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Momentum and contrarian effects on the cryptocurrency market

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Abstract

We report the results of investigation of the momentum and contrarian effects on cryptocurrency markets. The investigated investment strategies involve 100 (amongst over 1200 present as of date Nov 2017) cryptocurrencies with the largest market cap and average 14-day daily volume exceeding a given threshold value. Investment portfolios are constructed using different assumptions regarding the portfolio reallocation period, width of the ranking window, the number of cryptocurrencies in the portfolio, and the percent transaction costs. The performance is benchmarked against: (1) equally weighted and (2) market-cap weighted investments in all of the ranked assets, as well as against the buy and hold strategies based on (3) S&P500 index, and (4) Bitcoin price. Our results show a clear and significant dominance of the short-term contrarian effect over both momentum effect and the benchmark portfolios. The information ratio coefficient for the contrarian strategies often exceeds two-digit values depending on the assumed reallocation period and the width of the ranking window. Additionally, we observe a significant diversification potential for all cryptocurrency portfolios with relation to the S&P500 index.

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... The main idea and methodology concepts were adopted from the research paper 'Nonlinear support vector machines can systematically identify stocks with high and low future returns' by Huerta et al. (2013) and 'Momentum and contrarian effects on the cryptocurrency market' by Kość et al. (2018). ...
... Another paper that contributed to the methodological and strategy implementation part is the work of Kość et al. (2018), which investigates the momentum and contrarian effects on cryptocurrency markets. The performance of investment portfolios was benchmarked against (1) equally weighted and (2) market-cap weighted investments as well as against the B&H strategies based on (3) S&P500 index and (4) BTCUSD price. ...
... To construct the benchmark portfolios, the Top100 market cap ranking was used. To estimate the efficiency of the main SVM strategy, similarly as in Kość et al. (2018), the benchmark portfolios were chosen as follows: -The benchmark equally weighted portfolio (further denoted as EqW) is constructed as an investment with equal weights in all cryptocurrencies, which are qualified for Top100 on the reallocation day. As for a base case, the reallocation period is set to one week. ...
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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.
... This total capitaliza other cryptocu Cryptocurrencies are a high-risk investment for investors. However, based on the volatility indicator, the potential profitability of various investment instruments was considered using the example of the currency, cryptocurrency, and stock markets [2,[11][12]. The market price of cryptocurrencies for the period 2020-2023 was considered in Table 2. ea of "cryptocu field (Table 3) rection of "crypt try, units [3]. ...
... Market price of cryptocurrencies for the period 2020-2023[1][2][3]. ...
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Cryptocurrencies are digital assets that are used to store and protect savings. The study examined the cryptocurrency market and analyzed the development of investment. A methodology for researching the crypto-asset market is proposed. The main directions for forming a cryptocurrency portfolio (crypto portfolio) have been identified. Methods for forming an optimal “cryptocurrency portfolio” (hedging) have been studied, including risk assessment in the context of income from cryptocurrency and determining the relationship between profitability and volatility. Crypto assets with the “Proof-of-Work” principle (for example, Bitcoin) as a source of investment for environmental events are acceptable when attracting other financial instruments. The prerequisites for developing the cryptocurrency market as a source of investment in “green” projects have been determined.
... According to the authors, lower quantiles of the daily return distribution and upper quantiles of the weekly return distribution show positive correlation with historical returns, indicating overreaction in the Bitcoin market. In their study, Kosc et al. (2019) report the results of investigation of the momentum and contrarian effects on cryptocurrency markets, with the investigated investment strategies involving 100 cryptocurrencies with the largest market cap. Their empirical results show a clear and significant dominance of the short-term contrarian effect over both momentum effect and the benchmark portfolios, along with a significant diversification potential for all cryptocurrency portfolios with relation to the S&P500 index. ...
