Niclas Dombrowski, Wolfgang Drobetz, Paul P. Momtaz
Crypto funds (CFs) are a growing intermediary in cryptocurrency markets. We evaluate CF performance using metrics based on alphas, value at risk, lower partial moments, and maximum drawdown. The performance of actively managed CFs is heterogeneous: While the average fund in our sample does not outperform the overall cryptocurrency market, there seem to be some few funds with superior skills. Given the non-normal nature of fund returns, the choice of the performance measure affects the rank orders of funds. Compared to the Sharpe ratio, the most commonly applied metric in the asset management practice, performance measures based on alphas and maximum drawdown lead to diverging fund rankings. Depending on their ranking order of preferences, CF investors should consider a bundle of metrics for fund selection and performance measurement.
Daniel Pereira Alves de Abreu, Robert Aldo Iquiapaza
Purpose The aim of the study was to analyze the performance of Black-Litterman (BL) portfolios using a views estimation procedure that simulates investor forecasts based on technical analysis. Design/methodology/approach Ibovespa, S&P500, Bitcoin and interbank deposit rate (IDR) indexes were respectively considered proxies for the national, international, cryptocurrency and fixed income stock markets. Forecasts were made out of the sample aiming at incorporating them in the BL model, using several portfolio weighting methods from June 13, 2013 to August 30, 2022. Findings The Sharpe, Treynor and Omega ratios point out that the proposed model, considering only variable return assets, generates portfolios with performances superior to their traditionally calculated counterparts, with emphasis on the risk parity portfolio. Nonetheless, the inclusion of the IDR leads to performance losses, especially in scenarios with lower risk tolerance. And finally, given the impact of turnover, the naive portfolio was also detected as a viable alternative. Practical implications The results obtained can contribute to improve investors practices, specifically by validating both the performance improvement – when including foreign assets and cryptocurrencies –, and the application of the BL model for asset pricing. Originality/value The main contributions of the study are: performance analysis incorporating cryptocurrencies and international assets in an uncertain recent period; the use of a methodology to compute the views simulating the behavior of managers using technical analysis; and comparing the performance of portfolio management strategies based on the BL model, taking into account different levels of risk and uncertainty.
Purpose This paper provides a thorough examination of Socios.com, a blockchain platform that integrates token sales with the fan experience in the sports industry. The study focuses on three key aspects: the performance, bubble phenomenon and dynamics of fan tokens. The author aims to address important questions that may concern potential supporters and investors. Might sports fans incur financial losses due to their team loyalty? Is the fan token market just a passing trend? Are fan tokens driven by the behaviour of the cryptocurrency market? Design/methodology/approach This analysis aims to involve several methodologies. The author evaluates the short- and long-term performance of fan tokens by computing first-day and buy-and-hold (abnormal) returns. The author also employs the Phillips, Shi, and Yu's (PSY) real-time bubble detection method to investigate the presence of bubble phenomenon in the fan token market segment. Finally, the author examines the potential dependences between fan tokens, Chiliz and the cryptocurrency market (represented by the CCi30 index) using both Pearson/Kendall correlations and the wavelet coherence approach. Findings The study presents three notable contributions to the existing literature. First, the author demonstrates that investing in fan tokens to support one's favourite sports teams can lead to financial losses, whereas traders can potentially outperform the market by investing in Chiliz. Second, the author states that fan tokens were a short-lived trend, as evidenced by their decline in value after the bubble burst in 2021. Third, the findings indicate that the fan token market was influenced by the cryptocurrency market and Chiliz during periods of market downturns. Originality/value To the best of author’s knowledge, this is the first paper to conduct a comprehensive analysis of the performance, bubble phenomenon and dynamics of the token market fan segment, along with the exclusive on-platform currency, Chiliz.
