The present study investigates the efficiency of the Bitcoin futures in the price discovery process by assessing the lead-lag relationship between the futures and spot prices of Bitcoin. The study tests whether the Bitcoin futures market is leading the price discovery mechanism for the Bitcoin spot market. The study considers daily closing prices of both Bitcoin spot and future indices from December 12, 2017 to December 31, 2020. The stationarity of the two time-series variables is tested using Augmented Dickey-Fuller test while the long-run co-integrating relationship is tested using Johansen Co-integration test. To test the long-run causality, the Error Correction Mechanism framework (ECM) is used while the Wald test is applied to assess the short-run causality between the Bitcoin future and spot prices. The results of trace and max-eigen statistics indicate that there is long term co-integrating relationship between Bitcoin futures and Bitcoin spot markets. The negative significant coefficient of error correction term indicates that there is long-run causality from the Bitcoin futures towards the Bitcoin spot market. The significant Chi-square test statistics of the Wald test suggest that there is short-run causality from the Bitcoin futures towards the Bitcoin spot market. This shows that the Bitcoin futures market is acting as a leading indicator and the Bitcoin spot market as a lagging indicator. Thus, it is concluded that the price discovery is taking place between Bitcoin futures and the Bitcoin spot market. With the entrance of the new information in the cryptocurrency market, it is first observed in the Bitcoin futures followed by the Bitcoin spot prices.
Abstract In this paper, we provide an in-depth analysis of the herding nature in the cryptocurrency market. We use the first 200 crypto coins data ranked based on market capitalization on January 1, 2020, to show the analysis. We illustrate the crypto investors' herding nature and intensity in different terms (by using daily, weekly, and monthly frequency data) and various states (high vs. low EPU states and high vs. low VIX states). We also demonstrate the magnitude of the herding effect on the next day's market returns in the cryptocurrency market.
Research on the prediction of cryptocurrency prices has been actively conducted, as cryptocurrencies have attracted considerable attention. Recently, researchers have aimed to improve the performance of price prediction methods by applying deep learning-based models. However, most studies have focused on predicting cryptocurrency prices for the following day. Therefore, clients are inconvenienced by the necessity of rapidly making complex decisions on actions that support maximizing their profit, such as âSellâ, âBuyâ, and âWaitâ. Furthermore, very few studies have explored the use of deep learning models to make recommendations for these actions, and the performance of such models remains low. Therefore, to solve these problems, we propose a deep learning model and three input features: sellProfit, buyProfit, and maxProfit. Through these concepts, clients are provided with criteria on which action would be most beneficial at a given current time. These criteria can be used as decision-making indices to facilitate profit maximization. To verify the effectiveness of the proposed method, daily price data of six representative cryptocurrencies were used to conduct an experiment. The results confirm that the proposed model showed approximately 13% to 21% improvement over existing methods and is statistically significant.
This paper aims to analyze the effect of Bitcoin on portfolio optimization using mean-variance, conditional value-at-risk (CVaR), and Markov regime switching approaches. I assessed each approach and developed the next based on the prior approach's weaknesses until I ended with a high level of confidence in the final approach. Though the results of mean-variance and CVaR frameworks indicate that Bitcoin improves the diversification of a well-diversified international portfolio, they assume that assets' returns are developed linearly and normally distributed. However, the Bitcoin return does not have both of these characteristics. Due to this, I developed a Markov regime switching approach to analyze the effect of Bitcoin on an international portfolio performance. The results show that there are two regimes based on the assets' returns: 1- bear state, where returns have low means and high volatility, 2- bull state, where returns have high means and low volatility.
In this work, I studied whether news media sentiments have an impact on Bitcoin volatility. In doing so, I applied three different range-based volatility estimates along with two different sentiments, namely psychological sentiments and financial sentiments, incorporating four various sentiment dictionaries. By analyzing 17,490 news coverages by 91 major English-language newspapers listed in the LexisNexis database from around the globe from January 2012 until August 2021, I found news media sentiments to play a significant role in Bitcoin volatility. Following the heterogeneous autoregressive model for realized volatility (HAR-RV)âwhich uses the heterogeneous market idea to create a simple additive volatility model at different scales to learn which factor is influencing the time seriesâalong with news sentiments as explanatory variables, showed a better fit and higher forecasting accuracy. Furthermore, I also found that psychological sentiments have medium-term and financial sentiments have long-term effects on Bitcoin volatility. Moreover, the National Research Council Emotion Lexicon showed the main emotional drivers of Bitcoin volatility to be anticipation and trust.
