We examine price discovery and liquidity provision in the secondary market for bitcoin—an asset with a high level of speculative trading. Based on BTC-e’s full limit order book over the 2013–2014 period, we find that order informativeness increases with order aggressiveness within the first 10 tiers, but that this pattern reverses in outer tiers. In a high volatility environment, aggressive orders seem to be more attractive to informed agents, but market liquidity migrates outward in response to the information asymmetry. We also find support to the Markovian learning assumption often made in theoretical models of limit order markets.
Following the popularity of Bitcoin trading in recent years, Bitcoin futures were introduced in December 2017 as an effort to provide institutional and retail investors with additional trading tools for Bitcoin. This study analyses the Bitcoin futures mid-quote data from CBOE, and Bitcoin market index applying VAR and VECM process methodologies, Hasbrouck’s information share and the Gonzalo-Granger component share measurement to examine price discovery in Bitcoin markets. Furthermore, the chapter seeks to assess the Bitcoin market microstructure. The results drawn on the intra-day prices show that the futures are leading the price discovery at different frequencies even with comparably low futures trading volumes. This supports the extant literature of futures-spot market price discovery and the role of informed traders in the futures market.
The purpose of this study is to develop robust estimation of association between two types of crypto-currencies namely Bitcoin and Ethereum. Daily data of crypto-currencies are collected from https://coinmarketcap.com. The period for data analysis is started from January 2017 until October 2018. The value of mean return for Bitcoin is 13.18 %. Meanwhile, the value of mean return for Ethereum is 27.85 %. The standard deviation for Bitcoin is 30.27 % and Ethereum is 64.24 %. Then, this study performed Person product moment coefficient analysis to evaluate the correlation between these two crypto-currencies. Result indicates the association coefficient value is 0.50. The correlation shows there is strong positive correlation between Bitcoin return and Ethereum return. As conclusion, there is significant relationship between Bitcoin and Ethereum return data with strong positive correlation (r = 0.503, n = 21, p =0.020).The significant of this study is to help investors to make better decision in selecting appropriate investment portfolio for their investment fund that contributes better return and lower risk.
We develop a strong diagnostic for bubbles and crashes in bitcoin, by analyzing the coincidence (and its absence) of fundamental and technical indicators. Using a generalized Metcalfe's law based on network properties, a fundamental value is quantified and shown to be heavily exceeded, on at least four occasions, by bubbles that grow and burst. In these bubbles, we detect a universal super-exponential unsustainable growth. We model this universal pattern with the Log-Periodic Power Law Singularity (LPPLS) model, which parsimoniously captures diverse positive feedback phenomena, such as herding and imitation. The LPPLS model is shown to provide an ex-ante warning of market instabilities, quantifying a high crash hazard and probabilistic bracket of the crash time consistent with the actual corrections; although, as always, the precise time and trigger (which straw breaks the camel's back) being exogenous and unpredictable. Looking forward, our analysis identifies a substantial but not unprecedented overvaluation in the price of bitcoin, suggesting many months of volatile sideways bitcoin prices ahead (from the time of writing, March 2018).
Abstract This study investigates the profitability of an algorithmic trading strategy based on training SVM model to identify cryptocurrencies with high or low predicted returns. A tail set is defined to be a group of coins whose volatility-adjusted returns are in the highest or the lowest quintile. Each cryptocurrency is represented by a set of six technical features. SVM is trained on historical tail sets and tested on the current data. The classifier is chosen to be a nonlinear support vector machine. The portfolio is formed by ranking coins using the SVM output. The highest ranked coins are used for long positions to be included in the portfolio for one reallocation period. The following metrics were used to estimate the portfolio profitability: %ARC (the annualized rate of change), %ASD (the annualized standard deviation of daily returns), MDD (the maximum drawdown coefficient), IR1, IR2 (the information ratio coefficients). The performance of the SVM portfolio is compared to the performance of the four benchmark strategies based on the values of the information ratio coefficient IR1, which quantifies the risk-weighted gain. The question of how sensitive the portfolio performance is to the parameters set in the SVM model is also addressed in this study.
Predicting currency prices remains a difficult endeavour. Investors are continually seeking new ways to extract \nmeaningful information about the future direction of price changes. Recently, cryptocurrencies have attracted \nhuge attention due to their unique way of transferring value as well as its value as a hedge. A method proposed \nin this project involves using data mining techniques: mining text documents such as news articles and tweets \ntry to infer the relationship between information contained in such items and cryptocurrency price direction. \nThe Long Short-Term Memory Recurrent Neural Network (LSTM RNN) assists in creating a hybrid model \nwhich comprises of sentiment analysis techniques, as well as a predictive machine learning model. The success \nof the model was evaluated within the context of predicting the direction of Bitcoin price changes. Findings \nreported here reveal that our system yields more accurate and real-time predictions of Bitcoin price fluctuations \nwhen compared to other existing models in the market.
We investigate the cross‐correlations of return‐volume relationship of the Bitcoin market. In particular, we select eight exchange rates whose trading volume accounts for more than 98% market shares to synthesize Bitcoin indexes. The empirical results based on multifractal detrended cross‐correlation analysis (MF‐DCCA) reveal that (1) the nonlinear dependencies and power‐law cross‐correlations in return‐volume relationship are found; (2) all cross‐correlations are multifractal, and there are antipersistent behaviors of cross‐correlation for q = 2; (3) the price of small fluctuations is more persistent than that of the volume, while the volume of larger fluctuations is more antipersistent; and (4) the rolling window method shows that the cross‐correlations of return‐volume are antipersistent in the entire sample period.
