Using daily returns on large-cap altcoins, this paper uses power-law functions to model cryptocurrency-specific exposure to events exhibiting potentially large standard deviations. Since our analysis provides evidence for power-law behavior in the returns on cryptocurrencies, co-fractality analysis is employed to explore potential co-dependencies in the heavy-tailed part of return distributions. The findings indicate that the potential arrival of events exhibiting large standard deviations in Bitcoin returns can hardly be diversified using other sample altcoins. Other altcoins exhibit very similar features in terms of co-dependencies. Further results show that co-fractal behavior is not specific to any subsample.
The main aim of the study will be to identify the major factors affecting the market price of crypto currencies and to analyze the nature of the crypto currency market from a global vs. Indian perspective. In India, crypto currency trading is not yet fully developed, and it has a history of being used to finance illicit operations from the start. Due to significant volatility and unlawful trading, India is considering bringing crypto currency trading under the jurisdiction of SEBI. Due to a high increase in interest in crypto currencies such as Bitcoin, there is a real concern about the ability of this currency to replace the monetary system by overcoming the issues related to it. Several aspects need to be considered to estimate the ability of replacement. The study will take into consideration five main cryptographic currencies. According to Coin MarketCap, the top five crypto currencies by market capitalization as of August 2023 are Bitcoin (BTC), Ethereum (ETH), Tether (USDT), Binance Coin (BNB), and Cardano (ADA).
Sonal Sahu, José Hugo Ochoa Vázquez, Alejandro Fonseca Ramírez, Jong‐Min Kim
This paper investigates portfolio optimization methodologies and short-term investment strategies in the context of the cryptocurrency market, focusing on ten major cryptocurrencies from June 2020 to March 2024. Using hourly data, we apply the Kurtosis Minimization methodology, along with other optimization strategies, to construct and assess portfolios across various rebalancing frequencies. Our empirical analysis reveals significant volatility, skewness, and kurtosis in cryptocurrencies, highlighting the need for sophisticated portfolio management techniques. We discover that the Kurtosis Minimization methodology consistently outperforms other optimization strategies, especially in shorter-term investment horizons, delivering optimal returns to investors. Additionally, our findings emphasize the importance of dynamic portfolio management, stressing the necessity of regular rebalancing in the volatile cryptocurrency market. Overall, this study offers valuable insights into optimizing cryptocurrency portfolios, providing practical guidance for investors and portfolio managers navigating this rapidly evolving market landscape.
Inderpreet Kaur, Yashica Chauhan, Utsav Gupta, Sagar Malik
CryptoBot is a groundbreaking automated cryptocurrency trading system that answers the issues faced by traders in an environment where market dynamics change swiftly. Hence, CryptoBot employs a holistic approach of data gathering, preprocessing, predictive modelling, and real-time decision-making, thus it keeps analyzing real-time market data. The system then uses state of the art machine learning algorithms to make accurate predictions about price movements and determine when to enter and exit trades. This program also shows very promising results indicating a significant improvement in trading performance compared to traditional human-based strategies. CryptoBot therefore becomes an instrument of innovation for both experienced and non-experienced cryptocurrency investors. Thus, with its flexibility, precision, and smart automation users become more competitive reshaping how the investor interacts with the volatile but lucrative world of cryptocurrencies. As digital assets continue to reshape finance, CryptoBot stands at the forefront, exemplifying the future of intelligent trading.
We use the internal trading records of a major Bitcoin exchange leaked by hackers to detect and characterize wash trading—a type of market manipulation in which a single trader clears the trader’s own limit orders to “cook” transaction records. Our finding provides direct evidence for the widely suspected “fake volume” allegation against cryptocurrency exchanges, which has so far only been backed by indirect estimation. We then use our direct evidence to evaluate various indirect techniques for detecting the presence of wash trades and find measures based on Benford’s law, trade size clustering, lognormal distributions, and structural breaks to be useful, whereas ones based on power law tail distributions to give opposite conclusions. We also provide suggestions to effectively apply various indirect estimation techniques. This paper was accepted by Professor Bruno Biais, finance. Funding: J. Li acknowledges support by the U.S. Department of Homeland Security [Grant 205187] through the Criminal Investigations and Network Analysis Center. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2021.01448 .