... Main findings Borgards (2021) Empirical results give evidence that high percentage of asset classes' creation phases are followed by momentum periods, indicating that the momentum effect is robust Caporale and Plastun (2020) Findings show that hourly returns on positive/negative anomalous returns during the day are considerably higher/lower than on a typical positive/ negative day Chevapatrakul and Mascia (2019) Lower quantiles of the daily return distribution and upper quantiles of the weekly return distribution show positive correlation with historical returns, indicating overreaction Chu et al. (2020) Findings indicate that the momentum method has the potential to be utilised successfully for bitcoin trading at a high frequency Grobys and Sapkota (2019) The findings, in contrary to earlier studies, do not indicate any evidence of significant momentum payoffs Jia et al. (2022) The study introduced a three-factor pricing model including market, size and momentum factors, outperforming relevant models Kosc et al. (2019) The empirical results show a clear and significant dominance of the short-term contrarian effect over both momentum effect and the benchmark portfolios Liu et al. (2020) Results show that there are anomalous returns that decrease with size and increase with return momentum, and the momentum effect is more significant in small cryptocurrencies Wen et al. (2022) The findings showcase evidence of intraday return predictability, consisting of both intraday momentum and reversal, in the cryptocurrency market Table 2. Set of studies on the topics of momentum and overreaction ...
Article
Purpose The present study sets out to examine the empirical literature on the behavioural aspects of cryptocurrencies, showing the findings of related studies and discussing the various results. A systematic literature review of cryptocurrencies in behavioural finance seems to be timely and particularly important in terms of providing a guide for future research. Key topics include an extent review on the issue of herding behaviour amongst cryptocurrencies, momentum effects and overreaction, contagion effect, sentiment and uncertainty, along with studies related to investment decision-making, optimism bias, disposition, lottery and size effects. Design/methodology/approach Systematic literature review. Findings A systematic literature review of cryptocurrencies in behavioural finance seems to be timely and particularly important in terms of providing a guide for future research. Key topics include an extent review on the issue of herding behaviour amongst cryptocurrencies, momentum effects and overreaction, contagion effect, sentiment (investor's, market's) and uncertainty, along with studies related to investment decision-making, optimism bias, disposition, lottery and size effect. Originality/value The authors' survey paper complements recent papers in the area by offering a systematic account on the influence of behavioural factors on cryptocurrencies. Further, this study's purpose is not just to index the relevant literature, but rather to showcase and pinpoint several research areas that have emerged in the field of behavioural cryptocurrency research. For all these reasons, a systematic literature review of cryptocurrencies in behavioural finance seems to be timely and particularly important.
... In addition, Conrad and Kaul [37] showed that, while a contrarian strategy nets statistically significant profits at long horizons, a momentum strategy is usually profitable at the medium (3-12 months) horizon [37], indicating that the adoption of an investment strategy might depend on the time frame of the investment. In addition, similar findings are also shown in previous studies [36,60,[67][68][69]. We thus use different holding period returns to measure stock price performances while buying these ETFs as buying signals are emitted by diverse trading rules (i.e., MA, ROI, SOI, and KD trading rules). ...
... Concerning whether the performances of both portfolios (e.g., green energy and energy ETFs) would have a time-varying change, we then adopted 5, 10, 25, 50, 100, 150, 200, and 250-day HPRs to measure diverse HPRs after buying signals emitted by these trading signals based on the suggestion of previous studies [36,37,60,67,69]. We revealed that, while employing momentum strategies, the performance of the global clean energy ETF (i.e., ICLN) is better than that of the global energy ETF (i.e., IXC) for either almost all of the HPRs based on the MA trading rule or long-term HPRs according to the KD trading rules in Table 3. ...
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Based on technological innovation and climate change, clean energy has been paid increasing attention by worldwide investors, thereby increasing their interest in investing in firms that specialize in clean energy. However, traditional energy still plays an important role nowadays because extreme weather has often occurred in the winters of recent years. We thus explore whether investing strategies adopted by diverse technical trading rules would matter for in-vesting in energy-related ETFs. By employing two representative global ETFs with more than 10 years of data, iShares Global Clean Energy ETF as the proxy of clean energy performance and iShares Global Energy ETF as that of traditional energy performance, we then reveal that mo-mentum strategies would be proper for buying the green energy ETF but contrarian strategies would be appropriate for buying the energy ETF. Furthermore, based on investment strategies adopted by diverse technical trading rules, we show that the performance of clean energy out-performs that of energy, indicating that green energy does matter for the economy. Moreover, while observing the price trend of these two ETFs, we find that such two ETFs may have opposite share price performance, implying that while the green energy ETF reached a relatively high price, investors following contrarian strategies suggested in this study may reap profits by investing the energy ETF.