Zhenghui Li, Hao Dong, Zhehao Huang, Pierre Failler
The rapid development of VFAs allows investors to diversify their choices of investment products. In this paper, we measure the return risk of VFAs based on GARCH-type model. By establishing a Markov regime-switching Regression (MSR) Model, we explore the asymmetric effects of speculation, investor attention, and market interoperability on return risks in different risk regimes of VFAs. The results show that the influences of speculation and investor attention on the risks of VFAs are significantly positive at all regimes, while market interoperability only admits a positive impact on risk under high risk regime. All of the three factors exert asymmetric effects on risks in different regimes. Further study presents that the risk regime-switching also shows asymmetric characteristic but the medium risk regime is more stable than any others. Therefore, transactions of investors and arbitrageurs are monitored by certain policies, such as limiting the number of transactions or restricting the trading amount at high risk regime. However, when return risk is low, it will return to a medium level if we encourage investors to access.
Guglielmo Maria Caporale, Alex Plastun, Viktor Oliinyk
This paper investigates the role of the frequency of price overreactions in the cryptocurrency market in the case of BitCoin over the period 2013–2018. Specifically, it uses a static approach to detect overreactions and then carries out hypothesis testing by means of a variety of statistical methods (both parametric and non-parametric) including ADF tests, Granger causality tests, correlation analysis, regression analysis with dummy variables, ARIMA and ARMAX models, neural net models, and VAR models. Specifically, the hypotheses tested are whether or not the frequency of overreactions (i) is informative about Bitcoin price movements (H1) and (ii) exhibits no seasonality (H2). On the whole, the results suggest that it can provide useful information to predict price dynamics in the cryptocurrency market and for designing trading strategies (H1 cannot be rejected), whilst there is no evidence of seasonality (H2 cannot be rejected).
The authors discuss several uses of blockchain and, more generally, distributed ledger technologies outside of cryptocurrencies. They take a pragmatic view, focusing on three main areas: the role of coin economies for “data malls” (specialized data marketplaces), data provenance (a historical record of data and its origins), and “keyless payments,” which are payments that can be made without having to know other users’ cryptographic keys. They also discuss voting and other areas and give a sizable list of academic and nonacademic references. <b>TOPICS:</b>Currency, quantitative methods
We provide empirical evidence within the context of cryptocurrency markets that the returns from liquidity provision, proxied by the returns of a short-term reversal strategy, are primarily concentrated in trading pairs with lower levels of market activity. Empirically, we focus on a moderately large cross section of cryptocurrency pairs traded against the U.S. Dollar from March 1, 2017 to March 1, 2022 on multiple exchanges. Our findings suggest that expected returns from liquidity provision are amplified in smaller, more volatile, and less liquid cryptocurrency pairs, where fear of adverse selection might be higher. A panel regression analysis confirms that the interaction between lagged returns and trading volume contains significant predictive information for the dynamics of cryptocurrency returns. This is consistent with theories that highlight the roles of inventory risk and adverse selection for liquidity provision.
This paper studies the concentration of block production in selected Proof-of-Stake (PoS) blockchains and finds evidence consistent with participants entering and leaving the consensus process, thereby changing the concentration level, but not with disproportionate compounding of wealth for large stakes.
Purpose The purpose of this paper is to analyze underpricing in initial coin offerings (ICO). It bridges the gap between findings in initial public offering (IPO) literature and empirical results from ICOs. Design/methodology/approach The sample set consists of 279 ICOs between April 2013 and January 2018. A regression analysis is performed with data from the ICOs. Findings The results show an average level of underpricing of ICOs of 123 percent in the USA and 97 percent in the other countries. The results for the US ICOs are significantly higher than for US IPOs on average and also higher than US IPOs at the beginning of the dot.com bubble. The authors also study the determinants of ICO underpricing. The authors use proxies based on asymmetric information from the IPO literature as well as ICO-related variables. First-day trading volume and a good sentiment on the ICO market go together with more ICO underpricing. Moreover, hot markets make first-day investors to benefit less. Finally, companies that use a large issue size or a pre-ICO (a sale of cryptocurrencies before the ICO) leave less money on the table. Research limitations/implications A first restriction is that the authors focus on ICOs and not on crowdfunding, though there are similarities in that both of them are novel ways to finance projects. A second restriction is that the authors had to decide on the definition of a listing day. Cryptocurrencies are traded on many exchanges, and if the exchange is tailored to the cryptocurrency itself, the data on, e.g., close prices are not necessarily to be trusted. The authors, therefore, decided to use close price data from coinmarketcap.com, which requires a listing on two exchanges. This choice implies that there may have been trades before the listing day itself. A third restriction arises from the relative newness of the ICO phenomenon. The authors gathered data on underpricing from coinmarketcap.com and combined that with project information from icobench.com. However, the data were not simply matched and they required manual adjustments based on several other sources. The authors hope that in due time data on ICOs will be as adequate as data on IPOs and that they become more readily available. It might help if regulators or the crypto community would institute publication requirements. Adherence to such requirements would also reduce the extent of fraud and of asymmetric information, so that solid issuers with good projects might benefit from less underpricing. Practical implications The research may help in reducing underpricing, as the authors find that issuers can reduce it by holding a pre-ICO and by considering larger issue sizes. If they do so, investors will get fewer opportunities to benefit from underpricing. Investors can, nevertheless, also profit from the knowledge generated in this paper. When market sentiment is positive and first-day trading volume is expected to be high, investing in ICOs is likely to give them higher first-day returns. Finally, the authors hope that this paper will serve as a basis for further research into the exciting and dynamic world of cryptocurrencies. Originality/value There is hardly any research on underpricing of ICOs. The paper is interesting for its table with a brief comparison of ICOs and IPOs. It also searches for variables from the asymmetric information theory behind IPOs to be applied in explaining ICOs. It shows high levels of ICO underpricing in comparison to IPOs. It also gives suggestions for issuers of (and investors in) ICOs.