Gian Pietro Bellocca, Giuseppe Attanasio, Luca Cagliero, Jacopo Fior
Cryptocurrency trading has become more and more popular among private investors. According to recent studies, the momentum effect influences the underlying market. Quantitative trading systems can leverage momentum indicators to open and close trading positions. However, existing approaches that exploit the momentum effect in cryptocurrency trading do not rely on machine learning. Since these systems are based on human generated rules they are not suited to highly volatile market conditions, which are quite common in cryptocurrency markets. This paper proposes to leverage machine learning approaches to automatically detect the momentum effect in cryptocurrency market data. For each cryptocurrency it estimates the likelihood of being affected by the momentum effect on the next trading day as well as the momentum direction. A backtesting session, performed on three very popular cryptocurrencies, shows that the machine learning models are able to predict, to a good approximation, short-term price volatility thus reducing the number of false trading signals and increasing the return on investments compared to state-of-the-art approaches.
Throughout the history of modern finance, very few financial instruments have been as strikingly volatile as cryptocurrencies. The long-term prospects of cryptocurrencies remain uncertain; however, taking advantage of recent advances in neural networks and volatility, we show that the trading algorithms reinforced by short-term price predictions are bankable. Traditional trading algorithms and indicators are often based on mean reversal strategies that do not advantage price predictions. Furthermore, deterministic models cannot capture market volatility even after incorporating price predictions. Thus motivated by these issues, we integrate randomness in the price prediction models to simulate stochastic behavior. This paper proposes hybrid trading strategies that take advantage of the traditional mean reversal strategies alongside robust price predictions from stochastic neural networks. We trained stochastic neural networks to predict prices based on market data and social sentiment. The backtesting was conducted on three cryptocurrencies: Bitcoin, Ethereum, and Litecoin, for over 600 days from August 2017 to December 2019. We show that the proposed trading algorithms are better when compared to the traditional buy and hold strategy in terms of both stability and returns.
Abstract We examine the interactions between stablecoins, Bitcoin, and a basket of altcoins to uncover whether stablecoins represent the investorsâ demand for trading and investing into cryptoassets or rather play a role as boosting mechanisms during cryptomarkets price rallies. Using a set of instruments covering the standard cointegration framework as well as quantile-specific and non-linear causality tests, we argue that stablecoins mostly reflect an increasing demand for investing in cryptoassets rather than serve as a boosting mechanism for periods of extreme appreciation. We further discuss some specificities of 2017, even though the dynamic patterns remain very similar to the general behavior. Overall, we do not find support for claims about stablecoins being bubble boosters in the cryptoassets ecosystem.
This research aims to evaluate whether dynamic portfolios consisting of bitcoin and LQ45 stocks outperform portfolios composed solely of LQ45 stocks, especially during the Covid-19 pandemic. Accordingly, we use the time-series data of eight stocks and bitcoin from January 1, 2020, to December 31, 2020. We then run the DCC-GARCH method to analyze better the dynamic correlation between assets and the abnormalities of stock return distributions. The findings demonstrate that bitcoin is negatively correlated with LQ45 stocks, and hence, it can be used to hedge against stock assets. Further, we measure the portfolio performance of bitcoin-hedged and unhedged stock portfolios using the Jensen Index, Treynor Index, Sharpe Index, Sortino Ratio, and Omega Ratio. These measures consistently indicate that bitcoin-hedged stocks outperform unhedged stocks. In sum, our study concludes that incorporating bitcoin into portfolio formation improves portfolio performance.
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.
This paper examines the relationship between events reported in international news via categorical discourses and Bitcoin price. Natural language processing was adopted in this study to model data-driven discourses in the crypto-economy, specifically the Bitcoin market. Using topic modelling, namely Latent Dirichlet Allocation, a text analysis of cryptocurrency articles ( N = 4218) published from 60 countries in international news media identified key topics associated with cryptocurrency in the international news media from 2018 to 2020. This study provides empirical evidence that across the corpora of international news articles, 18 key topics were framed around the following categorical macro discourses: crypto-related crime, financial governance, and economy and markets. Analysis shows that the identified discourses may have had a âsocial signalâ effect on movements in the crypto-financial markets, particularly on Bitcoin's price volatility. Results show these specific discourses proved to have a negative effect on Bitcoin's market price, within 24 h of when the crypto news articles were published. Further, the study found that in some cases, the source of the news may have amplified the volatility effect, particularly in terms of geographical region, relative to broader market conditions.
