Blockchain Papers

Ying-Ho Liu, Jian-Kai Huang

No abstract is available for this record.

Avraham Turgeman, Octavian Jude

Abstract Cryptocurrencies have rapidly become popular as digital assets, and as the market evolves, it is of great importance to understand their volatility and risk behavior. They present specific challenges and opportunities given that are operating within a decentralized and fast-changing ecosystem. Thus, their volatility affects risk management, investment strategies, and market stability. Cryptocurrency volatility can create both opportunities and risks. While it can provide substantial returns, it also presents challenges in terms of investment strategy, regulatory frameworks, business operations, and economic stability. As the cryptocurrency market matures, it’s likely that solutions to manage volatility will evolve, but it remains a key concern for participants in the ecosystem. In this respect, the aim of the paper is to examine the volatility behavior of the main cryptocurrencies (Bitcoin, Ethereum, and Litecoin), for a recent period, i.e. from June 2018 to June 2023. Using both traditional and advanced GARCH models, the results show that these cryptocurrencies experience periods of high and low volatility, but there is no significant asymmetry effect in their responses. This suggests a balanced risk-return profile for investors. Furthermore, there is no evidence for risk premium within the sample, that is no link between risk and return. Additionally, past volatility has a greater impact on current volatility than new information, since GARCH coefficients are significantly higher than the ARCH coefficients. These insights can help investors, policymakers, and researchers to manage the cryptocurrency markets more effectively.

Sabrı Burak Arzova, Ayben Koy, Bertaç Şakir Şahin

Purpose This study investigates the effect of the day of the week on the volatility of cryptocurrencies. Thus, we reveal investors' perceptions of the day of the week. Design/methodology/approach The EGARCH model consists of the day of the week for 2019–2022 and the volatility of 11 cryptocurrencies. Findings Empirical results show that the weekend harms cryptocurrency volatility. Also, there was positive cryptocurrency volatility at the beginning of the week. Our findings show that weekdays and weekends significantly impact cryptocurrency volatility. Besides, cryptocurrency investors are sensitive to market movements, disclosures, and regulations during the week. Holiday mode and cognitive shortcuts may cause cryptocurrency traders to remain passive on weekends. Research limitations/implications This study has some limitations. We include 11 cryptocurrencies in the analysis by limiting cryptocurrencies according to market capitalizations. Further studies may analyze a larger sample. In addition, further studies may examine the moderator and mediator effects of other financial instruments. Practical implications The empirical results have research, social and practical conclusions from different aspects. Our analysis may contribute to determining trading strategies, risk management, market efficiency, regulatory oversight, and investment decisions in the cryptocurrency market. Originality/value The calendar effect in financial markets has extensive literature. However, cryptocurrencies' weekday and weekend effect needs to be adequately analyzed. Besides, studies analyzing cryptocurrency volatility are limited. We contribute to the literature by investigating the impact of days of the week on cryptocurrency volatility with a large sample and current data.

Sreelekshmi Geetha, Nisha Sheen, Ajithakumari Vijayappan Nair Biju

Disclosure and transparency are two critical components in the green financing sector, especially the green bond segment. Compared to green instruments like green credit, green bond issuances facilitate information dissemination and reduce information asymmetry. Still, concerns stemming from numerous macro-level and firm-level factors impede market advancement. Investors are restrained from green bond financing owing to a fear of potential greenwashing. The nascency of the market, resulting in inadequate disclosure regimes and measurement challenges, exacerbates the problem. Can we find a solution to tackle the dilemma of greenwashing and information asymmetry using emerging, sophisticated technologies? Assessing the major theoretical underpinnings, this chapter presents a comprehensive landscape of how technologies like distributed ledger technologies, blockchain, the internet of things, artificial intelligence, machine learning, and the like fit into the green debt market. While following a theoretical approach, collating research, and the green bond market developments, the authors initiate an investigation into how technology can manage disclosure biases. The assessment signifies the role of technology, specifically FinTech, blockchain, and AI technologies, in spotting greenwashing and information asymmetry.