In the current study, the immediate correlation coefficient and root mean square error (RMSE) are combined to create a fusion model that can accurately predict cryptocurrency prices. Multivariate linear regression, MARS, artificial neural networks (ANN), random forests, support vector machines (SVM), bootstrap aggregation, decision trees, and extreme gradient boosting with XG Boost are just a few of the deep learning and machine learning models that we use. Utilizing long short-term memory (LSTM) is a crucial element. LSTM emerges as the most accurate model for predicting cryptocurrency prices during January 1, 2023, to March 31, 2023.
Abstract Distributed ledger technology (DLT) has become a vital technological tool for digital transformation worldwide. This chapter examines the regulation of DLT in the context of European capital markets law and its definition as a security. It examines whether DLT is already regulated as a security in the traditional sense and finds that the current legal situation is sufficient. However, a definition of DLT in the context of Article 4 MiFID II would be helpful. The chapter also analyses the forthcoming EU MiCA regulation and its impact on DLT in the context of securities regulation and its relationship to current securities legislation. It provides a brief comparative overview of various approaches to regulating DLT and shows that a technology-neutral approach is preferred, even if DLT has often inspired the formulation of a universal definition. It argues that the definition of DLT in a European Capital Markets code should be separated from the definition of traditional securities and should be open to new technologies. Overall, the chapter emphasizes the importance of developing a flexible and adaptable definition of DLT that can keep pace with rapidly evolving technologies.
Maximal Extractable Value (MEV) refers to a maximum value block producers can extract from an Ethereum block by reordering, inserting, or censoring transactions. Pair trading is an investment strategy which involves identifying two closely related assets and taking simultaneous long and short positions in them. This strategy aims to generate returns by exploiting price disparities between the two assets, regardless of the broader market’s direction. The purpose of this study is to examine whether pair trading opportunities can be used as a Maximal Extractable Value extraction method on Ethereum Network. The study expands on the existing literature on Maximal Extractable Value by analyzing new kind of extraction method. This study analyzes decentralized exchange transactions between September 2022 and September 2023 to determine the applicability of pair trading logic as a Maximal Extractable Value extraction method. Pair trading opportunities are identified from the transactions following methodology of pair trading literature and pair trading strategy is found to be profitable over a long period, while individual opportunities tend to make a loss. The strategy is also found to remain profitable during the collapse of FTX, bankruptcy of Silicon Valley Bank, exploitation of Curve which were periods of increased market distress. The majority of the opportunities are relatively time sensitive with the median entry window for positions lasting for slightly over 10 minutes, and the median position must be held being 16 minutes before the position reverts or diverges. The study does not find unambiguous evidence of pair trading opportunities’ characteristics changing under increased market distress. Similarly, the study does not find competition among MEV searchers for the pair trading opportunities having increasing or decreasing trend during the observation period.
Richard Harris, Murat Mazibaş, Dooruj Rambaccussing
Cryptocurrencies are characterized by high volatility and low correlations with traditional asset classes, and present an intriguing investment opportunity. However, their inherent risks and regulatory uncertainties make direct investment challenging for many investors. This paper addresses this challenge by proposing a replication framework that employs machine learning to create synthetic portfolios that replicate the risk-adjusted return profile and diversification benefits of Bitcoin, by far the largest cryptocurrency by market share. We show that the synthetic portfolios offer a compelling alternative to direct investment in Bitcoin, delivering superior risk-adjusted returns net of trading costs while mitigating the risks that are associated with holding Bitcoin directly. Furthermore, the synthetic portfolios provide better diversification benefits and lower tail risk.
Suwan Long, Ioannis Chatziantoniou, David Gabauer, Brian M. Lucey
In this paper, we investigate interdependencies between cryptocurrencies and investor sentiment by introducing the asymmetric TVP-VAR frequency connectedness approach. Our empirical results provide evidence of pronounced and time-varying interconnectedness between sentiment and cryptocurrency. In addition, we find that negative short-term interconnectedness dominates positive short-term interconnectedness until mid-2020 when this effect was reversed, persisting until the end of the sample period. While Ripple and Bitcoin are both found to be the main net transmitters of shocks, Stellar, Ethereum, and NEM are considered net receivers of shocks. Overall, our findings suggest that market sentiment is mainly driven by cryptocurrency volatility in both the short and the long run.
