In the rapidly evolving domain of digital finance, the interplay between cryptocurrencies and external variables such as financial and social media indicators warrants thorough examination. This investigation employs a novel, entropy-weighted Multiple Attribute Decision Making (MADM) model to decipher these intricate relationships. The study's foundation is an expansive dataset, meticulously compiled to encompass a broad spectrum of financial data alongside diverse social media indicators. Central to this analysis is the employment of the Stepwise Weight Assessment Ratio Analysis (SWARA) method, meticulously applied to ascertain the relative importance of various social media indicators. Complementing this, the Complex Proportional Assessment (COPRAS) methodology is adeptly utilized to derive utility functions for each cryptocurrency under scrutiny. The analytical prowess of neural network regressions is harnessed to delineate the influence exerted by a multitude of financial indicators on these utility functions. The findings of this research are pivotal in understanding the dynamics within the cryptocurrency market. Bitcoin and Ripple emerge as pivotal entities, primarily functioning as primary conduits for market shocks. In contrast, Ethereum is identified as a stabilizing force, predominantly absorbing such fluctuations. A nuanced aspect of this study is the differential impact of social media indicators on various cryptocurrencies. Bitcoin and Ethereum display a negative correlation with these indicators, suggesting a complex, possibly inverse relationship with social media dynamics. Conversely, Litecoin, Dogecoin, and Ripple exhibit a positive responsiveness, indicating a heightened susceptibility to social media attention, sentiment, and prevailing uncertainty.
The valuation of the cryptocurrency market surpassed three trillion dollars in 2022, underscoring the burgeoning interest in digital currencies and decentralized finance.In response, on June 7, 2022, a bipartisan initiative led to the introduction of the "Responsible Financial Innovation Act," positioning cryptocurrencies as commodities and designating the Commodity Futures Trading Commission as the primary regulatory authority for the cryptocurrency market.Intriguingly, a study by Kim et al. (2022, JABE & IJBR) utilized a non-Euclidean methodology, suggesting that cryptocurrencies, in terms of their price dynamics, resemble securities more than commodities.However, a critical assessment of Kim et al. (2022, IJBR) reveals a methodological gap: the non-Euclidean distances were employed to derive a Euclidean configuration of 28 asset classes via multi-dimensional scaling.This Euclidean structure was subsequently employed for asset class categorization using -means clustering.This approach, while acknowledging the non-Euclidean distances among the 28 asset classes, leverages a Euclidean embedding for classification.In contrast, our research employs data depth to categorize asset classes without resorting to Euclidean embedding.We compare our findings with those of Kim et al. (2022, JABE & IJBR) for a comprehensive understanding.
Georgiana Iulia LAZEA, Ovidiu-Constantin Bunget, Anca Diana SUMANARU
This article aims to provide a comparative analysis of cryptocurrencies and fiat money, in the context in which the former might be considered an alternative to the latter. Mainly, we perform a literature review and qualitative analysis of 64 articles from Web of Science Core Collection, published between 2017 and 2023, using as keywords âcryptocurrenciesâ and âfiat moneyâ. The information processing methodology involved presenting the data and information concisely, in order to gain a point of view on how crypto assets can be perceived in comparison with other financial assets. The results present the authorsâ conclusions regarding the economic differences and similarities between cryptocurrencies and traditional money. It also includes the limitations of the research and offers future directions for study.
Frequent price manipulation in the Bitcoin market will lead to market risk and seriously disrupt the financial order, but there is less research on its regulation. We address the Bitcoin price manipulation problem by building a regulatory game model. First, we study the price manipulation mechanism of the Bitcoin market based on behavioral finance and clarify the boundary conditions. Second, we introduce regulator constraints and establish a game model between the manipulator and the regulator. Further, through variable deconstruction, parameter verification, and simulation analysis, we explore how to achieve effective regulation of Bitcoin price manipulation. We find that the effective regulation of Bitcoin price manipulation can be achieved in three ways: (1) Adjust the penalty coefficient with a certain lower threshold so that the manipulator's expected return is negative; (2) Set the lowest possible price fluctuation standard while ensuring that it does not interfere with market-based transactions; (3) The simulation of price manipulation regulation is optimized and most efficiently controlled when the probability of investigation is dynamically adjusted by a concave function on the price fluctuation standard.