... The cryptocurrency market represents a particularly interesting case being rather new, relatively unexplored and at the same time extremely vulnerable to abnormal returns, given its high volatility relative to the FOREX, stock and commodity markets, etc. (Cheung et al. 2015;Aalborg et al. 2019;. A number of recent studies analyze momentum and contrarian effects in this market Kosc et al. 2019;Panagiotis et al. 2019;Qing et al. 2019;Yukun and Tsyvinski 2019) and obtain mixed results. ...
... Qing et al. (2019) using DFA and MF-DFA analysis found a strong momentum effect in BTC and ETH price behavior, and a reversion effect in XRP and EOS prices after abnormal returns. Kosc et al. (2019) investigated investment strategies in the cryptocurrency market and reported a clear and significant dominance of the short-term contrarian effect over momentum effect. Panagiotis et al. (2019) identified momentum effects in the cryptocurrency market; these are highly significant for short-term portfolios but less so in the long run. ...
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This paper examines whether there exists a momentum effect after one-day abnormal returns in the cryptocurrency market. For this purpose, a number of hypotheses of interest are tested for the Bitcoin, Ethereum and Litecoin exchange rates vis-à-vis the US dollar over the period 01.01.2015–01.09.2019, specifically whether or not: (H1) the intraday behavior of hourly returns is different on abnormal days compared to normal days; (H2) there is a momentum effect on days with abnormal returns, and (H3) after one-day abnormal returns. The methods used for the analysis include various statistical methods as well as a trading simulation approach. The results suggest that hourly returns during the day of positive/negative abnormal returns are significantly higher/lower than those during the average positive/negative day. The presence of abnormal returns can usually be detected before the day ends by estimating specific timing parameters. Prices tend to move in the direction of the abnormal returns till the end of the day when it occurs, which implies the existence of a momentum effect on that day giving rise to exploitable profit opportunities. This effect (together with profit opportunities) is also observed on the following day. In two cases (BTCUSD positive abnormal returns and ETHUSD negative abnormal returns), a contrarian effect is detected instead.
... In Bouri et al. [41], they prove, using Copula models, that financial stress effect has limited directional predictability on BTC. In Kosc et al. [42], they testify the existence of strong short-term contrarian effect. ...
... Regarding the momentum effect, Grobys and Sapkota [45] testify the nonexistence of significant momentum payoffs. Similarly, Kosc et al. [42] find lack of momentum effect. However, Cheng et al. [46] find strong momentum effect in BTC using DFA and MF-DFA. ...
Article
Bitcoin has rapidly gained much attention by media, investors and scholars, since it is widely used for investment purposes as an alternative to regular currencies. The price of bitcoin is characterized with high volatility, which makes firms that hold large amounts of it prone to risks. To prevent those risks and to gain insights into its behavior and trading strategies, it is necessary to forecast bitcoin volatility. In that context, this study reviews the impact of different factors on BTC price, returns and volatility. Moreover, it examines the sensitivity of GARCH family models to standardized residuals distributions in forecasting BTC volatility based on the leverage effect. Then, it compares the best GARCH-type model against the best ANN model with respect to short-term and long-term horizons. Results outline that APARCH, TGARCH and EGARCH are very sensitive to standardized residuals distributions, such that TGARCH run with the normal distribution is the best model that captures BTC volatility. Further, MLP outperforms all the parametric and nonparametric models, while its accuracy weakens with the level of forecasting horizons. Consequently, nonparametric models prevail parametric models in BTC volatility forecasting due to their high degree of flexibility and strong generalization abilities, whereas MLP is only effective in short-term forecasting.