Erdinç Akyıldırım, Ahmet Faruk Aysan, Oğuzhan Çepni, Özge Serbest
This paper explores the relationship between news media sentiment and spillover effects in the cryptocurrency market. By employing a time-varying parameter vector autoregressive model, we initially develop measures of spillover specific to individual cryptocurrencies. Subsequently, we employ unique data on cryptocurrency-specific sentiment to assess its impact on these spillover measures using panel fixed effects regression analysis. Our findings indicate that news media sentiment plays a significant role in explaining the spillover dynamics within the cryptocurrency market. Unlike traditional assets, it appears that only positive sentiment affects the spillovers among cryptocurrencies, suggesting an asymmetric effect. Taking into account various characteristics of cryptocurrencies, we find that sentiment's impact on spillover is more pronounced in community-based coins than in those driven by firms. An examination of news content suggests that sentiment pertaining to emotional and risk aspects of cryptocurrencies predominantly influences these spillovers. Additionally, a comparative analysis of sentiment derived from social media and traditional news sources reveals a stronger influence of the former on spillover effects. Through extensive robustness checks, our research consistently affirms the pivotal role of sentiment in driving spillovers among cryptocurrency returns, underlining the importance of sentiment analysis in understanding the dynamics of the cryptocurrency market.
This research aims to empirically investigate the portfolio risk associated with crypto assets. In other words, we want to investigate whether the inclusion of crypto assets in a portfolio can minimize the portfolio risk or not, because it is argued that there is a lower degree of correlation between crypto assets and traditional assets. In order to achieve our research objectives, we employ the Vector Autoregressive Model (VAR) by using five different asset classes. The first two variables are taken from the crypto assets, Bitcoin and Ethereum, and the remaining three variables for Gold, Crude Oil and VIX (Chicago Board Options Exchange's (CBOE) volatility index). Our research strategy will be based on an analysis for unit root, optimal lag selection, coefficient matrix, checking VAR stability, the Granger causality test, and impulse response function (IRF). Our findings suggest that none of the indicators of traditional assets drive and explain Bitcoin. We also found that only Bitcoin is significantly related to Ethereum. while none of the other variables are statistically useful to explain the variation in the Ethereum. Based on these findings it can be recommended that the inclusion of crypto assets into a portfolio reduces risk because none of the indicators of crypto assets are significantly related to the indicators of traditional assets.
In the dynamic world of finance, the application of Artificial Intelligence (AI) in pair trading strategies is gaining significant interest among scholars. Current AI research largely concentrates on regression analyses of prices or spreads between paired assets for formulating trading strategies. However, AI models typically exhibit less precision in regression tasks compared to classification tasks, presenting a challenge in refining the accuracy of pair trading strategies. In pursuit of high-performance labels to elevate the precision of classification models, this study advanced the Triple Barrier Labeling Method for enhanced compatibility with pair trading strategies. This refinement enables the creation of diverse label sets, each tailored to distinct barrier configurations. Focusing on achieving maximal profit or minimizing the Maximum Drawdown (MDD), Genetic Algorithms (GAs) were employed for the optimization of these labels. After optimization, the labels were classified into two distinct types: High Risk and High Profit (HRHP) and Low Risk and Low Profit (LRLP). These labels then serve as the foundation for training machine learning models, which are designed to predict future trading activities in the cryptocurrency market. Our approach, employing cryptocurrency price data from 9 November 2017 to 31 August 2022 for training and 1 September 2022 to 1 December 2023 for testing, demonstrates a substantial improvement over traditional pair trading strategies. In particular, models trained with HRHP signals realized a 51.42% surge in profitability, while those trained with LRLP signals significantly mitigated risk, marked by a 73.24% reduction in the MDD. This innovative method marks a significant advancement in cryptocurrency pair trading strategies, offering traders a powerful and refined tool for optimizing their trading decisions.