The objective of this paper is the construction of new indicators that can be useful to operate in the cryptocurrency market. These indicators are based on public data obtained from the blockchain network, specifically from the nodes that make up Bitcoin mining. Therefore, our analysis is unique to that network. The results obtained with numerical simulations of algorithmic trading and prediction via statistical models and Machine Learning demonstrate the importance of variables such as the hash rate, the difficulty of mining or the cost per transaction when it comes to trade Bitcoin assets or predict the direction of price. Variables obtained from the blockchain network will be called here blockchain metrics. The corresponding indicators (inspired by the âHash Ribbonâ) perform well in locating buy signals. From our results, we conclude that such blockchain indicators allow obtaining information with a statistical advantage in the highly volatile cryptocurrency market.
This study employed variable moving average (VMA) trading rules and heatmap visualization because the flexibility advantage of the VMA technique and the presentation of numerous outcomes using the heatmap visualization technique may not have been thoroughly considered in prior financial research. We not only employ multiple VMA trading rules in trading crypto futures but also present our overall results through heatmap visualization, which will aid investors in selecting an appropriate VMA trading rule, thereby likely generating profits after screening the results generated from various VMA trading rules. Unexpectedly, we demonstrate in this study that our results may impress Ethereum futures traders by disclosing a heatmap matrix that displays multiple geometric average returns (GARs) exceeding 40%, in accordance with various VMA trading rules. Thus, we argue that this study extracted the diverse trading performance of various VMA trading rules, utilized a big data analytics technique for knowledge extraction to observe and evaluate numerous results via heatmap visualization, and then employed this knowledge for investments, thereby contributing to the extant literature. Consequently, this study may cast light on the significance of decision making via big data analytics.
In the realm of financial markets, the manifestation of volatility clustering serves as a pivotal element, indicative of the inherent fluctuations characterizing financial instruments. This attribute acquires pronounced relevance within the sphere of cryptocurrencies, a sector renowned for its elevated risk profile. The present analysis, conducted through the Autoregressive Moving Average - Generalized Autoregressive Conditional Heteroskedasticity (ARMA-GARCH) model, seeks to elucidate the enduring nature of volatility clustering and the occurrence of leverage effects within this domain. Over the course of a four-year time frame, it was observed that Bitcoin diverges from the anticipated Autoregressive Conditional Heteroskedasticity (ARCH) effects, in contrast to Ethereum and Cardano, which exhibit marked volatility clustering. Binance Coin, Ripple, and Dogecoin, whilst demonstrating moderate clustering, uniformly reflect the existence of leverage effects. An exception to this pattern was identified in Ripple, where it was discerned that positive market news exerts a disproportionate influence on log returns. The findings of this study illuminate the critical influence of both leverage effects and volatility clustering on the pricing dynamics of cryptocurrencies. It underscores the imperative for a nuanced comprehension of risk management in the context of cryptocurrency investments, given their susceptibility to abrupt price fluctuations. The distinct degrees to which these phenomena are manifested across diverse cryptocurrencies accentuate the necessity for a tailored risk management approach, resonant with the unique attributes of the asset in question. Such strategies, accounting for the potential amplification of losses through leverage, may encompass prudent position sizing, portfolio diversification, and the implementation of stress tests, thereby fortifying the investment against the dual perils of volatility clustering and leverage effects. The implications of this analysis serve to inform investors, providing a foundation upon which to construct risk management tactics that are responsive to the idiosyncrasies of the cryptocurrency market.