In this paper, the generalised extreme value distribution (GEVD) model is employed to estimate financial risk in the form of return levels and the value at risk (VaR) for the two exchange rates, BitCoin/US dollar (BTC/USD) and the South African rand/US dollar (ZAR/USD). The Basel Committee on Banking Supervision (BCBS) responsible for developing supervisory guidelines for banks and financial trading desks recommended that VaR be computed and reported. The maximum likelihood estimation (MLE) method is used to estimate the parameters of the GEVD. The estimated risk values are used to compare the riskiness of the two exchange rates and help both traders and investors to define their position in forex trading. This is to helping understanding the risk they are taking when they convert their savings/investments to BitCoin instead of the South African currency, the rand. The high extreme value index associated with the BTC/USD compared to the ZAR/USD implies that BitCoin is riskier than the rand. The BTC/USD has higher values of expected extreme/tail losses of 13.44%, 18.02%, and 23.41% at short (6 months), medium (12 months), and long (24 months) terms, compared to the ZAR/USD expected extreme/tail losses of 2.40%, 2.84%, and 3.28%, respectively. The computed VaR estimates for losses of USD 0.17, USD 0.22, and USD 0.38 per dollar invested in BTC/USD at 90%, 95%, and 99%, compared to ZAR/USD’s USD 0.03, USD 0.03, and USD 0.04 at the respective confidence levels, confirm the high risk associated with BitCoin. The conclusion drawn from this study is that BTC/USD is riskier than ZAR/USD, despite the rand being a developing country’s currency, hence perceived as being risky. The perception is that the rand is riskier than BitCoin and perceptions do influence exchange rates. Kupiec’s backtest results confirmed the model’s adequacy. These findings are helpful to investors, traders, and risk managers when deciding on trading positions for the two currencies.
Vasileios Kochliaridis, Eleftherios Kouloumpris, Ioannis Vlahavas
Abstract Cryptocurrency markets experienced a significant increase in the popularity, which motivated many financial traders to seek high profits in cryptocurrency trading. The predominant tool that traders use to identify profitable opportunities is technical analysis. Some investors and researchers also combined technical analysis with machine learning, in order to forecast upcoming trends in the market. However, even with the use of these methods, developing successful trading strategies is still regarded as an extremely challenging task. Recently, deep reinforcement learning (DRL) algorithms demonstrated satisfying performance in solving complicated problems, including the formulation of profitable trading strategies. While some DRL techniques have been successful in increasing profit and loss (PNL) measures, these techniques are not much risk-aware and present difficulty in maximizing PNL and lowering trading risks simultaneously. This research proposes the combination of DRL approaches with rule-based safety mechanisms to both maximize PNL returns and minimize trading risk. First, a DRL agent is trained to maximize PNL returns, using a novel reward function. Then, during the exploitation phase, a rule-based mechanism is deployed to prevent uncertain actions from being executed. Finally, another novel safety mechanism is proposed, which considers the actions of a more conservatively trained agent, in order to identify high-risk trading periods and avoid trading. Our experiments on 5 popular cryptocurrencies show that the integration of these three methods achieves very promising results.
Since its conception, the cryptocurrency market has been frequently described as an immature market, characterized by significant swings in volatility and occasionally described as lacking rhyme or reason. There has been great speculation as to what role it plays in a diversified portfolio. For instance, is cryptocurrency exposure an inflationary hedge or a speculative investment that follows broad market sentiment with amplified beta? We have recently explored similar questions with a clear focus on the equity market. There, our research revealed several noteworthy dynamics such as an increase in the market's collective strength and uniformity during crises, greater diversification benefits across equity sectors (rather than within them), and the existence of a "best value" portfolio of equities. In essence, we can now contrast any potential signatures of maturity we identify in the cryptocurrency market and contrast these with the substantially larger, older and better-established equity market. This paper aims to investigate whether the cryptocurrency market has recently exhibited similar mathematical properties as the equity market. Instead of relying on traditional portfolio theory, which is grounded in the financial dynamics of equity securities, we adjust our experimental focus to capture the presumed behavioral purchasing patterns of retail cryptocurrency investors. Our focus is on collective dynamics and portfolio diversification in the cryptocurrency market, and examining whether previously established results in the equity market hold in the cryptocurrency market and to what extent. The results reveal nuanced signatures of maturity related to the equity market, including the fact that correlations collectively spike around exchange collapses, and identify an ideal portfolio size and spread across different groups of cryptocurrencies.
Günümüzde ekonomilerin, işletmelerin başarılı ve sürdürülebilir bir şekilde büyümesi için sermaye piyasaları önem arz etmektedir. Varlık fiyatları alternatif yatırım araçları olmaları yönüyle hisse senedi piyasaları ile etkileşim içindedir. Dolayısıyla varlık fiyatlarında oluşan balonların hisse senedi piyasaları ile ilişki içinde olması beklenmektedir. Bu çalışmada 08:2010 ile 10:2022 arası aylık verilerle Dolar, Euro, Bitcoin, CDS ve mevduat faizi değişkenlerinde balon varlığı incelenmiştir. Ele alınan değişkenlerde balon oluşumunun varlığı durumunda bu balonların BIST 100 endeksi oynaklığına etkilerinin incelenmesi amaçlanmıştır. Balonların varlığı SADF ve GSADF testleri ile analiz edilirken, TARCH ve ARCH-GARCH modelleri yardımıyla oynaklık belirlenmeye çalışılmıştır. USD, Euro, Bitcoin değişkeni için ele alınan dönem boyunca istatistiksel olarak önemli balon oluşumları söz konusu iken, CDS ve mevduat değişkeni için söz konusu dönemde istatistiksel olarak önemli bir balon oluşumu gözlemlenmemiştir. USD ve Euro değişkenlerinde meydana gelen balonların BIST 100 endeks getirisinde oynaklığı artırdığı söylenebilir. Ancak BITCOIN de yaşanan balonların istatistiksel olarak anlamlı bir etkisinin olmadığı görülmüştür.
