This article employs the DCC-GARCH-based approach proposed by Gabauer (2020) to measure the connectedness of the cryptocurrency market, and whether the connectedness indexes became significantly different after China issued the regulatory ban in May 2021. The empirical finding suggests that the cryptocurrency market became significantly more interconnected after the ban was issued. Furthermore, we find that the net connectedness indexes of the majority of cryptocurrencies in the sample changed significantly after the ban. Among all the studied cryptocurrencies, Bitcoin is the largest net volatility receiver and Solana is the largest net volatility transmitter.
We employ and analyze various machine learning models for daily cryptocurrency market prediction and trading. We train the models to predict binary relative daily market movements of the 100 largest cryptocurrencies. Our results show that all employed models make statistically viable predictions, whereby the average accuracy values calculated on all cryptocurrencies range from 52.9% to 54.1%. These accuracy values increase to a range from 57.5% to 59.5% when calculated on the subset of predictions with the 10% highest model confidences per class and day. We find that a long-short portfolio strategy based on the predictions of the employed LSTM and GRU ensemble models yields an annualized out-of-sample Sharpe ratio after transaction costs of 3.23 and 3.12, respectively. In comparison, the buy-and-hold benchmark market portfolio strategy only yields a Sharpe ratio of 1.33. These results indicate a challenge to weak form cryptocurrency market efficiency, albeit the influence of certain limits to arbitrage cannot be entirely ruled out.
In this study, we apply an interactive agentâbased model to investigate fundamentalists and positive-feedback traders behaviours in cryptocurrency markets. Our results suggested that fundamentalists pushed up cryptocurrency prices in some past periods. In addition, the cryptocurrency markets are unstable and agitated.
Yuanyuan Zhang, Stephen Chan, Jeffrey Chu, Shouâhsing Shih
Decentralized finance, or âDe-Fiâ, is an emerging sector and movement in finance and the cryptocurrency space that aims to extend the idea of digital currencies to a global decentralized financial system. In many cases, customized âcoinsâ or âtokensâ are used for applications such as borrowing or lending, providing liquidity, and even voting. Built on the same foundations of traditional cryptocurrencies (e.g. Bitcoin), these tokens possess monetary value and can be traded using fiat currencies on specialized decentralized exchanges. We provide the first analysis investigating the market efficiency of the decentralized finance market through DeFi tokens. Our findings from applying the adaptive market hypothesis (AMH) revealed that the efficiency of the markets varies over time, with the majority of the DeFi token returns exhibit very short days of inefficiency and predictability in their price every year. This is consistent with the AMH, but perhaps unexpected when considering the link between emerging financial markets and market efficiency. We conclude that the majority of investors and practitioners purchase these DeFi tokens for their utility value rather than for investment purposes, hence making the DeFi market more efficient. Further robustness checks on other comparable products in the blockchain ecosystem such as NFTs also reveal similar results.
The goal of cryptocurrencies is decentralization. In principle, all currencies have equal status. Unlike traditional stock markets, there is no default currency of denomination (fiat), thus the trading pairs can be set freely. However, it is impractical to set up a trading market between every two currencies. In order to control management costs and ensure sufficient liquidity, we must give priority to covering those large-volume trading pairs and ensure that all coins are reachable. We note that this is an optimization problem. Its particularity lies in: 1) the trading volume between most (>99.5%) possible trading pairs cannot be directly observed. 2) It satisfies the connectivity constraint, that is, all currencies are guaranteed to be tradable. To solve this problem, we use a two-stage process: 1) Fill in missing values based on a regularized, truncated eigenvalue decomposition, where the regularization term is used to control what extent missing values should be limited to zero. 2) Search for the optimal trading pairs, based on a branch and bound process, with heuristic search and pruning strategies. The experimental results show that: 1) If the number of denominated coins is not limited, we will get a more decentralized trading pair settings, which advocates the establishment of trading pairs directly between large currency pairs. 2) There is a certain room for optimization in all exchanges. The setting of inappropriate trading pairs is mainly caused by subjectively setting small coins to quote, or failing to track emerging big coins in time. 3) Too few trading pairs will lead to low coverage; too many trading pairs will need to be adjusted with markets frequently. Exchanges should consider striking an appropriate balance between them.