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.
The main purpose of this study is to reveal whether seasonal/time-oriented/calendar anomalies affect the price and transaction volume of Bitcoin. Day of the week and month of the year anomalies are examined in this context. The data for the years 2013-2021 are handled in 3 different sampling periods, consisting of the whole of this time period and each of its divided parts. The existence of these anomalies is analyzed with EGARCH models created separately. The most important conclusion reached in this study is that the analyzed anomalies differ according to the sampling periods. The common findings reached as a result of the analyzes for all three time intervals are as follows: It has been determined that Monday has positive effects in terms of both Bitcoin return and transaction volume, while Saturday has negative effects only regarding transaction volume. Mondays, Tuesdays, and Wednesdays create volatility-increasing effects concerning returns, Friday, Saturday and Sunday reduce volatility. In terms of trading volume, Monday and Tuesday reduce volatility, while Thursday and Friday increase volatility. Whereas March has a positive effect on return volatility, it has a negative effect on trading volume volatility, and September has only a negative effect on return 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.
Jan Arvid Berg, Robin Fritsch, Lioba Heimbach, Roger Wattenhofer
Decentralized exchanges are revolutionizing finance. With their ever-growing increase in popularity, a natural question that begs to be asked is: how efficient are these new markets? We find that nearly 30% of analyzed trades are executed at an unfavorable rate. Additionally, we observe that, especially during the DeFi summer in 2020, price inaccuracies across the market plagued DEXes. Uniswap and SushiSwap, however, quickly adapt to their increased volumes. We see an increase in market efficiency with time during the observation period. Nonetheless, the DEXes still struggle to track the reference market when cryptocurrency prices are highly volatile. During such periods of high volatility, we observe the market becoming less efficient - manifested by an increased prevalence in cyclic arbitrage opportunities.
The global market capitalisation of bitcoin has exponentially increased in recent years and there are concerns that the current prices of bitcoin do not reflect the true and fair underlying value of this particular type of digital asset. Applying Cue utilisation theory and signalling theory, and using a panel data on bitcoin prices from Bloomberg between 1st November 2019 and 31st May 2021, we examine the association between celebrity and government endorsements and volatility in bitcoin prices. We find that positive celebrity tweets and positive government sentiments towards bitcoin are significantly positively associated with positive changes in its prices. Our findings imply that although celebrity endorsements may cause a temporary âexponential riseâ in bitcoin prices, investors need to carefully diversify their portfolio to maximise their riskâreturn relationship.
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.
Purpose. Significant capital inflows in the cryptocurrency market and record-breaking prices on cryptocurrency assets have led to the creation of alternative investment options on cryptocurrency markets, including a new field of decentralized investing, known as decentralized finance, operating on smart contracts. The objective of this study is to review investment options in the industry sector available to investors on cryptocurrency markets and decentralized protocols. Methodology. The model of decentralized cryptocurrency exchanges was used in the article. It is based on providing liquidity into the liquidity pool. Findings. The results of this study demonstrate that new industrial cryptocurrency investors have a wide range of investment options that can outperform strategies like passive holding of cryptocurrency or investing in the stock. Given the liquidity mining model attracts early investors, they need to look at assets such as governance tokens of different platforms. The Sharpe ratio of COMP and UNI tokens is higher than S&P500. In addition, these tokens are mined via a liquidity mining model. Originality. The crypto market has been growing rapidly since the beginning of the pandemic. The calculations for crypto assets might be influenced by the bull run on the crypto market because the last time such high Sharpe ratio for BTC and ETH was observed during the 20172018 cryptocurrency bubble. Investing in the crypto market is riskier than investing in the stock market due to high operational risks. Crypto market investors might prefer to mine or buy UNI or COMP tokens to diversify their portfolios. Practical value. According to the analysis results of the received information, a Sharpe ratio of investments in protocols for loanable funds is lower compared to investment options on the stock market or CeFi lending. It is also potentially riskier due to volatile interest rates and high operational risks.
