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 “Hash Ribbon”) 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.
Valuation of Bitcoin, Ethereum, and other cryptoassets is a challenge for the investment industry. We review the tools available to value cryptoassets, and in doing so, we aim to help practitioners better understand the dynamics of cryptoassets.
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, Özlem 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 Tesla’s 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, Tesla’s 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.
This work introduces my second entropy measure, Ismail’s second entropy, namely $(H_{1}^{q})$ is a novel generalization to Shannonian entropy with a visionary link to both long- and short-range interactions, (LRIs), (SRIs) respectively. The fractal dimension of $H_{1}^{q}$ is identified in this paper. Following this, some potential fractal applications to ChatGPT, Distributed Ledger Technologies(DLTs), and Image Processing are highlighted. The paper ends with closing remarks combined with some challenging open problems and the next phase of research.
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 Härdle
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 . • NLP data from social media improve the accuracy of cryptocurrency forecasting models. • As a target variable, local extrema are a valid alternative to daily price changes. • Deep learning language models substantially outperform dictionary-based methodologies. • 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.
In the digital age, the growth of blockchain technology, underpinning cryptocurrency, has been undeniable since its debut with Bitcoin in 2009. While the cryptocurrency market offers vast profit potential, its price volatility poses a significant challenge to investors and traders. Addressing this challenge, this study proposes a prediction method that fuses SARIMAX and LSTM into a Hybrid SARIMAX-LSTM model, considering trading volume as an exogenous factor. Through evaluation metrics like MSE, RMSE, MAPE, and MAE, it was found that this hybrid model provides more accurate forecasts than singular models, showcasing its potential to counteract cryptocurrency market volatility..
Blockchain has become a revolutionary technology that has had a great impact on the business environment. Non-fungible tokens (NFTs) are distinct from fungible tokens traded on multiple centralized or decentralized exchanges. Automated Market-MMs (AMMs) are decentralized markets for crypto-tokens that offer users three core operations: deposition of crypto tokens to get AMM shares in return; the dual operation of getting shares for the base tokens; and swapping of two different tokens with each other. This research aims to put forth a comprehensive view of Blockchain and its applications in the real world, including cryptocurrencies, NFTs trading, voting, and much more. It also focuses on how NFTs are traded on different platforms and aims at better marketplaces for trading NFTs,namely Automated Market Makers on different blockchains like Ethereum and Tezos.
Christian Fieberg, Gerrit Liedtke, Daniel Metko, Adam Zaremba
Is there a momentum effect in cryptocurrency anomalies? To answer this, we analyze data from over 3900 coins spanning the years 2014 to 2022 and replicate 34 anomalies in the cross-section of cryptocurrency returns. We document a discernible pattern in factor premia: past winners consistently outperform losers. The effect persists across subperiods, withstands various methodological approaches, and its magnitude parallels that of its stock market counterpart. However, the autocorrelation in factor returns is not widespread and primarily stems from size and volatility anomalies. Additionally, unlike in stocks, cryptocurrency factor momentum originates from price momentum, which subsequently transfers to the factor level.
Abstract Without theoretically specifying the future of money as an equivalent commodity of other commodities, it is impossible to reveal the recent role of the emergence of cryptocurrencies, as a reflection of speculative competition increasingly sophisticated in its technological aspect and in response to the abusive use of the spurious competition of the big banks promoting the huge financial bubbles that have haunted the world economy, such as the one unleashed from Wall Street in 2008. The explosive growth of transactions in cryptocurrencies may mean, at some point, in the capitalist economic cycle, the possibility of a new financial bubble, as well as the emergence of new swindles to investors; but valid answers can also come from those actors who until now have had to endure the almost exclusive dominance of the international monetary system by the currency issued by the US government, the main exporter of inflation on a global scale.
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.