time period, our approach associates each user with a spectrum showing the trend of some behavioral features obtained from a social network-based representation of Ethereum. Each class of users has its own spectrum, obtained by averaging the spectra of its users. In order to evaluate the similarity between the spectrum of a class and the one of a user, we propose a tailored similarity measure obtained by adapting to this context some general measures provided in the past. Finally, we test our approach on a dataset of Ethereum transactions. Findings We define a social network-based model to represent Ethereum. We also define a spectrum for a user and a class of users (i.e., token contract, exchange, bancor and uniswap), consisting of suitable multivariate time series. Furthermore, we propose an approach to classify new users. The core of this approach is a metric capable of measuring the similarity degree between the spectrum of a user and the one of a class of users. This metric is obtained by adapting the Eros distance (i.e., Extended Frobenius Norm) to this scenario. Originality/value This paper introduces the concept of spectrum of a user and a class of users, which is new for blockchains. Differently from past models, which represented user behavior by means of univariate time series, the user spectrum here proposed exploits multivariate time series. Moreover, this paper shows that the original Eros distance does not return satisfactory results when applied to user and class spectra, and proposes a modified version of it, tailored to the reference scenario, which reaches a very high accuracy. Finally, it adopts spectra and the modified Eros distance to classify Ethereum users based on their past behavior. Currently, no multi-class automatic classification approach tailored to Ethereum exists yet, albeit some single-class ones have been recently proposed. Therefore, the only way to classify users in Ethereum are online services (e.g., Etherscan), where users are classified after a request from them. However, the fraction of users thus classified is low. To address this issue, we present an automatic approach for a multi-class classification of Ethereum users based on their past behavior.
Mario Iván Contreras-Valdez, José Antonio Núñez Mora, Guillermo Benavides Perales
This study presents a multivariate study regarding Bitcoin and its interactions with other financial assets of different classes. This is done by adjusting a multivariate semi heavy-tailed distribution to portfolios containing indexes, currencies, and commodities and one cryptocurrency. Later, a rolling window is deployed to obtain the dynamic parameters of the distribution in a weekly basis. With a Markowitz specification problem, the optimal portfolio weights are computed dynamically using the parameters of the multivariate NIG distribution as inputs. The results provide evidence that correlations of Bitcoin with other assets may provide certain degree of diversification to portfolios; nevertheless, the high volatility of this asset makes it unpractical to employ in significant weights. This paper is relevant for researchers and practitioners as it provides a new tool to manage portfolios with cryptocurrencies and more reliable weights to the asset allocation.
Decentralized Finance (DeFi) is a nascent set of financial services, using tokens, smart contracts, and blockchain technology as financial instruments. We investigate four possible drivers of DeFi returns: exposure to cryptocurrency market, the network effect, the investor's attention, and the valuation ratio. As DeFi tokens are distinct from classical cryptocurrencies, we design a new dedicated market index, denoted DeFiX. First, we show that DeFi tokens returns are driven by the investor's attention on technical terms such as "decentralized finance" or "DeFi", and are exposed to their own network variables and cryptocurrency market. We construct a valuation ratio for the DeFi market by dividing the Total Value Locked (TVL) by the Market Capitalization (MC). Our findings do not support the TVL/MC predictive power assumption. Overall, our empirical study shows that the impact of the cryptocurrency market on DeFi returns is stronger than any other considered driver and provides superior explanatory power.
Since its inception in 2009, Bitcoin has increasingly gained main stream attention from the general population to institutional investors. Several models, from GARCH type to jump-diffusion type, have been developed to dynamically capture the price movement of this highly volatile asset. While fitting the Gaussian and the Generalized Hyperbolic and the Normal Inverse Gaussian (NIG) distributions to log-returns of Bitcoin, NIG distribution appears to provide the best fit. The time-varying Hurst parameter for Bitcoin price reveals periods of randomness and mean-reverting type of behaviour, motivating the study in this paper through fractional Ornstein–Uhlenbeck driven by a Normal Inverse Gaussian Lévy process. Features such as long-range memory are jump diffusion processes that are well captured with this model. The results present a 95% prediction for the price of Bitcoin for some specific dates. This study contributes to the literature of Bitcoin price forecasts that are useful for Bitcoin options traders.
Our study empirically predicts the bubble of non-fungible tokens (NFTs): transferable and unique digital assets on public blockchains. This topic is important because, despite their strong market growth in 2021, NFTs on a project basis have not been investigated in terms of bubble prediction. Specifically, we applied the logarithmic periodic power law (LPPL) model to time-series price data associated with four major NFT projects. The results indicate that, as of December 20, 2021, (i) NFTs, in general, are in a small bubble (a price decline is predicted), (ii) the Decentraland project is in a medium bubble (a price decline is predicted), and (iii) the Ethereum Name Service and ArtBlocks projects are in a small negative bubble (a price increase is predicted). A future work will involve a prediction refinement considering the heterogeneity of NFTs, comparison with other methods, and the use of more enriched data.
