Abstract We investigate the significance of extreme positive returns in the cross-sectional pricing of cryptocurrencies. Through portfolio-level analyses and weekly cross-sectional regressions on all cryptocurrencies in our sample period, we provide evidence for a positive and statistically significant relationship between the maximum daily return within the previous month (MAX) and the expected returns on cryptocurrencies. In particular, the univariate portfolio analysis shows that weekly average raw and risk-adjusted return differences between portfolios of cryptocurrencies with the highest and lowest MAX deciles are 3.03% and 1.99%, respectively. The results are robust with respect to the differences in size, price, momentum, short-term reversal, liquidity, volatility, skewness, and investor sentiment.
Managing extreme price fluctuations in cryptocurrency markets are of central importance for investors in this market segment. Using a sample of highly liquid cryptocurrencies from January 2017 to June 2021, this paper proposes a dynamic investment strategy that selects cryptocurrencies based on their historical volatility and is complemented by a simple stop-loss rule. Our results reveal that investing in highly concentrated low volatility cryptocurrency portfolios with six to twelve months volatility look-back and holding period generate statistically significant excess returns. By including a simple stop-loss rule, the downside risk of cryptocurrency portfolios is reduced markedly, and the Sharpe ratios are improved significantly.
This study examines the relationship between investor attention and herding effects in the cryptocurrency market by employing the vector autoregression and quantile regression models. Furthermore, we examine whether the COVID-19 pandemic affected herding behaviour in cryptocurrencies. Using the daily closing price and Google search volume of the five leading cryptocurrencies, the paper finds that herding in the cryptocurrency market decreases with an increase in investor attention for the overall sample. The results for the COVID-19 period indicate that the impact of investor attention on the herding effect decreases due to increased attention to the pandemic. This study is one of the initial attempts to examine the impact of investor attention on herding in cryptocurrencies.
Abstract The increasing attention on Bitcoin since 2013 prompts the issue of possible evidence for a causal relationship between the Bitcoin market and internet attention. Taking the Google search volume index as the measure of internet attention, time-varying Granger causality between the global Bitcoin market and internet attention is examined. Empirical results show a strong Granger causal relationship between internet attention and trading volume. Moreover, they indicate, beginning in early 2018, an even stronger impact of trading volume on internet attention, which is consistent with the rapid increase in Bitcoin users following the 2017 Bitcoin bubble. Although Bitcoin returns are found to strongly affect internet attention, internet attention only occasionally affects Bitcoin returns. Further investigation reveals that interactions between internet attention and returns can be amplified by extreme changes in prices, and internet attention is more likely to lead to returns during Bitcoin bubbles. These empirical findings shed light on cryptocurrency investor attention theory and imply trading strategy in Bitcoin markets.
Introduction of the Exchange Traded Funds (ETFs) in 1989 in the USA added another category to the bunch of asset classes that continues to gain increasing popularity not only with the Asset Management Companies but also the potential investors who had been looking eagerly for innovative ways of diversifying their investment portfolio. Among a host of theme-based ETFs which offer varied investment strategies and returns, Bitcoin ETFs are of relatively recent origin and have caught investorsâ attention as a substitute for investment in cryptocurrency directly or through derivatives. Bitcoin ETFs have appeared on the horizon designing products backed by Bitcoin and Bitcoin backed structured products like Bitcoin futures etc. At the same time, there is a growing class of unregulated Bitcoin ETF, Trusts and other financial products that track the value of Bitcoin and trade on traditional market exchanges rather than cryptocurrency exchanges. Also, there is growing another asset class of Blockchain ETFs, operating many a time as a proxy for Bitcoin/Bitcoin ETFs that invest in companies having direct or indirect exposure to Blockchain. The instant paper attempts to examine the fundamentals of Bitcoin and Blockchain ETFs, Blockchain technology, the factors throwing up Bitcoin ETFs in to prominence and the issues relating to their legal recognition by the regulators. It also attempts to provide a governance model for effective regulation of the two without mutual overlapping either in investorsâ comprehension or in practice.