This study employs robust martingale difference hypothesis tests to examine return predictability in a broad sample of the 40 most capitalized cryptocurrency markets in the context of the adaptive market hypothesis. The tests were applied to daily returns using the rolling window method in the research period from May 1, 2013 to September 30, 2022. The results of this study suggest that the returns of the majority of the examined cryptocurrencies were unpredictable most of the time. However, a great part of them also suffered some short periods of weak-form inefficiency. The results obtained validate the adaptive market hypothesis. Additionally, this study allowed the observation of some differences in return predictability between the examined cryptocurrencies. Also some historical trends in weak-form efficiency were identified. The results suggest that the predictability of cryptocurrency returns might have decreased in recent years also no significant relationship between market cap and predictability was observed.
The response of the Bitcoin market to the novel coronavirus (COVID-19) pandemic is an example of how a global public health crisis can cause drastic market adjustments or even a market crash. Investor attention on the COVID-19 pandemic is likely to play an important role in this response. Focusing on the Bitcoin futures market, this paper aims to investigate whether pandemic attention can explain and forecast the returns and volatility of Bitcoin futures. Using the daily Google search volume index for the "coronavirus" keyword from January 2020 to February 2022 to represent pandemic attention, this paper implements the Granger causality test, Vector Autoregression (VAR) analysis, and several linear effects analyses. The findings suggest that pandemic attention is a granger cause of Bitcoin returns and volatility. It appears that an increase in pandemic attention results in lower returns and excessive volatility in the Bitcoin futures market, even after taking into account the interactive effects and the influence of controlling other financial markets. In addition, this paper carries out the out-of-sample forecasts and finds that the predictive models with pandemic attention do improve the out-of-sample forecast performance, which is enhanced in the prediction of Bitcoin returns while diminished in the prediction of Bitcoin volatility as the forecast horizon is extended. Finally, the predictive models including pandemic attention can generate significant economic benefits by constructing portfolios among Bitcoin futures and risk-free assets. All the results demonstrate that pandemic attention plays an important and non-negligible role in the Bitcoin futures market. This paper can provide enlightens for subsequent research on Bitcoin based on investor attention sparked by public emergencies.
A cryptocurrency is a digital asset maintained by a decentralised system using cryptography. Investors in this emerging digital market are exploring the profitability potential of portfolios in place of single coins. Portfolios are particularly useful given that price forecasting in such a volatile market is challenging. The crypto market is a self-organised complex system where the complex inter-dependencies between the cryptocurrencies may be exploited to understand the market dynamics and build efficient portfolios. In this letter, we use network methods to identify highly decorrelated cryptocurrencies to create diversified portfolios using the Markowitz Portfolio Theory agnostic to future market behaviour. The performance of our network-based portfolios is optimal with 46 coins and superior to benchmarks up to an investment horizon of 14 days, reaching up to 1,066% average expected return within 1 day, with reasonable associated risks. We also show that popular cryptocurrencies are typically not included in the optimal portfolios. Past price correlations reduce risk and may improve the performance of crypto portfolios in comparison to methodologies based exclusively on price auto-correlations. Short-term crypto investments may be competitive to traditional high-risk investments such as the stock market or commodity market but call for caution given the high variability of prices.
In response to the unprecedented uncertain rare events of the last decade, we derive an optimal portfolio choice problem in a semi-closed form by integrating price diffusion ambiguity, volatility diffusion ambiguity, and jump ambiguity occurring in the traditional stock market and the cryptocurrency market into a single framework. We reach the following conclusions in both markets: first, price diffusion and jump ambiguity mainly determine detection-error probability; second, optimal choice is more significantly affected by price diffusion ambiguity than by jump ambiguity, and trivially affected by volatility diffusion ambiguity. In addition, investors tend to be more aggressive in a stable market than in a volatile one. Next, given a larger volatility jump size, investors tend to increase their portfolio during downward price jumps and decrease it during upward price jumps. Finally, the welfare loss caused by price diffusion ambiguity is more pronounced than that caused by jump ambiguity in an incomplete market. These findings enrich the extant literature on effects of ambiguity on the traditional stock market and the evolving cryptocurrency market. The results have implications for both investors and regulators.