Renan Gomes Mendes Diniz, Diogo de Prince, Leandro Maciel
Purpose The aim of this paper is to test the existence of bubbles for the daily prices of cryptocurrencies Bitcoin and Ethereum and verify if there is a relationship between bubbles and volatility regimes. Design/methodology/approach The authors test the presence of bubbles with the generalized supremum augmented Dickey–Fuller (GSADF) test using critical values simulated by the bootstrap procedures of Gutierrez (2011), Harvey et al. (2016) and Pedersen and Schütte (2020). Also, the authors estimate Markov regime switching generalized autoregressive conditional heteroskedasticity model for these cryptocurrencies. Findings The GSADF test result indicates the presence of bubbles for both cryptocurrencies. Simulating critical values by wild-bootstrap, which is robust to non-stationary volatility, leads to the highest number of bubbles in both cryptocurrencies. In addition, based on the estimates of conditional variance models with regime changes, the authors find that the bubbles identified are associated with a regime of low returns volatility, indicating a change in the trade-off between risk and return when the prices of cryptocurrencies differ from their fundamental values. Originality/value To the best of the authors knowledge, there are no studies that test the explosive behavior for cryptocurrencies by the GSADF test using the bootstrap method to simulate critical values from the procedures of Harvey et al. (2016) or Pedersen and Schütte (2020). These bootstrapping procedures are robust to heteroscedasticity and avoid the detection of false bubbles. Further, the advantage of Harvey et al. (2016) procedure is the robustness to non-stationary volatility.
Raquel Quiroga García, Natalia Pariente-Martinez, Mar Arenas‐Parra
This paper analyses the relationship between price clustering and trade volume in the Ether, Ripple and Litecoin cryptocurrencies. We examine at which digits price clustering exists and study the behaviour at different price levels and time frames. By using recent data to provide an updated view of price clustering in the cryptocurrency market, we find a remarkable level of price clustering at round prices: 5.29%, 2.84% and 2.97% for Ether, Ripple and Litecoin for every one minute at open prices, respectively. This paper reaffirms the negotiation hypothesis by finding that price clustering appears at prices at which traded volume is higher.
The last few years have seen a paradigm shift in the financial sector with the development of cryptocurrencies as an alternative mode of payment as well as an investment scheme. The aim of this study is two-fold. The first is to quantify the volatility of cryptocurrencies in terms of the dynamics of tail-end behavior using different approaches and choose the one with the lowest value-at-risk. The second is to investigate the effect of its inclusion in a portfolio with and without gold, to see if Bitcoin is indeed the “digital gold”. This paper uses the generalized simulated annealing optimization technique to compare portfolios for ten countries across the world. The data provide convincing evidence in favor of the inclusion of Bitcoin in the optimized portfolios. Rolling-window analyses (three-year and five-year) confirm the same. However, for some countries, the empirical pattern suggests that instead of replacing gold from the portfolio, both should be comprised. Our results are robust in terms of the inclusion of non-linear constraints.
In this article forecasting of daily closing price series of Bitcoin, Ripple, Dash, Litecoin and Ethereum crypto currencies, using data on prices (open, low, high), market capital and volumes using prior days is focused. The value conduct of cryptographic forms of money remains to a great extent neglected, giving new chances to scientists and business analysts to feature the likenesses and contrasts with standard monetary costs. Hence the paper is focused on this area. he results are compared with various benchmarks. Predictions are done using statistical techniques and machine learning algorithms. A simple linear regression (SLR) model that uses only a single-variable sequence of closing prices for forecasting, and a multiple linear regression (MLR) model that uses a multivariate sequence of prices and quantities at the same time. The simple linear regression (SLR) model for univariate serial forecasting uses only closing prices. Mean Absolute Percentage Error (MAPE) and relative Root Mean Square Error (relative RMSE) performance measures are considered. The accuracy achieved by the ARIMA model on our dataset is the highest, followed by Multivariable Linear Regression and LSTM.
We examine the presence of outliers and time-varying jumps in the returns of four major cryptocurrencies (Bitcoin, Ethereum, Ripple, Dogecoin, Litecoin), and a broad cryptocurrency index (CCI30). The results indicate that only Bitcoin returns are contaminated with outliers. Time-varying jumps are present in Bitcoin, Litecoin, Ripple, and the cryptocurrency index. Notably, the presence of jumps in Bitcoin is significant after correcting for outliers. The main findings point to a price instability in some major cryptocurrencies and thereby the importance of accounting for large shocks and time-varying jumps in modelling volatility in the debatable cryptocurrency markets.
This paper analyses the herding behaviour among exchanges around the expiration of bitcoin futures traded on the Chicago Mercantile Exchange (CME). The database extends from December 2017 to October 2020, taking as a reference the main exchanges that trade bitcoin (Binance, Bitfinex, Bitstamp, Coinbase, itBit, Kraken, and Gemini) and using hourly closing prices and trading volumes in bitcoin and US dollars. Adapting the proposal of Chang, Cheng and Khorana (2000) (CCK) to test conditional herding, we obtain results that indicate that the herding effect is significant during the week before expiration. After expiration, the herding effect lasts for a few hours and disappears. Information overload originating, among other causes, from sophisticated investors' strategies may generate this mimetic behaviour. The results show the relevance of intraday data applied to specific events such as expiration since the unconditional analysis shows, in general, anti-herding behaviour throughout the period of study.