Andreas Nugroho Sihananto, Anggraini Puspita Sari, Muhammad Eko Prasetyo, Mochammad Yanuar Fitroni · 6 authors
Reinforcement learning as machine learning algorithms can construct software agents, and machines work automatically to determine the superior manners to maximize the algorithm. On the other hand, in recent years, cryptocurrencies are increasingly known because numerous people have used them for investment and trading. People have created automated cryptocurrency trading systems to save time in trading activities. Therefore, researchers are interested in inventing a computerized trading system by implementing a reinforcement learning algorithm. This implementation uses a stable baseline and OpenAi gym with three methods of RNN, such as A2C, ACER, and PPO. The result is that A2C is the best method for low-volume trade like BTC/USDT, while for higher-volume trade in ETH/USDT, the ACER method proved more beneficial. The best BTC/USDT trading method is A2C, with a reward in the testing phase of 0.332. Meanwhile, for ETH/USDT, the best approach is ACER, with the testing phaseâs reward is 0.257.
This paper analyses the effects known as the day of the week and the month of the year in the cryptocurrency markets. The closing values of eleven cryptocurrencies have been considered. The study employs dummy variable regression techniques, ANOVA and Friedman tests for assessing two calendar anomalies, the day-of-week and month-of-year effects. To test these calendar effects, we have applied both full sample and rolling-regression techniques for two lengths of the rolling sample intervals. Furthermore, we have examined the existence of long memory in day-of-the- week and month-of-the-year cryptocurrency returns. The results provide evidence about the existence of day-of-the-week and month-of-the-year effects in cryptocurrency returns, in particular, on Thursdays and in November. In addition, it should be added that the general results of the current study show that the calendar effect in the cryptocurrency market is dynamic rather than static, which indicates that the calendar effect is a phenomenon that varies over time.
Rongxin Chen, Gabriele M. Lepori, Chung-Ching Tai, MingâChien Sung
Research on human attention indicates that objects that stand out from their surroundings, i.e., salient objects, attract the attention of our sensory channels and receive undue weighting in the decision-making process. In the financial realm, salience theory predicts that individuals will find assets with salient upsides (downsides) appealing (unappealing). We investigate whether this theory can explain investor behaviour in the cryptocurrency market. Consistent with the theory's predictions, using a sample of 1738 cryptocurrencies, we find that cryptocurrencies that are more (less) attractive to âsalient thinkersâ earn lower (higher) future returns, which indicates that they tend to be overpriced (underpriced). On average, a one cross-sectional standard-deviation increase in the salience theory value of a cryptocurrency reduces its next-week return by 0.41%. However, the salience effect is confined to the micro-cap segment of the market, and its size is moderated by limits to arbitrage.
Abstract This paper explores the use of machine learning algorithms and narrative sentiments when applied to the task of forecasting and trading Bitcoin. The forecasting framework starts from the selection among 295 individual prediction models. Three machine learning approaches, namely, neural networks, support vector machines, and gradient boosting approach, are used to further improve the forecasting performance of individual models. By taking dataâsnooping bias into account, three different metrics are applied to examine the forecasting ability of each model. Our results suggest that the machine learning techniques always outperform the best individual model whereas the gradient boosting framework has the best performance among all the models. Finally, a timeâvarying leverage trading strategy combined with narrative sentiments and volatility is proposed to enhance trading performance. This suggests that the hybrid leverage strategy provides the highest Bitcoin profits consistently among all trading exercises.
This paper examines the forecasting power of daily infectious disease-related uncertainty in predicting the realized volatility of nine foreign exchange futures and the Bitcoin futures series using the heterogeneous autoregressive realized variance model. Our results indicate that the infectious diseases-related uncertainty index plays a crucial role in predicting the future path of foreign exchange and Bitcoin futures realized volatility in all the selected time intervals. These findings have important implications for portfolio managers and investors during periods of high levels of uncertainty associated with infectious diseases.
At the beginning of 2020, the panic of Covid-19 had an excessive impact on global economics and the financial market. Based on the unit root test, this paper exposes the newly global Covid-19 confirmed cases and the rate of return of Ethereum and Bitcoin are stationary time series. This paper further completes the VAR model and ARMA-GARCH model. The VAR model examines the effect of newly confirmed cases on to rate of return of Bitcoin and Ethereum, and the ARMA-GARCH model scrutinizes the newly confirmed cases to the fluctuation of Bitcoin and Ethereum. This study found that the impact of the COVID-19 on cryptocurrency earnings was short-term, and did not improve the market volatility.