... Additionally, there have been attempts to combine low liquidity and non-correlated investment strategies across various asset classes, resulting in significantly increased risk-adjusted returns of such combined systems [23]. Such research has been conducted not only on classical assets like equities, bonds, currencies, or commodities [27], but also on newly recognized asset classes such as volatility and cryptocurrency [11,20,27,14]. ...
... Long Only means the strategy which opens only Long positions when Long signal is generated and stays out of the market when Short or Hold signal is generated. the details can be found in[18] ...
... In order to evaluate the efficiency of tested strategies, we calculate the following performance metrics based on Kosc et al. (2019) and Bui andŚlepaczuk (2021). The performance metrics were divided into four categories: ...
... However, similarly to other cryptocurrency markets, many previous studies also indicate that the Bitcoin market is still inefficient (e.g. Kosc, Sakowski & Ślepaczuk, 2019). ...
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Despite recent studies focused on comparing the dynamics of market efficiency between Bitcoin and other traditional assets, there is a lack of knowledge about whether Bitcoin and emerging markets efficiency behave similarly. This paper aims to compare the market efficiency dynamics between Bitcoin and the emerging stock markets. In particular, this study indicates whether the dynamics of Bitcoin market efficiency mimic those of emerging stock markets. Thus, the paper's contribution emerges from the combination of Bitcoin and emerging markets in the field of dynamics of market efficiency. The dynamics of market efficiency are measured using the Hurst exponent in the rolling window. The study uses daily data for the MSCI Emerging Markets Index and the Bitcoin market over the period 2011–2022. Our results show that there is at most a moderate correlation between the dynamics of Bitcoin and emerging stock markets’ efficiency over the entire study period. The strongest correlations occur mainly in periods of high economic policy uncertainty in the largest Bitcoin mining countries. Therefore, the association between Bitcoin market efficiency and emerging stock markets’ efficiency may strengthen with an increase in economic policy uncertainty. These findings may be useful for investors and portfolio managers in constructing better investment strategies.
... Second, it affirms the contrarian behavior of the investors against the prevailing market sentiments (Kosc et al., 2019). Investors believe market sentiment during extreme events is evidence of irrationality, leading to mispriced assets. ...
Article
This study comprehensively analyzes herding behavior in the cryptocurrency market. First, we conduct an in-depth investigation of herding behavior in the overall cryptocurrency market. Second, we form several groups of cryptocurrencies according to their characteristics and analyze whether each group behaves similarly in volatile market regimes. Third, we investigate whether herding existed in each cryptocurrency group before and during the COVID-19 pandemic. Using a sample of 227 cryptocurrencies constituting nearly 95% of market capitalization, we reveal that herding behavior was absent in the overall sample and sub-samples comprising cryptocurrency groups. Further, the anti-herding behavior implies a contrarian response to the crowd. This anti-herding can be explained from two views: rational behavior of taking profit from market irrationality and irrational behavior due to fear or recency bias.
... Furthermore, Conrad and Kaul [87] demonstrated that, while a contrarian strategy generates statistically significant profits over long time horizons, a momentum strategy is typically profitable over medium time horizons (3-12 months), indicating that investors may choose different investment strategies based on their investment horizon. Moreover, similar findings have been reported in previous studies [68,[88][89][90]. Thus, we examine subsequent index performance as oversold signals emitted by the SOI, RSI, and BB trading rules at the outset, utilizing various holding period return measures (HPRs). ...
... We find studies which show that shifts in prices are followed by price movements in the opposite direction (overreaction effect) such as those of De Thaler (1985, 1987), Atkins and Dyl (1990), Bremer and Sweeney (1991), Dissanaike (1994), Gunaratne and Yonesawa (1997), Fung et al. (2000), Benou and Richie (2003), Grant et al. (2005), Ising et al. (2006), Miralles-Marcelo et al. (2010, Choi and Hui (2014), Lalwani et al. (2019), Miwa (2019), and Bogards and Czudaj (2020), among others. On the other hand, we also find studies which report that these shifts lead to price movements in the same direction which is known as the momentum effect or underrreaction, see Jegadeesh and Titman (1993), Cox and Peterson (1994), Lasfer et al. (2003), Savor (2012), 749 Caporale and Plastun (2019), Kosc et al. (2019), andQing et al. (2019), among others. ...