The cryptocurrencies are a hot topic in the global financial system. Cryptocurrency is a digital or virtual or internet currency that uses cryptography for security. Cryptocurrency has created unmatched changes in the financial market having both positive and negative contributions. The concept of cryptocurrency is a little hard to accept, but it is easy to use. It is considered difficult because it is entirely different from our conventional currencies that we people are using since ages. Here, we focus the different types of cryptocurrencies, origin and evolution of the term. The role of cryptography in early cryptocurrencies, Issues currently associated with the term, the role of cryptography in today’s cryptocurrencies, cryptocurrencies exchanges, Cryptocurrencies Trading, advantages and disadvantages of cryptocurrencies trading, How Many cryptocurrencies are there? Market Capitalization of Cryptocurrency, the 2021 Global Crypto Adoption Index Top 20, One Year change in the value of Crypto Assets.
The paper proposes a trading strategy for investing in the cryptocurrency market that uses instant market entries based on additional sources of information in the form of a developed dataset. The task of predicting the moment of entering the market is formulated as the task of classifying the trend in the value of cryptocurrencies. To solve it, ensemble models and deep neural networks were used in the present paper, which made it possible to obtain a forecast with high accuracy. Computer analysis of various investment strategies has shown a significant advantage of the proposed investment model over traditional machine learning methods.
We study the prediction of Value at Risk (VaR) for cryptocurrencies. In contrast to classic assets, returns of cryptocurrencies are often highly volatile and characterized by large fluctuations around single events. Analyzing a comprehensive set of 105 major cryptocurrencies, we show that Generalized Random Forests (GRF) (Athey, Tibshirani & Wager, 2019) adapted to quantile prediction have superior performance over other established methods such as quantile regression, GARCH-type and CAViaR models. This advantage is especially pronounced in unstable times and for classes of highly-volatile cryptocurrencies. Furthermore, we identify important predictors during such times and show their influence on forecasting over time. Moreover, a comprehensive simulation study also indicates that the GRF methodology is at least on par with existing methods in VaR predictions for standard types of financial returns and clearly superior in the cryptocurrency setup.
In this paper, we build an empirical specification that helps to explain bitcoin volatility and to characterize phases of the bitcoin bubble using information derived from investors’ emotions and sentiment that captures investment intentions and investors’ aversion to risk. To this end, we investigated the bilateral relations between bitcoin volatility and investor emotions between 2018 and 2021, a period characterized by significant changes in bitcoin prices as well as wide disparities in investor emotions, especially in the context of the ongoing COVID-19 pandemic. The study was based on a linear and nonlinear Vector Autoregressive (VAR) model that we applied to data related to bitcoin prices and market sentiment as expressed by the Fear and Greed index. Overall, our results evince the key role played by collective emotions in the formation and collapse of the bitcoin bubble. Two findings in particular stand out. First, our model shows significant time-varying lead-lag effects between bitcoin volatility and investor sentiment that come into play bilaterally and help to characterize the dynamics of bitcoin volatility. Second, these interactions exhibit asymmetry and nonlinearity as the sign and size of collective emotions (resp. bitcoin volatility) vary with the regime and market state under consideration (calm state versus period of bubble formation, etc.). In other words, the power of sentiment has a time-varying effect on the market. Indeed, in the first regime (“calm state”), where bitcoin volatility is relatively low and the market shows evidence of stability, collective emotions have a negative impact on bitcoin volatility, prompting a stabilizing strength. However, in the second regime (“bubble formation”), the effect of emotions turns significantly positive as investors gradually become less fearful and more reassured, which can simultaneously increase volatility and destabilize the market. Finally, in the third regime (“bubble collapse”), when bitcoin reaches a high level of value and experiences impressive volatility excess , the effect of emotions again turns negative, resulting in further switching behavior that pushes investor action to provoke a bitcoin price correction, moving it toward a new state of stability. Our conclusion helps improve predictions of bitcoin price dynamics informed by the information provided by investor emotions.
Blockchain technology has been around for more than ten years, nevertheless, the knowledge about its economic and business implications is still fragmented and heterogeneous. The present article intends to tackle this issue with a twofold contribution. The first is an analysis of the shift from economics to tokenomics highlighting the central role played by tokens within blockchain-based ecosystems. The second is a framework for tokens design leveraging a morphological analysis deeply grounded in the literature. As blockchain becomes a mainstream phenomenon, the value of the work proposed lies in lowering the cognitive barriers and in clarifying the space of available options for private and public actors willing to leverage tokenization in their daily operations.
The current study has examined the informational efficiency of market leader of cryptocurrency i.e, Bitcoin. The daily, weekly and monthly prices of Bitcoin have been used for analysis from 2013 to 2017. The information efficiency has been investigated by using different tests of random walk both parametric and non-parametric. The results indicate the Bitcoin returns are not weak form efficient and the element of random walk is not there. Hence, the investors have an opportunity to beat the market by using technical trading and get abnormal returns from the predictability of Bitcoin prices.