Non-fungible tokens (NFT) have recently emerged as a novel blockchain-hosted financial asset class that has attracted major transaction volumes. However, preprocessing and analysis of NFT transaction data, which investors often rely on for their investment decisions, pose several challenges not commonly encountered in traditional financial data. These challenges arise mainly due to the non-fungible nature of NFTs as well as the intrinsic characteristics of the blockchain, the primary data source for NFT transactions. Using data consisting of the transaction history of eight highly valued NFT collections, a selection of such challenges is illustrated. These include price differentiation by token traits, the possible existence of lateral swaps and wash trades in the transaction history, and finally, severe price volatility. This paper provides an overall summary of the challenges associated with data analytics on NFT transaction data and lay a foundation for future research on the topic.
The enormous rise of the cryptocurrencies over the last few years has created one of the largest unregulated markets in the world. In this study, we obtain millisecond data for the five major cryptocurrenciesâbitcoin, ethereum, ripple, litecoin and dashâand two cryptocurrency indicesâCrypto Index (CRIX) and CCI30 Crypto Currencies Indexâto investigate the relationship between cryptocurrency liquidity, herding behaviour and profitability during periods of extreme price movements (EPMs). We demonstrate that cryptocurrency traders (CTs) facilitate EPMs and demand liquidity even during the utmost EPMs. We observe the presence of herding behaviour during up markets across the entire dataset. Our robustness checks indicate that herding behaviour follows a dynamic pattern that varies over time with decreasing magnitude. We also provide novel evidence of CTsâ profitability after transaction costs, and demonstrate their strong profitability-generating record in the future.
Rarity is known to be a factor in the price of non-fungible tokens (NFTs). Most investors make their purchasing decisions based on the rarity score or rarity rank of NFTs. However, not all rare NFTs are associated with a higher price, especially for play-to-earn gaming NFTs. In this paper, we studied the top-ranked play-to-earn gaming NFTs on Axie Infinity. We found that, in addition to rarity, utility is also a significant factor influencing the price. Furthermore, we use utility as a predictor to predict the price of Axies using the XGBoost regressor. Our results reveal that, compared to using rarity-based predictors only, leveraging utility-based predictors can improve the prediction accuracy, thus highlighting utility as a price determinant for play-to-earn gaming NFTs.
Cryptocurrency markets have attracted many interest for global investors because of their novelty, wide on-line availability, increasing capitalization, and potential profits. In the econophysics tradition, we show that many of the most available cryptocurrencies have return statistics that do not follow Gaussian distributions, instead following heavy-tailed distributions. Entropy measures are applied, showing that portfolio diversification is a reasonable practice for decreasing return uncertainty.
The cryptocurrency market has received much interest in the media and academia because of its large price fluctuations since the beginning of 2013. By looking into the impact of behavioral finance elements on investing decisions in the cryptocurrency market, investors who invest in the cryptocurrency market will be able to make better decisions. Based on studies on its principal currency, the 18th of December 2017 has been designated as the peak date of the cryptocurrency market's greatest bubble. A bibliometric approach by means of quantitative analytical methods was applied to discover the relationship between the keywords associated with cryptocurrency and behavioral finance. Articles were extracted from the Scopus database that was published between 2018 and 2021. Publication Year, nation, area of research, journal, authors, and organizational affiliations were all examined in the extracted records. The VOSviewer application was used to visualise relation between both the research themes. Analysis of 102 review and original articles exposed that the total number of publications has incessantly increased over the last 4 years. This study examines the countries that contribute more publications in the selected field of research. The current study uses bibliometric approaches to evaluate cryptocurrency research and highlighted current trends in the interaction between cryptocurrencies and behavioural finance using several metrics, as well as prospective future research hot spots in this sector.
Purpose This paper aims to examine the impact of investor attention due to the COVID-19 pandemic, Twitter-based sentiment towards uncertainty and public sentiment on the performance of cryptocurrencies. Design/methodology/approach The authors employ the simple linear regression, quantile regression (QR), the exponential generalised autoregressive conditional heteroskedasticity (EGARCH) model, and sentiment analysis to examine this phenomenon. The authors utilise the daily closing price of the 20 leading cryptocurrencies, the Google search volume index of the âCoronavirusâ keyword, the Twitter-based economic uncertainty index, and textual data collected from the Reddit social media platform. Findings The results show that investor attention and Twitter uncertainty have a negative (positive) effect on cryptocurrency returns (volatility). The QR results indicate a heterogeneous effect of investor attention and Twitter economic uncertainty on cryptocurrency returns with a higher effect in the lower quantiles. The findings indicate that cryptocurrencies fail to act as a safe haven during this pandemic. Originality/value The study is amongst the very few studies that capture the impact of investor attention/sentiment due to COVID-19 on the performance of cryptocurrencies.