Naseem Al Rahahleh, Ahmed Al Qurashi

No abstract is available for this record.

David Atance, Gregorio Serna

We employ a GARCH-type model to jointly estimate returns, conditional variance and skewness and show that conditional skewness outperforms sample skewness and conditional and sample variance in predicting future Bitcoin returns. Interestingly, the results show that the relationship between conditional skewness and future Bitcoin returns is different depending on the sample period. In the first subsample (2018–2020), a period of relative calm in the Bitcoin market, the relationship is negative, which is in line with that found in the literature. However, in the second subsample (2021–2022), a period of major turmoil in the Bitcoin market, the relationship is positive, which is consistent with that found in previous papers on the relationship between conditional market skewness and future index returns during crisis periods. Based on these results, a dynamic buy and sell strategy of buying or selling Bitcoin based on the estimated conditional skewness is proposed. This dynamic strategy outperforms a static buy-and-hold strategy. The profitability of this strategy can be viewed as the reward that investors demand for bearing the risk associated with the changing conditions in the cryptocurrency market that generate time-varying expected returns.

Vytautas Tumas, Aanchal Malhotra

Often referred to as A Dark Forest, Ethereum is home to predatory trading bots that prey on user transactions. Frontrunning is made simpler on Ethereum as builders & validators are incentivised to process the highest fee transactions first. One suggested mitigation strategy is to process transactions in a pseudo-random order, preventing frontrunners from predictably affecting transaction execution order. XRP Ledger, one of the oldest blockchains to use pseudorandom ordering, is launching an Automated Market Maker. This study investigates whether frontrunning techniques commonly observed in Ethereum Automated Market Makers are feasible on the XRP Ledger Automated Market Maker. In summary, our findings demonstrate that with minor adjustments, the conventional Sandwich Attack is feasible. Additionally, we unveil a distinctive attack facilitated by the integration with the Close Limit Order Book.

Jiacheng Fu, Marco Mandolfo, Giuliano Noci

The rapid growth and significant fluctuations of the cryptocurrency market have increasingly attracted investors to add digital currencies in their portfolios. Compared to traditional financial markets, the cryptocurrency market exhibits more pronounced characteristics of behavioral finance. Investors demonstrate irrational behavior in trading processes, exhibiting clear cognitive biases, such as the endowment effect and the ostrich effect. This paper initially undertakes an analytical dissection and synthesis of various archetypal irrational behaviors, then we selected technical indicators that reflect these irrational behaviors. After the process of feature engineering, the study employs TCN-MLP model to predict the thirty-minute returns of ETH. This paper presents a comprehensive cryptocurrency returns prediction process, addressing the weakness of loosely connected theory in previous research.

Yean‐Fu Wen, Chien-Ming Huang

This work enhances the operational efficiency and balances slippage prices stability and arbitrage opportunities. The predominant mechanism in existing exchange operations is the automated market maker, which eliminates the need for customers to agree on prices and utilizes the inverse formula X × Y = K. Several existing market maker schemes, such as constant product market maker and constant mean market maker, are analyzed and compared. There are market stakeholders—such as investors, customers, arbitrageurs, and trading platforms— perceive risks and opportunities differently amidst the high volatility of the cryptocurrency market. This study discusses the merits, drawbacks, potential opportunities, and challenges associated with the mechanism. We explore the exchange efficiency and methods to balance stable slippage prices for customers and arbitrage opportunities.

Injun Hwang, Ji Ho Kwon

This paper examines whether the cross-section of cryptocurrency returns is captured by risk factors based on consumption-based asset pricing. It is an imperative task for financial economists to find the fundamental risk behind characteristic-based cryptocurrency factors in order to economically understand cryptocurrency. To address the data availability issue in the analysis of cryptocurrency, we employ mixed data sampling (MIDAS) regression to check the relation between the principal component analysis (PCA) factors in cryptocurrency returns and the factors in the consumption capital asset pricing models (CCAPMs) and intertemporal capital asset pricing models (ICAPMs). We establish significant links between them, which in turn implies that cryptocurrency returns are investors’ compensation for bearing consumption risk, conditional consumption risk, and intertemporal consumption risk. This finding underscores that cryptocurrency returns are the manifestation of macroeconomic equilibrium derived from investors’ utility maximization.