Paul Gerrans, Sherin Babu Abisekaraj, Zhangxin Liu
Abstract The “Fear of Missing Out” or FoMO has become an accepted motivator of behaviours extending from the purchase of limited-edition sneaker brands to social media use and cryptocurrency investment. As a motivator of individual financial behaviours, such as cryptocurrency and stock investment, it is unclear how FoMO relates to consumer financial literacy and other consumer traits, including risk tolerance and personality. We propose, and assess, a model of reported investment behaviour and investment behaviour intention. We find a larger association between FoMO and crypto ownership, both current and intended, compared with stocks. FoMO has a small association with current stock ownership, relative to the association of financial literacy and risk tolerance. Context matters when measuring FoMO with the more context-specific measures having the largest associations with investment behaviour and investment intentions. Finally, our results suggest financial literacy is an antecedent of FoMO, more so for stocks.
This systematic literature review summarizes the extant research in the Behavioral Finance (BeFi) and digital asset spaces to understand better the interactions of behavioral effects on the pricing of assets constructed, enabled, and exchanged in Decentralized Finance (DeFi) markets. We find that asset pricing in these rapidly evolving markets is better explained through BeFi than through traditional finance (TradFi) theory. Investor attention, sentiment, heuristics and biases, and network effects interact to form a highly volatile and dynamic market. We offer a deterministic research framework with propositions for future research. We further provide investors with a theoretically and empirically supported structure to better inform their decisions through an understanding of BeFi applications to DeFi.
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This study examines whether the Bitcoin market satisfies the (weak-form) efficient market hypothesis using a quantum harmonic oscillator, which provides the state-specific probability density functions that capture the superimposed Gaussian and non-Gaussian states of the log return distribution. Contrasting the mixed evidence from a variance ratio test, the high probability allocated to the ground state suggests a near-efficient Bitcoin market. Findings imply that as Bitcoin evolves into an efficient market, speculators might encounter difficulty in exploiting profitable trading strategies. Furthermore, when policymakers initiate tight regulations to control the market, they should closely monitor market efficiency as an index of price distortion.
According to the monetary theory, this paper believes that the demand for Bitcoin mainly includes two aspects: transaction demand and investment demand. This paper further discusses the impact of different demands on the price of Bitcoin based on two aspects of demand. Transaction demand and investment demand together affect the supply and demand relationship of the Bitcoin market. The empirical results show that the volatility of Bitcoin price is higher than that of international currencies and stocks as investment tools. This article emphasizes that the price of Bitcoin is primarily affected by supply and demand.
Researchers put efforts into explanations of the momentum phenomenon and improvements of the momentum strategy since the emergence of momentum in 1993. Interested in anomalies appearing as exhibited in traditional asset markets, adequate studies are launched on the nascent phenomenon emergers in the last decade, the cryptocurrency market. Recent studies have shown that there is hardly any cross-sectional momentum in the cryptocurrency market. To explore the momentum anomaly additionally in the cryptocurrency market, this paper implemented a time-series momentum on cross-sectional winners for improvement. Previous studies have introduced detecting the turning point between long-term slow time-series factor and short-term fast time-series factor contributes to predicting the trend well. Furthermore, a threshold decided by a certain machine learning model suggests better performance. In this paper. A multilayer perceptron (MLP) is utilized to learn the weights of time-series factors. The combination of cross-sectional momentum and time-series momentum shows advantages and the MLP learned weighted strategy is preferable.
This paper studies the optimal liquidation of stocks in the presence of temporary and permanent price impacts, and we focus in the case of cryptocurrencies. We start by presenting analytical solutions to the problem with linear temporary impact, and linear and quadratic permanent impact. Then, using data from the order book of the BNB cryptocurrency, we estimate the functional form of the temporary and permanent price impact in three different scenarios: underestimation, overestimation and average estimation, finding different functional forms for each scenario. Using finite differences and optimal policy iteration, we solve the problem numerically and observe interesting changes in the optimal liquidation policy when applying calibrated linear and power forms for the temporary and permanent price impacts. Then, with these optimal policies, we identify optimal liquidation trajectories and simulate the liquidation of initial inventories to compare the performance among the optimal strategies under different parametrizations and against a naive strategy. Finally, we characterize the optimal policies based on the functional form of the inventory and find that policies generating the highest revenue are those starting with a low trading rate and increasing it as time passes.