Patrice Racine Diallo, Bakhtiyar Garayev, Ăzlem Sayılır, Muhammed Chelery Komath
Herding behavior is expected to intensify with increasing uncertainty in financial markets, especially after jarring structural changes such as thepandemic. For this reason, in this study we examined herding behavior in the cryptocurrency market amid market crashes. Using daily cryptocurrency price data in 02.01.2018 - 22.03.2023 of the 9 most traded cryptocurrencies, the presence of herding behavior was investigated using the cross-sectional absolute deviation (CSAD) method. The dynamics of herding behavior was explored in 3 sub-periods: the Pre-Covid Period, the Covid-19 Period and the Post Market Crash Period after Teslaâs Announcement. We also tested if the largest cryptocurrencies were driving small cryptocurrencies in the 3 sub-periods as well as the whole period. The findings of the study reveal that there is no herding in the market in significant market fluctuations. Moreover, there seems to be no asymmetric herding behavior as we distinguish between up and down markets. Hence, our findings imply rational investment decision-making. Yet, the results indicate that the largest cryptocurrencies are wielding a substantial influence over the rest of the market across the overall period and in the Post Covid Period (Period 2) in both up and down markets. However, in Period 1 and Period 3, the herding of small cryptocurrencies varies depending on whether the market returns are positive or negative. Our findings imply that the dynamics of the herding behavior between large and small cryptocurrencies has shifted with significant market crashes (outbreak of Covid-19, Teslaâs announcement).
The issue related to the quantification of the tail risk of cryptocurrencies is considered in this paper. The statistical methods used in the study are those concerning recent developments in Extreme Value Theory (EVT) for weakly dependent data. This research proposes an expectile-based approach for assessing the tail risk of dependent data. Expectile is a summary statistic that generalizes the concept of mean, as the quantile generalizes the concept of the median. We present the empirical findings for a dataset of cryptocurrencies. We propose a method for dynamically evaluating the level of the expectiles by estimating the level of the expectiles of the residuals of a heteroscedastic regression, such as a GARCH model. Finally, we introduce the Marginal Expected Shortfall (MES) as a tool for measuring the marginal impact of single assets on systemic shortfalls. In our case of interest, we are focused on the impact of a single cryptocurrency on the systemic risk of the whole cryptocurrency market. In particular, we present an expectile-based MES for dependent data.
The rapid proliferation of digital assets and the emergence of Central Bank Digital Currencies (CBDCs) are reshaping the global financial landscape, with significant implications for cross-border capital flows and the stability of capital markets. This review paper explores the dynamics of cross-border digital asset movements, analyzing how decentralized finance (DeFi), stable coins, and CBDCs influence liquidity, market volatility, and regulatory oversight. It investigates the potential risks posed by CBDCs to financial stability, including currency substitution, capital flight, and systemic vulnerabilities in interconnected markets. Furthermore, the paper assesses the readiness of global regulatory frameworks to address these challenges and examines the roles of interoperability, digital identity verification, and cross-jurisdictional cooperation in mitigating associated risks. Drawing from recent developments, policy reports, and empirical studies, this review provides a comprehensive analysis of how digital transformation in finance may disrupt traditional monetary mechanisms and market structures. It concludes by offering policy recommendations for ensuring resilient capital markets amid evolving digital asset ecosystems and central bank innovations.
We investigate the benefits of using intraday realized volatility (RV) commonality, and propose a novel non-parametric framework for forecasting one-day ahead intraday RV (1D-ahead intraday RV). Specifically, we train multiple models using machine learning (ML) techniques under various training settings (single-asset, cluster-driven, and cross-asset), where commonality gradually enters model dynamics as training schemes become more complex. We conclude that models that leverage the cryptocurrency commonality outperform models that do not explicitly account for it, regardless of the market regime considered. The source code of this project is available at: github.com/edjanga/crypto_volatility_commonality.