Changhoon Kang, Jong-Soo Woo, James Won‐Ki Hong

Unlike the traditional stock markets, the 24/7 nature of the cryptocurrency market poses unique challenges and opportunities, particularly in asset trading and management. These dynamic market conditions have accelerated the development of sophisticated trading strategies, increasingly leveraging the power of Artificial Intelligence (AI). Among these, AI-driven trading bots have become a prominent tool, offering enhanced decision-making capabilities over conventional methods. This paper proposes the application of the Advantage Actor-Critic (A2C) model, a reinforcement learning technique ideally suited for the unpredictable nature of the cryptocurrency market. Our research aims to optimize asset allocation within a diverse portfolio, including both high-volatility cryptocurrencies and the more stable US Dollar. The proposed A2C model strategically leverages current and predicted price data of cryptocurrencies with current asset allocation to make new asset allocation decisions. Our experiments demonstrate the A2C model’s efficacy in managing asset allocations under varying market conditions. We particularly focus on how the model responds to alterations in the loss penalty factor within its reward function, which enables a shift between aggressive and conservative investment strategies. The model effectively balances risk and return, showing promising potential in achieving stable asset growth in rising markets while mitigating losses during market downturns.

Kristína Okasová, Kristián Košťál

Cryptocurrency arbitrage, a riskless trading strategy, can yield profits but requires swift execution due to volatile opportunities that vanish rapidly. Utilizing arbitrage bots for algorithmic trading is essential for immediate trade execution across exchanges like Binance and Bybit. This paper implements such a system focusing on BTCUSDT and ETHUSDT pairs. Integrating Machine Learning (ML) aims to predict arbitrage occurrences in advance for faster trade execution, a tactic many traders overlook. Logistic Regression, Random Forest, Support Vector Machine, and Multilayer Perceptron models are implemented. Adding ML principles required the collection of a dataset with historical prices of the observed cryptocurrency pairs for various time intervals, on which we trained the model. Afterward, the model was evaluated in a live-trading environment. Results show Random Forest predicting exploitable arbitrage intervals ahead for Ether, with ML models more effective during less volatile periods. However, careful consideration is needed as predictions may not always align with market realities, leading to mixed trading outcomes. Furthermore, the training led to a model that can predict the occurrence of arbitrage; however, classifying the calculations even more carefully than in reality, resulting in a partially profitable or partially lossy trading strategy depending on the time of day and the current market stage.

Luca Galati

This study examines the impact of zero fees on market quality. This issue is examined using a natural experiment in Bitcoin provided by the Binance exchange, which eliminated maker–taker trading fees for market participants in July 2022. I find that although zero fees increase investors’ willingness to trade, thereby prima facie increasing liquidity, their elimination encourages market makers to widen the bid–ask spread and provide a shallower market depth, which in turn reduces liquidity. Liquidity providers realize gains at the expense of liquidity takers, suggesting the emergence of new potential forms of unethical financial market conduct. Notably, despite the removal of trading fees, total transaction costs increased for customers. These outcomes, coupled with the boost in exchange market share, raise concerns about price integrity and investors’ protection in the highly unregulated crypto environment, in turn implying that the elimination of maker–taker fees is harmful to the market.