Article
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Research background: A current strand of the financial literature is focusing on detecting inefficiencies, such as the day-of-the-week effect, in the cryptocurrency market. However, these studies are not considering that there are no daily closes in this market, and it is possible to trade cryptocurrencies on a continuous basis. This fact may have led to biases in previous empirical results. Purpose of the article: We propose to analyse the day-of-the-week effect on the Bitcoin from an alternative perspective where each hourly data in a day is considered an event. Focusing on that objective, we employ hourly closing prices for Bitcoin which are taken from the Kraken exchange, one of the world leading exchanges and trading platforms in the cryptocurrency markets, for the period spanning from January 2016 to December 2021. Methods: Contrary to the previous empirical evidence, we do not calculate daily returns, but rather the first stage of our proposed approach is devoted to analysing the hourly mean returns for each of the 24 hours of the day for each day of the week. We look for statistically significant hourly mean returns that could advance the importance of the hourly differentiation in the Bitcoin market. In a second stage, we calculate different post-event cumulative returns which are defined as the change in log prices over a time interval. Finally, we propose different investment strategies simply based on the significant hourly mean returns we obtain and we evaluate their performance in terms of the Sharpe ratio. Findings & value added: We contribute to the debate about the degree of Bitcoin?s market efficiency by providing an alternative methodology based on an event study hourly approach. Furthermore, we provide evidence that by investing in different post-event hourly windows it is possible to outperform the classic buy-and-hold strategy.
... Since the irruption of cryptocurrencies in the financial markets, the literature of these new assets has not stopped growing, although most have focused on the study of Bitcoin. One of the lines of research open in the cryptocurrency market is the analysis of the efficiency of these markets (Urquhart, 2016;Wei, 2018;Caporale et al., 2018;Kosc et al., 2019;Charfeddine and Maouchi, 2019;L opez-Mart ın et al., 2021, among others). ...
Article
Purpose This paper examines the effect of the holy month of Ramadan on the returns and conditional volatility of cryptocurrency markets. Design/methodology/approach The closing prices of six cryptocurrencies have been considered. The study employs different classical tests for checking if the efficiency behaviour is similar during Ramadan celebration days and non-Ramadan days. Besides, dummy variable regression technique for assessing this anomaly on returns and volatilities has been applied. Findings Although no significant effect on returns and volatility for Litecoin has been found, the results provide evidence about the existence of the Ramadan effects in cryptocurrency markets. The results of the mean equations show the existence of Ramadan effect for Ethereum, Ripple, Stellar and BinanceCoin for all considered models. Significant effect on Bitcoin returns is found with an autoregressive model of order 1. The results of conditional volatility show Ramadan effect on volatility is not detected. Originality/value First, a new contribution in the incipient study of cryptocurrency analysis. Second, a comprehensive review of recently published empirical articles about Ramadan effect on traditional assets has been carried out. Third, unlike most of the papers focussed on the study of Bitcoin, this study has been extended to six cryptocurrencies. Ramadan effect have not been analysed in cryptomarkets yet. This study come to fill this gap and analyses Ramadan effect, previously documented for traditional assets, in particular, stock index from Muslim countries, but not yet analysed in the cryptocurrency markets.
... They indicate an inverse relationship between economic uncertainty and the level of connectedness, suggesting the potential hedging ability of cryptocurrencies against economic uncertainty. Kosc et al. (2019) examine momentum and contrarian effects among 100 cryptocurrencies with the highest market capitalization and find evidence of a strong short-term contrarian effect that dominates the momentum effect. Borgards (2021) supports the momentum effect in cryptocurrency markets using data on twenty cryptocurrencies and proposes a momentum trading strategy. ...
... In order to evaluate the efficiency of tested strategies we calculate the following performance metrics based on Kosc et al. (2019) [45] and Bui andŚlepaczuk (2021) [46]. ...