We present a systematic approach to detect fake transactions on cryptocurrency exchanges by exploiting robust statistical and behavioral regularities associated with authentic trading. Our sample consists of 29 centralized exchanges, among which the regulated ones feature transaction patterns consistently observed in financial markets and nature. In contrast, unregulated exchanges display abnormal first-significant-digit distributions, size rounding, and transaction tail distributions, indicating widespread manipulation unlikely driven by specific trading strategy or exchange heterogeneity. We then quantify the wash trading on each unregulated exchange, which averaged over 70% of the reported volume. We further document how these fabricated volumes (trillions of dollars annually) improve exchange ranking, temporarily distort prices, and relate to exchange characteristics (e.g., age and user base), market conditions, and regulation. Overall, our study cautions against potential market manipulations on centralized crypto exchanges with concentrated power and limited disclosure requirements, and highlights the importance of FinTech regulation.
Pairs trading that is built on âRelative-Value Arbitrage Ruleâ is a popular short-term speculation strategy enabling traders to make profits from temporary mispricing of close substitutes. This paper aims at investigating the profit potentials of pairs trading in a new finance area â on cryptocurrencies market. The empirical design builds upon four well-known approaches to implement pairs trading, namely: correlation analysis, distance approach, stochastic return differential approach, and cointegration analysis, that use monthly closing prices of leading cryptocoins over the period January 1, 2018, â December 31, 2019. Additionally, the paper executes a simulation exercise that compares long-short strategy with long-only portfolio strategy in terms of payoffs and risks. The study finds an inverse relationship between the correlation coefficient and distance between different pairs of cryptocurrencies, which is a prerequisite to determine the potentially market-neutral profits through pairs trading. In addition, pairs trading simulations produce quite substantive evidence on the continuing profitability of pairs trading. In other words, long-short portfolio strategies, producing positive cumulative returns in most subsample periods, consistently outperform conservative long-only portfolio strategies in the cryptocurrency market. The profitability of pairs trading thus adds empirical challenge to the market efficiency of the cryptocurrency market. However, other aspects like spectral correlations and implied volatility might also be significant in determining the profit potentials of pairs trading.
We examine the shortâterm and longâterm effects of hacking events on bitcoin return. Additionally, we attempt to find out if investors can benefit from these events by adopting and modifying the models proposed by Baur et al. (2018) [ Journal of International Financial Markets, Institutions & Money, 54 , 177â189] who compare the performance of bitcoin against FX returns and the S&P500. The results show that hacking events present an opportunity for investors to make a profit if they invest in bitcoin, stocks and currencies. Such an opportunity, however, does not last for long.
Liquid markets are driven by information asymmetries and the injection of new information in trades into market prices. Where market matching uses an electronic limit order book (LOB), limit orders traders may make suboptimal price and trade decisions based on new but incomplete information arriving with market orders. This paper measures the information asymmetries in Bitcoin trading limit order books on the Kraken platform, and compares these to prior studies on equities LOB markets. In limit order book markets, traders have the option of waiting to supply liquidity through limit orders, or immediately demanding liquidity through market orders or aggressively priced limit orders. In my multivariate analysis, I control for volatility, trading volume, trading intensity and order imbalance to isolate the effect of trade informativeness on book liquidity. The current research offers the first empirical study of Glosten (1994) to yield a positive, and credibly large transaction cost parameter. Trade and LOB datasets in this study were several orders of magnitude larger than any of the prior studies. Given the poor small sample properties of GMM, it is likely that this substantial increase in size of datasets is essential for validating the model. The research strongly supports Glosten's seminal theoretical model of limit order book markets, showing that these are valid models of Bitcoin markets. This research empirically tested and confirmed trade informativeness as a prime driver of market liquidity in the Bitcoin market.