Walid M.A. Ahmed

Abstract This study aims to identify the factors that robustly contribute to Bitcoin liquidity, employing a rich range of potential determinants that represent unique characteristics of the cryptocurrency industry, investor attention, macroeconomic fundamentals, and global stress and uncertainty. To construct liquidity metrics, we compile 60-min high-frequency data on the low, high, opening, and closing exchange rates of Bitcoin against the US dollar. Our empirical investigation is based on the extreme bounds analysis (EBA), which can resolve model uncertainty issues. The results of Leamer’s version of the EBA suggest that the realized volatility of Bitcoin is the sole variable relevant to explaining liquidity. With the Sala-i-Martin’s variant of EBA, however, four more variables, (viz. Bitcoin’s negative returns, trading volume, hash rates, and Google search volume) are also labeled as robust determinants. Accordingly, our evidence confirms that Bitcoin-specific factors and developments, rather than global macroeconomic and financial variables, matter for explaining its liquidity. The findings are largely insensitive to our proxy of liquidity and to the estimation method used.

Sascha Hägele

Abstract Research on cryptocurrency exchanges, consisting of both centralized exchanges (CEXs) and decentralized exchanges (DEXs), has seen a significant increase in contributions in recent years, driven by growing interest in the conceptual design of cryptocurrency markets. Through a comprehensive review of literature published between January 2019 and September 2023, I identify and analyze different dimensions of the ongoing CEX vs. DEX debate. While DEXs emphasize decentralization, user control, and resistance to censorship, CEXs offer higher liquidity, advanced trading features, and a more established track record. Regulatory challenges, such as Know Your Customer (KYC) and Anti-Money Laundering (AML) compliance, also feature prominently in the literature and influence the choice of exchange for both traders and policymakers. In addition, I observe a growing interest in the design of pricing functions for CEXs and DEXs, particularly in the area of automated market makers (AMMs). Finally, based on my findings, I outline future research opportunities in this context and derive research gaps as well as recommended actions for practitioners.

Xie He, Shigeyuki Hamori

No abstract is available for this record.

Vishnu Laxman, R. Nithyashree

This chapter explores the concept of “financial illusion” as a tool for businesses to navigate the digital banking landscape. It highlights the symbiotic relationship between businesses and digital banking, focusing on emerging trends like decentralized finance (DeFi) and the integration of artificial intelligence and blockchain. These trends challenge conventional financial security and reshape business strategies, as traditional financial paradigms are replaced by innovative technologies. The analysis discusses the impact of digital currencies, central bank digital currencies (CBDCs), and data analytics on financial decisions. It also highlights the importance of cybersecurity and regulatory frameworks in mitigating risks. The chapter emphasizes the need for businesses to adapt and embrace these changes, shedding the illusion of financial stability. It serves as a roadmap for businesses to navigate the digital banking landscape and make informed decisions in an era of financial illusion.

Joy Dip Das, Ruppa K. Thulasiram, Christopher J. Henry, A. Thavaneswaran

This work addresses the intricate task of predicting the prices of diverse financial assets, including stocks, indices, and cryptocurrencies, each exhibiting distinct characteristics and behaviors under varied market conditions. To tackle the challenge effectively, novel encoder–decoder architectures, AE-LSTM and AE-GRU, integrating the encoder–decoder principle with LSTM and GRU, are designed. The experimentation involves multiple activation functions and hyperparameter tuning. With extensive experimentation and enhancements applied to AE-LSTM, the proposed AE-GRU architecture still demonstrates significant superiority in forecasting the annual prices of volatile financial assets from the multiple sectors mentioned above. Thus, the novel AE-GRU architecture emerges as a superior choice for price prediction across diverse sectors and fluctuating volatile market scenarios by extracting important non-linear features of financial data and retaining the long-term context from past observations.

Nicolò Giunta, Giuseppe Orlando, Alessandra Carleo, Jacopo Maria Ricci

This study addresses market concentration among major corporations, highlighting the utility of relative entropy for understanding diversification strategies. It introduces entropic value at risk (EVaR) as a coherent risk measure, which is an upper bound to the conditional value at risk (CVaR), and explores its generalization, relativistic value at risk (RLVaR), rooted in Kaniadakis entropy. Through extensive empirical analysis on both developed (i.e., S&P 500 and Euro Stoxx 50) and developing markets (i.e., BIST 100 and Bovespa), the study evaluates entropy-based criteria in portfolio selection, investigates model behavior across different market types, and assesses the impact of cryptocurrency introduction on portfolio performance and diversification. The key finding indicates that entropy measures effectively identify optimal portfolios, particularly in scenarios of heightened risk and increased concentration, crucial for mitigating negative net performances during low returns or high turnover. Bitcoin is primarily used for diversification and performance enhancement in the BIST 100 index, while its allocation in other markets remains minimal or non-existent, confirming the extreme concentration observed in stock markets dominated by a few leading stocks.