Portfolio optimization is the art and science of constructing investment portfolios to strike a balance between risk and return. Traditional models, like Modern Portfolio Theory (MPT) and the Capital Asset Pricing Model (CAPM), have long served as the foundation for portfolio management. However, these methods often struggle to account for the intricacies of real financial markets. This study explores cutting-edge portfolio optimization techniques, incorporating unconventional assets such as cryptocurrencies and ESG investments to bolster diversification. Leveraging machine learning and artificial intelligence, we aim to improve asset selection, risk assessment, and allocation, accommodating the dynamic and non-linear nature of markets. Furthermore, we evaluate how these models perform in various market conditions through empirical analyses of historical data. Our findings indicate that adopting a more adaptable portfolio optimization framework can help investors navigate changing market dynamics more effectively, ultimately achieving a more efficient risk-return trade-off. These insights are invaluable for both individual and institutional investors, enabling them to construct portfolios that adapt to evolving market realities while optimizing wealth preservation and growth. In essence, this research contributes to the ongoing discourse on portfolio optimization, offering potential enhancements for investment strategies in today's financial landscape.
The study investigates the predictability of both the individual and basket of 10 major cryptocurrencies’ daily price changes between 2017 and 2023 by employing various machine learning classification algorithms such as random forests, k-nearest neighbour, decision trees, logistic regression, and Bernoulli naïve Bayes. These models utilize 15 different features based on historical price data and technical indicators as input features. The study estimates find logistic regression as superior over other models under consideration in predicting cryptocurrency daily returns. Overall, the study finds that on an average machine learning classification algorithms predictive accuracies have surpassed 50% when applied to daily frequencies on the basket of 10 major cryptocurrencies.
This research aims to examine the influence of cryptocurrencies on stock market prices in Indonesia. This research uses multiple regression analysis using daily tme series data from 2020-2022 so that the number of observations in this research is 1096. The findings in this research show mixed results between cryptocurrency assets and stock market prices in Indonesia. From the research results, Bitcoin does not have a significant influence on stock market prices in Indonesia, while Ethereum and Binance Coin have a positive and significant influence, but this is different from Maker and Pax Gold. Maker and Pax Gold have a negative and significant influence on stock market prices. The findings in this research show that the nature of the influence of cryptocurrency on stock market prices in Indonesia is not the same but depends on the cryptocurrency asset itself. The findings in this research suggest that investors and market players need to consider these two assets together in their investment strategies.
Sonal Sahu, José Hugo Ochoa Vázquez, Alejandro Fonseca Ramírez, Jong‐Min Kim
This paper investigates portfolio optimization methodologies and short-term investment strate-gies in the context of the cryptocurrency market, focusing on ten major cryptocurrencies from January 2020 to November 2023. We employ high frequency data and utilize the Kurtosis Mini-mization methodology, alongside other optimization strategies, to construct and evaluate port-folios under different rebalancing frequencies. The empirical analysis reveals that cryptocurren-cies exhibit significant volatility, skewness, and kurtosis, necessitating sophisticated portfolio management techniques. We find that the Kurtosis Minimization methodology consistently outperforms other optimization strategies, delivering optimal returns to investors, particularly in shorter-term investment horizons. We demonstrate the diversification benefits of integrating cryptocurrencies into multi-asset portfolios and emphasize the importance of regular portfolio rebalancing in the volatile cryptocurrency market. Our findings offer insights for portfolio man-agers and investors seeking to optimize their investment outcomes in the cryptocurrency market, highlighting the importance of risk management, diversification, and dynamic portfolio man-agement strategies.
In this paper, we describe and apply different models of portfolio construction in the selection between a small number of big-cap cryptocurrencies. Our purpose is to select the minimum riskiness between cryptocurrencies, comparing different risk measures and maximum diversification. We build our models without the constraints of the expected returns. Without relying on expected returns, we have the same condition on the comparison between them. Cryptocurrencies are not common stock or other assets indexed in the market but it is interesting to study how diversification can significantly improve investment performance. We first give the methodology to use high-frequency observation data, in the numeral approximation especially in the novel application of the Risk parity models, used with different risk measures we can achieve a very good result, from the position of gaining and variation. Since Risk parity models divide the weights of the asset in equal risk contribution proportion, it is suggested to use a small number of cryptocurrencies, otherwise their performance will be close to the uniform portfolio. To the traditional Mean Variance model, and the alternative, Expected shortfall/Conditional Value at Risk, we use three versions of Risk Parity with two different risk measures and a naive risk parity. The uniform portfolio is used as a benchmark for selection comparison with the other portfolio models. We give the conditions for the Risk Parity with the Expected shortfall/Conditional Value at Risk (CVaR) to guarantee convergence with the numerical approximation. In the end, we study the tradeoff between each model and which is more suitable for a small cryptocurrency portfolio.