This work presents an application of self-attention networks for cryptocurrency trading. Cryptocurrencies are extremely volatile and unpredictable. Thus, cryptocurrency trading is challenging and involves higher risks than trading traditional financial assets such as stocks. To overcome the aforementioned problems, we propose a deep reinforcement learning (DRL) approach for cryptocurrency trading. The proposed trading system contains a self-attention network trained using an actor-critic DRL algorithm. Cryptocurrency markets contain hundreds of assets, allowing greater investment diversification, which can be accomplished if all the assets are analyzed against one another. Self-attention networks are suitable for dealing with the problem because the attention mechanism can process long sequences of data and focus on the most relevant parts of the inputs. Transaction fees are also considered in formulating the studied problem. Systems that perform trades in high frequencies cannot overlook this issue, since, after many trades, small fees can add up to significant expenses. To validate the proposed approach, a DRL environment is built using data from an important cryptocurrency market. We test our method against a state-of-the-art baseline in two different experiments. The experimental results show the proposed approach can obtain higher daily profits and has several advantages over existing methods.
Abstract We study the problem of the intraday short-term volume forecasting in cryptocurrency multi-markets. The predictions are built by using transaction and order book data from different markets where the exchange takes place. Methodologically, we propose a temporal mixture ensemble, capable of adaptively exploiting, for the forecasting, different sources of data and providing a volume point estimate, as well as its uncertainty. We provide evidence of the clear outperformance of our model with respect to econometric models. Moreover our model performs slightly better than Gradient Boosting Machine while having a much clearer interpretability of the results. Finally, we show that the above results are robust also when restricting the prediction analysis to each volume quartile.
This study examines the impact of investor sentiment on cryptocurrency returns. We use a direct survey-based measure that captures the investorsâ sentiment on Bitcoins. This direct measure of Bitcoin investor sentiment is obtained from the Sentix database. The results of the study found that the Bitcoin prices experience appreciation when investors are optimistic about Bitcoin. Bitcoin sentiment has significant power in predicting the Bitcoin prices after controlling for the relevant factors. There is also evidence that the sentiment of the dominant cryptocurrency, i.e., Bitcoin, influences the price of other cryptocurrencies. Further, we extend our analysis by investigating the impact of equity market sentiment on cryptocurrency returns. We proxy equity market sentiment using two measures viz: Baker-Wurgler sentiment Index and the VIX Index. When the equity market investorsâ sentiment is bearish, cryptocurrency prices rise, indicating that cryptocurrency can act as an alternative avenue for investment. Our results remain unaffected after controlling for potential factors that could impact cryptocurrency prices.
Abstract Cryptocurrencies are becoming an exciting topic for legislative bodies, practitioners, media, and scholars with diverse academic backgrounds. The work identifies diversification benefits when cryptocurrencies are combined with the equity instruments from Visegrad Stock Exchanges. Furthermore, the results of the study explore financial and economic benefits for the investors of combining cryptocurrencies with equity stocks on the mixed portfolio. Three different independent experiments were conducted to observe diversification benefits generated from cryptocurrencies. Results from the two experiments show that cryptocurrencies employ higher portfolio risk and generate higher returns when they are involved with equity stocks portfolios. The first experiment indicates that cryptocurrencies reduce the risk level of the equity portfolios while increase average returns. Providing the equity portfolios with additional equity stocks lower the portfolio risk which is in line with the theoretical paradigms. Results indicate that cryptocurrencies must be seriously considered by the portfolio managers as an essential aspect of the portfolio diversification benefits. Future studies might raise the samples of selected portfolios with stocks from different stock indexes, to identify the problem from a broader perspective.
We apply the Markowitz mean-variance framework to assess risk-return benefits of cryptocurrency-portfolios. Using daily data of the three major cryptocurrencies for the time span 1/1/2019 to 27/04/2021, we relate risk and return of different mean-variance portfolio strategies to Bitcoin, Etherium, Ripple and BIST 30 benchmark. We find that combining cryptocurrencies crowds out BIST 30 index to maximize return and Sharpe ratio while cryptocurrencies are crowded out if the optimization problem is changed to a risk minimization problem rather than a return maximization problem. Furthermore, according to rolling-window approach shift from Bitcoin to Etherium is important.