Article
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We use LSTM networks to forecast the value of the BTC and S&P500 index, using data from 2013 to the end of 2020, with the following frequencies: daily, 1 h, and 15 min data. We introduce our innovative loss function, which improves the usefulness of the forecasting ability of the LSTM model in algorithmic investment strategies. Based on the forecasts from the LSTM model we generate buy and sell investment signals, employ them in algorithmic investment strategies and create equity lines for our investment. For this purpose we use various combinations of LSTM models, optimized on in-sample period and tested on out-of-sample period, using rolling window approach. We pay special attention to data preprocessing in the input layer, to avoid overfitting in the estimation and optimization process, and assure correct selection of hyperparameters at the beginning of our tests. The next stage is devoted to the conjunction of signals from various frequencies into one ensemble model, and the selection of best combinations for the out-of-sample period, through optimization of the given criterion in a similar way as in the portfolio analysis. Finally, we perform a sensitivity analysis of the main parameters and hyperparameters of the model.
... Moreover, in order to evaluate the efficiency of algorithmic investment strategies built based on the signals from econometric models, we calculated the performance metrics based on the created equity lines and formulas from Kość et al. [38] and Zenkova and Slepaczuk [39]. ...
Article
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This research aims to compare the performance of ARIMA as a linear model with that of the combination of ARIMA and GARCH family models to forecast S&P500 log returns in order to construct algorithmic investment strategies on this index. We used the data collected from Yahoo Finance with daily frequency for the period from 1 January 2000 to 31 December 2019. By using a rolling window approach, we compared ARIMA with the hybrid models to examine whether hybrid ARIMA-SGARCH and ARIMA-EGARCH can really reflect the specific time-series characteristics and have better predictive power than the simple ARIMA model. In order to assess the precision and quality of these models in forecasting, we compared their equity lines, their forecasting error metrics (MAE, MAPE, RMSE, MAPE), and their performance metrics (annualized return compounded, annualized standard deviation, maximum drawdown, information ratio, and adjusted information ratio). The main contribution of this research is to show that the hybrid models outperform ARIMA and the benchmark (Buy&Hold strategy on S&P500 index) over the long term. These results are not sensitive to varying window sizes, the type of distribution, and the type of the GARCH model.
... Based on the methodology presented in Kość et al. (2019) we calculated the following performance statistics enabling us to evaluate tested algorithmic strategies. ...
Article
This research aims to seek an alternative approach to stock selection for algorithmic investment strategy. We try to build an effective pair trading strategy based on 103 stocks listed in the NASDAQ 100 index. The dataset has a daily frequency and covers the period from 01/01/2000 to 31/12/2018 , and to 01/07/2021 as an additional out-of-time data set. In this study, Generalized Hurst Exponent, Correlation, and Cointegration methods are employed to detect the mean-reverting pattern in the time series of a linear combination of each pair of stock. The result shows that the Hurst method cannot outperform the benchmark, which implies that the market is efficient. These results are quite sensitive to varying number of pairs traded and rebalancing period but they are less sensitive to financial leverage degree. Moreover, the Hurst method is better than the cointegration method but is not superior as compared to the correlation method.
... The emergence of Bitcoin and other cryptocurrencies in the market has been studied during this decade (Gil-Alana et al., 2020). The literature on cryptocurrencies has covered a wide range of divergent aspects, beginning with the technical and investment characteristics of Bitcoin in relation to risk and returns (Wei, 2018;Tan et al., 2020;Omane-Adjepong et al., 2019;Kosc et al., 2019;Da Gama Silva et al., 2019;Brauneis and Mestel, 2018;Bouri, et al., 2019;Antonakakis et al., 2019;Yi et al., 2018;Koutmos, 2018). There are vast sum of studies focusing on Bitcoin suitability as an asset to diversify the risks of other traditional assets, which is consistent with the purpose of this paper. ...