Vincent Gurgul, Stefan Lessmann, Wolfgang Karl HĂ€rdle
We introduce novel approaches to cryptocurrency price forecasting, leveraging Machine Learning (ML) and Natural Language Processing (NLP) techniques, with a focus on Bitcoin and Ethereum. By analysing news and social media content, primarily from Twitter and Reddit, we assess the impact of public sentiment on cryptocurrency markets. A distinctive feature of our methodology is the application of the BART MNLI zero-shot classification model to detect bullish and bearish trends, significantly advancing beyond traditional sentiment analysis. Additionally, we systematically compare a range of pre-trained and fine-tuned deep learning NLP models against conventional dictionary-based sentiment analysis methods. Another key contribution of our work is the adoption of local extrema alongside daily price movements as predictive targets, reducing trading frequency and portfolio volatility. Our findings demonstrate that integrating textual data into cryptocurrency price forecasting not only improves forecasting accuracy but also consistently enhances the profitability and Sharpe ratio across various validation scenarios, particularly when applying deep learning NLP techniques. The entire codebase of our experiments is available via an online repository: https://anonymous.4open.science/r/crypto-forecasting-public . âą NLP data from social media improve the accuracy of cryptocurrency forecasting models. âą As a target variable, local extrema are a valid alternative to daily price changes. âą Deep learning language models substantially outperform dictionary-based methodologies. âą Both pre-trained and fine-tuned language models effectively quantify market sentiment.
The authors test the weak-form efficiency in cryptocurrency markets using the most recent and comprehensive data as of 2021. The authors apply various technical indicators to take a long or short position on 99 cryptocurrencies and compare the 10-day returns based on the technical trading strategies to the simple buy-and-hold returns. The authors find that the trading strategies based on single indicators or the combination of two indicators do not generate higher returns than buy-and-hold returns among cryptos. These findings suggest that cryptocurrency markets are weak-form efficient in general.
We analyse the pattern of daily price of a collection of artistic non-fungible tokens, namely, the "Bored Ape Yacht Club" (BAYC) collectibles, over the first year of their life, from May 2021 to May 2022. Taking a time-series analysis approach, we consider the daily average price, and other variants of daily price index, derived from hedonic regression model. Aesthetic features of the collectibles do matter. At the same time, the price series emerge to be non-stationary, integrated of order 1, with their first difference exhibiting heteroscedasticity and autoregressive variance. Models of ARCH/GARCH class are appropriate to describe the dynamics. Though the price series of BAYC collectibles and their daily movements share many characteristics with the series of financial assets, they do not appear to be related to financial variables from both the crypto- and the real (i.e., not crypto) world.
Kushal Babel, Mojan Javaheripi, Yan Ji, Mahimna Kelkar · 6 authors
We introduce Lanturn: a general purpose adaptive learning-based framework for measuring the cryptoeconomic security of composed decentralized-finance (DeFi) smart contracts. Lanturn discovers strategies comprising of concrete transactions for extracting economic value from smart contracts interacting with a particular transaction environment. We formulate the strategy discovery as a black-box optimization problem and leverage a novel adaptive learning-based algorithm to address it.
This research investigates the function of price discovery between the Bitcoin futures and the spot markets while also analyzing the impact of investor sentiment and attention on these markets. This study utilizes various statistical models to examine the short-term and long-term relations between these variables, including the bivariate Granger causality model, the ARDL and NARDL models, and the Johansen cointegration procedure with a vector error correction mechanism. The results suggest that there is no statistical evidence of price discovery between the Bitcoin spot price and futures, and the term structure of the Bitcoin futures neither enriches nor impairs this lead lag relation. However, the study finds robust evidence of a long-run cointegrating relation between the two markets and the presence of asymmetry in them. Moreover, this research indicates that investor sentiment exhibits a lead lag relation with both the Bitcoin futures and the spot markets, while investor attention only leads to the Bitcoin spot market, without showing any lead lag relation with the Bitcoin futures. These findings highlight the crucial role of investor behavior in affecting both Bitcoin futures and spot prices.