Raed Alsini, Qasem Abu Al‐Haija, Abdulaziz A. Alsulami, Badraddin Alturki · 8 authors

Introduction: The cryptocurrency market is captivating the attention of both retail and institutional investors. While this highly volatile market offers investors substantial profit opportunities, it also entails risks due to its sensitivity to speculative news and the erratic behavior of major investors, both of which can provoke unexpected price fluctuations. Methods: In this study, we contend that extreme and sudden price changes and atypical patterns might compromise the performance of technical signals utilized as the basis for feature extraction in a machine learning-based trading system by either augmenting or diminishing the model's generalization capability. To address this issue, this research uses a bagged tree (BT) model to forecast the buy signal for the cryptocurrency market. To achieve this, traders must acquire knowledge about the cryptocurrency market and modify their strategies accordingly. Results and discussion: To make an informed decision, we depended on the most prevalently utilized oscillators, namely, the buy signal in the cryptocurrency market, comprising the Relative Strength Index (RSI), Bollinger Bands (BB), and the Moving Average Convergence/Divergence (MACD) indicator. Also, the research evaluates how accurately a model can predict the performance of different cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), and Binance Coin (BNB). Furthermore, the efficacy of the most popular machine learning model in precisely forecasting outcomes within the cryptocurrency market is examined. Notably, predicting buy signal values using a BT model provides promising results.

Israel Nunes de Almeida, Rafael Baptista Palazzi, Marcelo Cabús Klötzle, Antônio Carlos Figueiredo Pinto · 5 authors

No abstract is available for this record.

Barbara Abou Tanos, Georges Badr

The rise of cryptocurrencies as alternative financial investments, with potential safe-haven and hedging properties, highlights the need to examine their market efficiency. This study is the first to investigate the combined impact of liquidity and volatility features of cryptocurrencies on their price delays. Using a wide spectrum of cryptocurrencies, we investigate whether the COVID-19 outbreak has affected market efficiency by studying price delays to market information. We find that as liquidity increases and volatility decreases, cryptocurrencies demonstrate stronger market efficiency. Additionally, we show that price delay differences during the COVID-19 outbreak increase with higher levels of illiquidity, particularly for highly volatile quintiles. We suggest that perceived risks and high transaction costs in illiquid and highly volatile cryptocurrencies reduce active traders’ willingness to engage in arbitrage trading, leading to increased market inefficiencies. Our findings are relevant to investors, aiding in improving their decision-making processes and enhancing their investment efficiency. Our paper also presents significant implications for policymakers, emphasizing the need for reforms aimed at enhancing the speed at which information is incorporated into cryptocurrency returns. These reforms would help mitigate market distortions and increase the sustainability of cryptocurrency markets.

Maximilian Nagl

This paper quantifies the intricacy, i.e., non-linearity and interactions of predictor variables, in explaining cryptocurrency returns. Using data from several thousand cryptocurrencies spanning 2014 to 2022, we observe a notably high level of intricacy. This provides a quantitative measure why linear models are often outperformed by machine learning algorithms in predicting cryptocurrency returns. Furthermore, we document that the intricacy in these predictions is considerably larger compared to stocks. Our analysis reveals that interactions are gaining importance over time, while individual non-linearity of the drivers is diminishing. This adds to the emerging literature on spillover effects between cryptocurrencies, traditional finance and the economy. This finding is important for investors as well as regulators as the high intricacy proposes challenges to both actors in the market.

Woosung Jung, Haerang Park

No abstract is available for this record.