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Purpose This study examines the inter-linkages between Bitcoin prices and CEE stock markets (Hungary, the Czech Republic, Poland, Romania and Croatia). Design/methodology/approach The dynamic contemporaneous nexus has been analyzed using both the multivariate DECO-GARCH model proposed by Engle and Kelly (2012) and quantile on quantile (QQ) methodology proposed by Sim and Zhou (2015). Our study is implemented using the daily data spanning from 6 September 2012 to 12 August 2019. Findings First, the findings show that the average return equicorrelation across Bitcoin prices and CEE stock indices are positive, even though it is found to be time-varying over the research period shown. Second, the Bitcoin-CEE stock market association has positive signs for most pairs of quantiles of both variables and represents a rather similar pattern for the cases of Poland, the Czech Republic and Croatia. However, a weaker and primarily negative connectedness is found for Hungary and Romania, respectively. Furthermore, the interconnectedness between the co-movements in the Bitcoin market and stock returns changes significantly across quantiles of both variables within each nation, indicating that the Bitcoin-stock market relationship is dependent on both the cycle of the stock market and the nature of Bitcoin price shocks. Practical implications The evidence documented in this study has significant implications for divergent economic agents, including global investors, risk managers and policymakers, who would benefit from a comprehensive knowledge of the Bitcoin-stock market relationship to build efficient risk-hedging models and to conduct appropriate policy reactions to information spillover effects in different time horizons. Originality/value This paper is the first study employing both the multivariate DECO-GARCH model and QQ methodology to shed light on the nexus between Bitcoin prices and the stock markets in CEE countries. The DECO model uses more information to compute dynamic correlations between each pair of returns than standard dynamic conditional correlation (DCC) models, declining the estimation noise of the correlations. Besides, QQ approach allows us to capture some nuanced features of the Bitcoin-stock market relationship and explore the interdependence in its entirely. Therefore, the main contribution of this article to the related literature in this field is significant. 研究目的 本研究旨在探討比特幣的價格與中東歐股市(匈牙利、捷克共和國、波蘭、羅馬尼亞和克羅地亞) 之相互聯繫. 研究設計/方法/理念 研究使用恩格爾與凱利(2012)(Engle and Kelly (2012)) 提出的多變量DECO-GARCH模型及Sim 與Zhou(2015)(Sim and Zhou ( 2015)) 研製的分位數-分位數方法來分析動態同期的聯繫。我們的研究使用由2012年9月6日至2019年8月12日期間取得的每日數據來進行. 研究結果 首先、研究結果顯示、跨比特幣價格與中東歐股價指數的平均回報當量關聯是正相關的,即使在研究期間被發現是隨時間而變化的。第二、比特幣與中東歐股市之聯繫在大多數兩變數分位數對而言出現正相關跡象,而且,這聯繫在波蘭、捷克共和國及克羅地亞而言表現一個頗相似的模式。唯就匈牙利而言、這聯繫則較弱、而羅馬尼亞則主要是負聯繫。研究結果亦顯示: 比特幣市場內的聯動與股票回報間之內在關聯會在每個國家內跨兩個變數的分位數而顯著地改變,這顯示比特幣-股市關係是取決於股市的週期和比特幣價格衝擊的本質. 實際的意義 本研究所記載的證據、對不同的經濟行為者而言極具意義 (這包括國際投資者、風險管理經理和政策制定者),因他們會受惠於對比特幣-股市關係的全面認識,他們可建立有效的風險對沖模型、及在不同時間範圍對資訊溢出效應進行適當的政策反應. 研究的原創性/價值 本文為首個研究使用多變量DECO-GARCH模型和分位數-分位數(QQ)方法、來解釋比特幣價格與中東歐國家之股市的關係。這DECO模型使用比標準動態條件關係模型更多資訊,來計算每對回報間之動態關係,這能減少估測雜訊,而且,QQ方法讓我們可以取得比特幣-股市關係的一些細微特徵及全面地探索其相互依賴性。因此,本文的主要貢獻是在這學術領域內有關的文獻上.
... The emergence of Bitcoin and other cryptocurrencies in the market has been studied during this decade (Gil-Alana et al., 2020). The literature on cryptocurrencies has covered a wide range of divergent aspects, beginning with the technical and investment characteristics of Bitcoin in relation to risk and returns (Wei, 2018;Tan et al., 2020;Omane-Adjepong et al., 2019;Kosc et al., 2019;Da Gama Silva et al., 2019;Brauneis and Mestel, 2018;Bouri, et al., 2019;Antonakakis et al., 2019;Yi et al., 2018;Koutmos, 2018). There are vast sum of studies focusing on Bitcoin suitability as an asset to diversify the risks of other traditional assets, which is consistent with the purpose of this paper. ...