The speculative financial services sector and the systemic failures of financial globalisation have prompted a growing call for delinking from the hegemony of the US dollar. These systemic failures have been illustrated by financial mismanagement and the financial crisis of 2007-8. This event paints a dire picture of the consequences associated with poor regulation of the financial sector. This study seeks to interpret these events in terms of Gramsciâs Prison Notebooks and his concept of the âmorbid symptoms of the interregnumâ. According to Babic (2020), these occur when a hegemony and its institutions are âdyingâ, thereby hampering their power. This notion also highlights that the future remains murky, despite public calls for change. Gramsci calls this the ânew that cannot be bornâ.This study seeks to extend this framework to the current state of the dollar hegemony. The âmorbid symptomsâ we will examine include the call to delink from the speculative high-risk US economy, the emergence of cryptocurrencies, and the politics surrounding fiat money. Some have argued that the emergence of cryptocurrencies could be the ânewâ. However, this study argues that the negative characteristics associated with cryptocurrencies such as cybersecurity concerns and price volatility create ambiguity about whether cryptocurrency is the ideal trajectory. Therefore, it argues that ambiguity and calls for change qualify our current monetary situation to be classified as a Gramscian interregnum.It also examines the current fiat money discourse by highlighting how the dollar hegemony may be succeeded by another fiat currency. However, it argues that the inflationary shortfalls of fiat money make this a fallible option, thereby perpetuating the ambiguities within the Gramscian interregnum.
An Pham Ngoc Nguyen, Tai Tan, Marija Bezbradica, Martin Crane
We employ graph-based methods to examine the connectedness between cryptocurrencies of different market caps over time. By applying denoising and detrending techniques inherited from Random Matrix Theory and the concept of the so-called Market Component, we are able to extract new insights from historical return and volatility time series. Notably, our analysis reveals that changes in volatility-based network structure can be used to identify major events that have, in turn, impacted the cryptocurrency market. Additionally, we find that these structures reflect investorsâ sentiments, including emotions like fear and greed. Using metrics such as PageRank, we discover that certain minor coins unexpectedly exert a disproportionate influence on the market, while the largest cryptocurrencies such as BTC and ETH seem less influential. We suggest that our findings have practical implications for investors in different ways: Firstly, helping them to avoid major market disruptions such as crashes, to safeguard their investments, and to capitalize on opportunities for high returns; Secondly, sharpening and optimizing the portfolios thanks to the understanding of cryptocurrenciesâ connectedness.
Abstract This study employs the Bayesian Networks (BN) and the wavelet coherence approaches to invest the relationship between Bitcoin volatility and financial asset classes (MSCI world equity index, S&P Goldman Sachs Commodity Index [GSCI], US index and Investment Grade Corporate Bond Index ETF [PIMCO]) using daily data for the period from August 2011 to October 2021. The results show that the causal relationship between Bitcoin and other financial assets varies depending on the market states. During the low volatility periods, Bitcoin has a stronger impact on the GSCI, while during the stability periods, it has a direct effect on the US index and the MSCI world index. In contrast, during high volatility periods, Bitcoin has a direct impact on both the GSCI and PIMCO indices. The key findings enabled us to provide implications for US investors to promote asset allocation and risk management covering both Bitcoin and traditional financial markets. The results suggest that policymakers should watch Botcoin closely to preserve financial stability.
PaweĆ SzydĆo, Marcin WÄ torek, JarosĆaw KwapieĆ, StanisĆaw DroĆŒdĆŒ
A non-fungible token (NFT) market is a new trading invention based on the blockchain technology, which parallels the cryptocurrency market. In the present work, we study capitalization, floor price, the number of transactions, the inter-transaction times, and the transaction volume value of a few selected popular token collections. The results show that the fluctuations of all these quantities are characterized by heavy-tailed probability distribution functions, in most cases well described by the stretched exponentials, with a trace of power-law scaling at times, long-range memory, persistence, and in several cases even the fractal organization of fluctuations, mostly restricted to the larger fluctuations, however. We conclude that the NFT market-even though young and governed by somewhat different mechanisms of trading-shares several statistical properties with the regular financial markets. However, some differences are visible in the specific quantitative indicators.