The purpose of this study is to examine the efficacy of an online stock trading platform in enhancing the financial literacy of those with limited financial knowledge. To this end, an intelligent system is proposed which utilizes social media sentiment analysis, price tracker systems, and machine learning techniques to generate cryptocurrency trading signals. The system includes a live price visualization component for displaying cryptocurrency price data and a prediction function that provides both short-term and long-term trading signals based on the sentiment score of the previous day’s cryptocurrency tweets. Additionally, a method for refining the sentiment model result is outlined. The results illustrate that it is feasible to incorporate the Tweets sentiment of cryptocurrencies into the system for generating reliable trading signals.
This paper investigates the impact of COVID-19 on the cryptocurrency market. It empirically examines the level of volatility and the dynamic conditional correlations among cryptocurrencies pre-COVID-19 and during COVID-19. We find significant dynamic conditional correlations among cryptocurrencies and that the level of volatility is higher during COVID-19 than pre-COVID-19.
The cryptocurrency market is characterized by extremely high volatility. In the present study, we show the predictive ability of conditional EVT models in the cryptocurrency market during the price upsurge of 2020–2021. Taking high-frequency intraday data of four popular cryptocurrencies, Bitcoin, Ethereum, Litecoin, and Binance coin, we compare the accuracy of different competing models in estimating intraday value at risk (VaR) and expected shortfall (ES). The present study focuses on the extreme value theory (EVT) for modeling the tail of the distribution to forecast the measures of intraday VaR and ES. The study confirms the fat-tailed behavior of intraday returns of all four cryptocurrencies. Further, the study shows the magnitudes of high negative shocks are more than the positive ones for the returns of all four cryptocurrencies. The study uses suitable GARCH-family models such as apARCH, EGARCH, and CGARCH in the ARMA-GARCH framework. Using a two-stage approach the study shows how GARCH-EVT models with skewed student’s— t distribution outperform the predictability of conditional EVT with standard normal distribution as well as the unconditional EVT models in predicting intraday VaR and ES. The result of the study is useful for risk managers, day traders, and also for machine-based algorithmic trading.
Russia massively invaded Ukraine on February 24, 2022, unavoidably having an effect on the world economy and finance. This paper uses the event study to research the short-term response of the February 2022 top 5 variable-price cryptocurrencies (BTC, ETH, BNB, XRP, SOL) to the Russia-Ukrainian war under the constant mean model. The cryptocurrency volatility was dramatic during the event window, and cryptocurrencies did not show the characteristics of safe haven. Overall, the result of the effect of the Russia-Ukraine war on the cryptocurrency market was negative, with the least negative impact on SOL and the most negative impact on BNB, XRP. Finally, Using the different event window analysis, it shows the cryptocurrency market return volatility rebounded, but it does not sufficiently indicate there is a positive trend in the cryptocurrency market after the event. The analysis of this paper can provide some help for cryptocurrency investors in the event of unforeseen circumstances. And in the data selection, this paper doesn’t consider stablecoins.
<p>The popularity of cryptocurrencies has grown significantly in recent years, and they have become an important asset for internet trading. One of the main drawbacks of cryptocurrencies is the high volatility and fluctuation in value. The value of cryptocurrencies can change rapidly and dramatically, making them a risky investment. Cryptocurrencies are largely unregulated, which can exacerbate their volatility. The high volatility of cryptocurrencies has also led to a speculative bubble, with many investors buying and selling cryptocurrencies based on short-term price fluctuations rather than their underlying values. Therefore, how to reduce the fluctuation risk introduced by exchanges, transform uncertain prices to deterministic value, and promote the benefits of decentralized finance are critical for the future development of cryptos and Web 3.0. </p> <p>To address the issues, this paper proposes a novel theory as Automatic Increase Market Systems (AIMS) for cryptos, which could potentially be designed to automatically adjust the value of a cryptocurrency helping to stabilize the price and increase its value over time in a deterministic manner. We build a crypto, WISH (https://wishbank.wtf), based on AIMS in order to demonstrate how the automatic increase market system would work in practice, and how it would influence the supply of the cryptocurrency in response to market demand and finally make itself to be a stable medium of exchange, ensuring that the AIMS is fair and transparent.</p>