Rebekka Buse, Konstantin Görgen, Melanie Schienle
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.
This paper explores price effects caused by the expiration of derivatives in the cryptocurrency market. Applying different statistical tests (ANOVA, MannâWhitney, and t-tests) and econometric methods (the modified cumulative abnormal return approach, regression analysis with dummy variables, and the trading simulation approach) to daily and weekly Bitcoin data over the period 2018â2021, the following hypotheses are tested: (H1) Expiration days create patterns in price behavior in the cryptocurrency market; and (H2) Price patterns can be exploited to generate abnormal profits from trading. The results suggest that expiration effects are only nominally present in the cryptocurrency market. There are differences in returns between expiration-related periods and average returns, but these differences are statistically insignificant. The only case in which an anomaly was detected was related to abnormally high returns during the week of expiration: returns during such weeks were positive in 65% of cases, and were on average 5 times higher than during usual weeks. Trading strategies based on this fact were able to generate results different from those of random trading, with a Sharpe ratio above 1. This is evidence in favor of the existence of a real price anomaly, which contradicts the efficient market hypothesis, and this could be implemented in the practice of traders and investors by creating trading strategies based on detected price effects or special technical analysis indicators to generate trading signals. For academics, these results might provide an opportunity to improve time series forecasting analysis in the case of Bitcoin.
In this chapter structures that generate yield in cryptofinance will be analyzed and related to leverage. While the majority of crypto-assets do not have intrinsic yields in and of themselves, similar to cash holdings of fiat currency, revolutionary innovation based on smart contracts, which enable decentralised finance, does generate return. Examples include lending or providing liquidity to an automated market maker on a decentralised exchange, as well as performing block formation in a proof of stake blockchain. On centralised exchanges, perpetual and finite duration futures can trade at a premium or discount to the spot market for extended periods with one side of the transaction earning a yield. Disparities in yield exist between products and venues as a result of market segmentation and risk profile differences. Cryptofinance was initially shunned by legacy finance and developed independently. This led to curious and imaginative adaptions, reminiscent of Darwin's finches, including stable coins for dollar transfers, perpetuals for leverage, and a new class of exchanges for trading and investment.
In recent years, cryptocurrencies have become a new topic for financial studies. In this study, the effects of positive and negative events related to cryptocurrencies on the prices of related cryptocurrencies were researched using the event study. These events include major listing, delisting and airdrop announcements, and SEC enforcements. As a result of the analysis, 22 significant abnormal return values related to negative events and eight significant abnormal return values related to positive events were determined at 1% significance level within the event window (-5, +10). Therefore, it has been determined that negative events have more effect on cryptocurrencies than positive events. The number of significant cumulative abnormal return values obtained (13 for negative events, three for positive events) also supports these results. The results of the study have crucial implications for investors, centralized cryptocurrency exchanges, and cryptocurrency CEOs. Even after the negative events were announced publicly, pull out of the market will prevent investors from making more losses. In addition, it is recommended that investors sell for profits in case of a rapid high return on the day of the listing announcement. Because it was determined that the prices returned to the equilibrium prices at the closing.
Robo-advisor is one of the most prominent innovation in the wealth management industry, and its success in Indonesia has been evident in the case of Bibit. Therefore, wealth management companies need to employ Robo-Advisor to overcome their competition. This research aims to give recommendation on asset allocation method and asset class selection for Robo-Advisors in Indonesia using Sharpe Ratio Analysis. Then, the author will analyze the robo-advisorâs performance during equity market downturn. Finally, The Robo-Advisorâs actual performance will be tested in 2018, 2019, and 2020. The Sharpe ratio analysis result showed that Robo-Advisors seeking higher risk-adjusted return should choose mean-variance optimization over risk parity for asset allocation method, and the inclusion of gold and bitcoin in a portfolio of stock mutual fund and bond mutual fund increases the risk-adjusted return of the portfolio. The proposed robo-advisorâs portfolio protected investors from equity market downturn in 2011-2010 in 83,3% of the case. Finally, the proposed robo-advisorâs portfolio generated better return for the conservative, moderate and aggressive investor during 2018, 2019, and 2020 when compared to LQ45.