This paper investigates the persistence in the cryptocurrency market, focusing on five distinct groups categorized by their market capitalization during the sample period from 2020 to 2023. The study aims to test two hypotheses: (H1) The degree of persistence in the cryptocurrency market is contingent on market capitalization, and (H2) The efficiency of the cryptocurrency market has increased in recent years. The methodology employed for this examination is R/S analysis. The results indicate that the cryptocurrency market maintains its inefficiency, and no significant variations in persistence are discerned among different cryptocurrency groups, leading to the rejection of H1. Outcomes related to H2 present a nuanced scenario. Specifically, Litecoin and Ripple exhibit supportive evidence for the Adaptive Market Hypothesis, suggesting an improvement in the efficiency of the cryptocurrency market in recent years. A noteworthy revelation pertains to the anomaly observed in Bitcoin. Despite being the most capitalized and liquid cryptocurrency, it demonstrates inefficiency akin to levels observed five years ago. The implications of this study contribute to the comprehension of cryptocurrency market efficiency. The findings challenge the assumptions of the Efficient Market Hypothesis, favoring instead the Adaptive Market Hypothesis. For practitioners, the results hold significance, providing evidence of price predictability, particularly in the case of Bitcoin. This suggests that trend trading strategies remain viable for generating abnormal profits in the cryptocurrency market. Acknowledgments Alex Plastun gratefully acknowledges financial support from the Ministry of Education and Science of Ukraine (0121U100473).
This paper proposes a new investment strategy in the cryptocurrency market based on a two-step procedure. The first step is the computation of the asset's levels of efficiency in an universe of cryptocurrencies. Price returns efficiency degrees are measured by their corresponding levels of multifractality, obtained by the multifractal detrended fluctuation analysis method. The higher the multifractality, the higher the inefficiency in terms of the weak form of market efficiency. Cryptocurrencies are then ranked in terms of efficiency. The second step is the construction of portfolios under the Markowitz framework composed of the most/least efficient digital coins. Minimum variance, maximum Sharpe ratio, equally weighted and (in)efficient-based portfolios were considered. The former strategy is also proposed, where the weights are computed proportionally to the assets levels of (in)efficiency. The main findings are: cryptocurrency price returns are multifractal and their levels of (in)efficiency change over time; returns exhibit left-sided asymmetry, which implies that subsets of large fluctuations contribute substantially to the multifractal spectrum; in bull markets portfolios with the least efficiency assets provided a better riskâreturn relation; in periods of high volatility and high price depreciation (bear market) a better performance is associated with the portfolios composed by the more efficient cryptocurrencies.
This paper reports our findings on the return dynamics of Bitcoin and Ethereum using high-frequency data (minute-by-minute observations) from 2015 to 2022 for Bitcoin and from 2016 to 2022 for Ethereum. The main objective of modeling these two series was to obtain a dynamic estimation of risk premium with the intention of characterizing its behavior. To this end, we estimated the Generalized Autoregressive Conditional Heteroskedasticity in Mean with Normal-Inverse Gaussian distribution (GARCH-M-NIG) model for the residuals. We also estimated the other parameters of the model and discussed their evolution over time, including the skewness and kurtosis of the Normal-Inverse Gaussian distribution. Similarly, we determined the parameters that define the evolution of the estimated variance, i.e., the parameters related to the fitted past variance, square error and long-term average value. We found that, despite the market uncertainty during the COVID-19 emergency period (2020 and 2021), the selected cryptocurrenciesâ return volatility and kurtosis were even greater for several other subperiods within our sampleâs time frame. Our model represents an analytical tool that estimates the risk premium that should be delivered by Bitcoin and Ethereum and is therefore of interest to risk managers, traders and investors.
Given that technical trading charts are publicly available on popular financial websites such as Bloomberg and MarketWatch, it stands to reason that the same technical trading approaches may be applied to cryptocurrency markets. One of these trading strategies is the variable length moving average (VMA), whose flexibility benefit has not been fully explored in prior research. To fill this gap, we evaluate Bitcoin futures using VMA trading rules and provide the results in a heatmap diagram. This approach allows investors to choose the most effective VMA rules, potentially leading to profits. Furthermore, our approach may shed new light on previously unexplored investment thinking and practices that have the potential to improve investment outcomes.