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... Bianchi et al. (2015), Yang et al. (2018) and Yu et al. (2019) confirmed the presence of momentum in the commodity futures market. Cheng et al. (2019), Kosc et al. (2019) and Grobys and Sapkota (2019) applied the momentum strategy on Cryptocurrencies. ...
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... The 59 eliminated articles were eliminated for various reasons such as not having any indication of use case examples or in general having little to do with blockchain. Articles focused exclusively on bitcoin/cryptocurrencies were also eliminated as these use cases are considered to already having met TRL9 considering the total market value of cryptocurrencies is over 200 billion USD [20]. The 75 remaining articles were then fully read to identify and evaluate the TRL-stage of the various use cases presented within the articles. ...
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This paper sets out to explore the hedging capabilities of bitcoin by applying the asymmetric GARCH methodology used in investigation of gold. The results show that bitcoin can clearly be used as a hedge against stocks in the Financial Times Stock Exchange Index. Additionally bitcoin can be used as a hedge against the American dollar in the short-term. Bitcoin thereby possess some of the same hedging abilities as gold and can be included in the variety of tools available to market analysts to hedge market specific risk.
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Amid its rapidly increasing usage and immense public interest the subject of Bitcoin has raised profound economic and societal issues. In this paper we undertake economic and econometric modelling of Bitcoin prices. As with many asset classes we show that Bitcoin exhibits speculative bubbles. Further, we find empirical evidence that the fundamental price of Bitcoin is zero.
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We use Bitcoin and S&P 500 Index daily return data to examine relative volatility using detrended ratios. We then model Bitcoin market returns with selected economic variables to study the drivers of Bitcoin market returns. We report strong evidence to suggest that Bitcoin volatility is internally (buyer and seller) driven leading to the conclusion that the Bitcoin market is highly speculative at present.
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A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution. Digital signatures provide part of the solution, but the main benefits are lost if a trusted third party is still required to prevent double-spending. We propose a solution to the double-spending problem using a peer-to-peer network. The network timestamps transactions by hashing them into an ongoing chain of hash-based proof-of-work, forming a record that cannot be changed without redoing the proof-of-work. The longest chain not only serves as proof of the sequence of events witnessed, but proof that it came from the largest pool of CPU power. As long as a majority of CPU power is controlled by nodes that are not cooperating to attack the network, they'll generate the longest chain and outpace attackers. The network itself requires minimal structure. Messages are broadcast on a best effort basis, and nodes can leave and rejoin the network at will, accepting the longest proof-of-work chain as proof of what happened while they were gone.
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This paper documents a strong and prevalent momentum effect in industry components of stock returns which accounts for much of the individual stock momentum anomaly. Specifically, momentum investment strategies, which buy past winning stocks and sell past losing stocks, are significantly less profitable once we control for industry momentum. By contrast, industry momentum investment strategies, which buy stocks from past winning industries and sell stocks from past losing industries, appear highly profitable, even after controlling for size, book-to-market equity, individual stock momentum, the cross-sectional dispersion in mean returns, and potential microstructure influences. Copyright The American Finance Association 1999.
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In a previous paper, we found systematic price reversals for stocks that experience extreme long‐term gains or losses: Past losers significantly outperform past winners. We interpreted this finding as consistent with the behavioral hypothesis of investor overreaction. In this follow‐up paper, additional evidence is reported that supports the overreaction hypothesis and that is inconsistent with two alternative hypotheses based on firm size and differences in risk, as measured by CAPM‐betas. The seasonal pattern of returns is also examined. Excess returns in January are related to both short‐term and long‐term past performance, as well as to the previous year market return.
Why You Should Not Invest in BTC Mining Endeavour, Working Papers of Faculty of Economic Sciences
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