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3,636 results · page 66 of 152
James Yae, George Zhe Tian
No abstract is available for this record.
John E. Marthinsen, Steven R. Gordon
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.
Luis Antonio Loredo Camou
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.
Soon Hyeok Choi, Robert A. Jarrow
Cryptocurrencies provide a natural setting to test for the existence of price bubbles using the local martingale theory of bubbles because cryptocurrencies have no cash flows. Using a robust statistical algorithm, we test for price bubbles in eight cryptocurrencies, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Ripple (XRP), Bitcoin Cash (BCH), EOS (EOS), Monero (XMR), and Zcash (ZEC), from 1 January 2019 to 17 July 2019. The statistical test first estimates the cryptocurrenciesâ volatilities as a function of the price level. Then, these estimates are extrapolated over the positive real line using power functions. Finally, these power functions underly a sequence of hypothesis tests for price bubbles that control for both Type I and Type II errors. Five of the eight currencies (BTC, BCH, EOS, XMR, ZEC) exhibit price bubbles, LTC does not, and the evidence for ETH and XRP is inconclusive. The paper provides strong evidence for the prevalence of bubbles in cryptocurrencies.
Panagiotis Anastasiadis, Stephanos Papadamou
No abstract is available for this record.
Duygu Ider, Stefan Lessmann
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.
Danyang Li, Yukun Shi, Liao Xu, Yahua Xu · 5 authors
No abstract is available for this record.
Jinsha Zhao
Purpose The paper provides new evidence for Bitcoinâs safe-haven property by examining the relationship between currency price, return and Bitcoin trading volume. Design/methodology/approach A unique dataset from a person-to-person (p2p) exchange is used to investigate association between Bitcoin trading volume and currency prices. Currency returns are used to identify local economic crises, the 8 crisis affected currencies are Venezuela Bolivar (VES), Iranian Rial (IRR), Ukrainian Hryvnia (UAH), Argentine Peso (ARS), Egyptian Pound (EGP), Nigerian Naira (NGN), Turkish Lira (TRY) and Kazakhstani Tenge (KZT). Findings The paper demonstrates that local economic crises are positively associated with increased Bitcoin trading. There is a negative association between trading volume and currency value (and return), suggesting low currency price and currency depreciation are accompanied with increased Bitcoin trading. The results not only hold for the crisis affected currencies but also currencies of advanced economies. Granger causality test also reinforces the negative association results. Originality/value The finding indicates some forms of flight-to-safety have occurred during local market crises when capital flight from domestic markets to Bitcoin, strengthening Bitcoinâs hedging asset status. However, total global trading volume declines after the start of the COVID pandemic, suggesting that Bitcoin is still regarded as a speculative asset. Overall, the findings show that Bitcoin is a hedging asset to protect against local currency depreciation, but not a safe-haven asset for the global crisis.
Gianluca Bonifazi, Enrico Corradini, Domenico Ursino, Luca Virgili
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.
Faruk Ăzer, C. Okan Sakar
No abstract is available for this record.
Mario IvĂĄn Contreras-Valdez, JosĂ© Antonio NĂșñez 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.
Florentina Ćoiman, JeanâGuillaume Dumas, Sonia Jimenez-GarcĂšs
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.
Ata Assaf, Avishek Bhandari, Husni Charif, Ender Demir
No abstract is available for this record.
Min-Yuh Day, Yirung Cheng, Paoyu Huang, Yensen Ni
We explore whether investors would receive excess profits by round-turn trading (hereafter referred to as trading) Bitcoin futures based on Bollinger Bands trading strategy (BBTS). Since investors are suggested to first buy (then sell) Bitcoin futures as oversold (overbought) signals emitted by the BBTS (i.e. penetrating lower (upper) Bollinger Bands regarded as a buying (selling) signal), we aim to explore whether investors would have better returns by trading such futures according to the BBTS. Results show that the average holding period return (AHPR) is over 20% for trading Bitcoin futures following the BBTS. Furthermore, after we adjust the 60-day moving average (MA) instead of the 20-day MA for the BBTS, the AHPR is above 50%. It is noted that if the margin could be deemed as an investment amount, its rate of return would be much higher than the 50% for trading Bitcoin futures.
Jules Clément, Sutene Mwambetania Mwambi, Edson Pindza
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 OrnsteinâUhlenbeck driven by a Normal Inverse Gaussian LĂ©vy 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.
David Neto
No abstract is available for this record.
I. Nasirtafreshi
No abstract is available for this record.
Kensuke Ito, Kyohei Shibano, Gento Mogi
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.
vishakha vishakha, Nikhil Sharma, Ila Kaushik, Bharat Bhushan · 5 authors
Time series forecasting is a big fuss or it can be said that itâs a trending topic that has many feasible implementations including stock prices prediction, weather prediction, trade design and capital allotment. Therefore, it contributes to a large number of applications in the controversial domain of blockchain. In the realm of blockchain, forecasting is necessary so as to predict the future prospects. Owing to its immutable and decentralised nature, blockchain facilitates the tracking of malicious activities via anomaly detection. In the recent past, machine learning is spreading its root in every domain and blockchain is not left unmarked by it. It enforces the system to learn from past experience, acknowledge the pattern and thus apply that knowledge in future. In this work, we measure the prophecy or predicting capability of the bitcoin time sequence. This work presents an extensive review of machine learning algorithms along with an implemented study of ARIMA model, LSTM model and XGBoost to predict the fluctuations on a monthly basis. Further, the paper introduces K-means clustering-based anomaly detection scheme that predicts anomalies on the basis of timestamps in Weighted price and Volume of bitcoin. It has been shown that prediction of the anomalies on the dataset yields favourable outcomes and surpasses the outcomes of forecasting in terms of accuracy. Finally, the paper concludes by highlighting several open research challenges in the field of study.
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 DickeyâFuller (GSADF) test using critical values simulated by the bootstrap procedures of Gutierrez (2011), Harvey et al. (2016) and Pedersen and SchĂŒtte (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 SchĂŒtte (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 GarcĂa, Natalia Pariente-Martinez, Mar ArenasâParra
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.
Zhongwen Tong, Zhanbo Chen, Chen Zhu
No abstract is available for this record.
Syed Zishan Ali, Pooja Bhargava, Ranjana Soni, Shivanshi Tiwari
Cryptocurrencies gaining popularity as a digital currency in todayâs world. The market is altering dramatically as a result of the rapid increase in investment in digital currency. The ability to accurately forecast and change the behavior of digital currency is becoming a critical aspect in todayâs digital environment. This prompted us to investigate bitcoin trading and investor adoption in the market. We began by examining and analyzing the costs of currencies, and after reviewing numerous methods, one of them proved to be the ideal technique for our data representation, taking into account various criteria such as date, time, and year. A time series forecasting technique has been implemented for forecast the future profit or loss in currencies.
Ankit Som, Parthajit Kayal
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 âdigital goldâ. 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.