In this study, we perform some analysis for the probability distributions in the space of frequency and time variables. However, in the domain of high frequencies, it behaves in such a way as the highly non-linear dynamics. The wavelet analysis is a powerful tool to perform such analysis in order to search for the characteristics of frequency variations over time for the prices of major cryptocurrencies. In fact, the wavelet analysis is found to be quite useful as it examine the validity of the efficient market hypothesis in the weak form, especially for the presence of the cyclical persistence at different frequencies. If we could find some cyclical persistence at different frequencies, that means that there exist some intrinsic causal relationship for some given investment horizons defined by some chosen sampling scales. This is one of the characteristic results of the wavelet analysis in the time-frequency domains.
Wash trading, the practice of simultaneously placing buy and sell orders for the same asset to inflate trading volume, has been prevalent in cryptocurrency markets. This paper investigates whether wash traders in Bitcoin act deliberately to exploit market conditions and identifies the characteristics of such manipulative behavior. Using a unique dataset of 18 million transactions from Mt. Gox, once the largest Bitcoin exchange, I find that wash trading intensifies when legitimate trading volume is low and diminishes when it is high, indicating strategic timing to maximize impact in less liquid markets. The activity also exhibits spillover effects across platforms and decreases when trading volumes in other asset classes like stocks or gold rise, suggesting sensitivity to broader market dynamics. Additionally, wash traders exploit periods of heightened media attention and online rumors to amplify their influence, causing rapid but short-lived spikes in legitimate trading volume. Using an exogenous demand shock associated with illicit online marketplaces, I find that wash trading responds to contemporaneous events affecting Bitcoin demand. These results advance the understanding of manipulative practices in digital currency markets and have significant implications for regulators aiming to detect and prevent wash trading.
This paper studies what Bitcoin (BTC) premiums in peer-to-peer (P2P) markets measure. Using transaction-level data from LocalBitcoins, we construct BTC premiums for 80 currencies relative to the U.S. dollar and relate them to blockchain transaction conditions, centralized crypto market (CEX) conditions, cross-border payment frictions, and foreign exchange (FX) markets. We show that these premiums reflect both trading frictions within crypto markets and local frictions in access to cross-border payments. They vary systematically with blockchain conditions and broader crypto market conditions, including BTC returns and volatility, and they are larger in countries facing greater frictions in conventional cross-border payment channels. This pattern is especially pronounced in economies with binding institutional constraints, i.e., tight capital controls and non-floating exchange-rate regimes, consistent with greater reliance on P2P crypto markets as an alternative cross-border payment channel. We further show that rising FX pressure is absorbed mainly through prices rather than trading volumes, and that P2P BTC premiums predict subsequent official exchange rate depreciation. Although premium levels differ across countries, their predictive content remains broadly similar. Overall, P2P BTC premiums reflect limits to arbitrage across crypto trading venues, especially where formal cross-border payment channels are more constrained, and they also embed forward-looking information about currency depreciation.
Maria Grith, Caio Almeida, Ratmir Miftachov, Zijin Wang
We analyze the first and second moment risk premia in the Bitcoin market based on options and realized returns and contrast them to the premia embedded in the main US stock index market. First, Bitcoin is much more volatile and has a higher variance risk premium than the S&P 500. By decomposing the return premium into different regions of the return state space, we find that while most of the S&P 500 equity premium comes from mildly negative returns, the corresponding negative Bitcoin returns (between three and one standard deviations) account for only one-third of the total Bitcoin premium (BP). Further, applying a novel clustering algorithm to a collection of estimated Bitcoin option-implied risk-neutral densities, we find that risk premia vary over time as a function of two distinct market volatility regimes. The low-volatility regime implies a relatively high share of BP attributable to positive returns and a high Bitcoin Variance Risk Premium (BVRP). In high-volatility states, the BP attributable to positive and negative returns is more balanced, and the BVRP is lower. These results suggest Bitcoin investors are more concerned about variance and upside risk in a low-volatility regime.
This paper investigates how pricing schemes can achieve efficient allocations in blockchain systems featuring multiple transaction queues under a global capacity constraint. I model a capacity-constrained blockchain where users submit transactions to different queues -- each representing a submarket with unique demand characteristics -- and decide to participate based on posted prices and expected delays. I find that revenue maximization tends to allocate capacity to the highest-paying queue, whereas welfare maximization generally serves all queues. Optimal relative pricing of different queues depends on factors such as market size, demand elasticity, and the balance between local and global congestion. My results have implications for the implementation of local congestion pricing for evolving blockchain architectures, including parallel transaction execution, directed acyclic graph (DAG)-based systems, and multiple concurrent proposers.
This paper explores how ad platforms can utilize Bayesian persuasion within blockchain-based auction systems to strategically influence advertiser behavior despite increased transparency. By integrating game-theoretic models with machine learning techniques and the principles of blockchain technology, we analyze the role of strategic information disclosure in ad auctions. Our findings demonstrate that even in environments with inherent transparency, ad platforms can design signals to affect advertisers' beliefs and bidding strategies. A detailed case study illustrates how machine learning can predict advertiser responses to different signals, leading to optimized signaling strategies that increase expected revenue. The study contributes to the literature by extending Bayesian persuasion models to transparent systems and providing practical insights for auction design in the digital advertising industry.
When analyzing Bitcoin users' balance distribution, we observed that it follows a log-normal pattern. Drawing parallels from the successful application of Gibrat's law of proportional growth in explaining city size and word frequency distributions, we tested whether the same principle could account for the log-normal distribution in Bitcoin balances. However, our calculations revealed that the exponent parameters in both the drift and variance terms deviate slightly from one. This suggests that Gibrat's proportional growth rule alone does not fully explain the log-normal distribution observed in Bitcoin users' balances. During our exploration, we discovered an intriguing phenomenon: Bitcoin users tend to fall into two distinct categories based on their behavior, which we refer to as ``poor" and ``wealthy" users. Poor users, who initially purchase only a small amount of Bitcoin, tend to buy more bitcoins first and then sell out all their holdings gradually over time. The certainty of selling all their coins is higher and higher with time. In contrast, wealthy users, who acquire a large amount of Bitcoin from the start, tend to sell off their holdings over time. The speed at which they sell their bitcoins is lower and lower over time and they will hold at least a small part of their initial holdings at last. Interestingly, the wealthier the user, the larger the proportion of their balance and the higher the certainty they tend to sell. This research provided an interesting perspective to explore bitcoin users' behaviors which may apply to other finance markets.
Sep 13, 2024·Complex Networks & Their Applications XIII: Proceedings of the 13th International Conference on Complex Networks and Their Applications (COMPLEX NETWORKS 2024)
The prediction of both the existence and weight of network links at future time points is essential as complex networks evolve over time. Traditional methods, such as vector autoregression and factor models, have been applied to small, dense networks, but become computationally impractical for large-scale, sparse, and complex networks. Some machine learning models address dynamic link prediction, but few address the simultaneous prediction of both link presence and weight. Therefore, we introduce a novel model that dynamically predicts link presence and weight by dividing the task into two sub-tasks: predicting remittance ratios and forecasting the total remittance volume. We use a self-attention mechanism that combines temporal-topological neighborhood features to predict remittance ratios and use a separate model to forecast the total remittance volume. We achieve the final prediction by multiplying the outputs of these models. We validated our approach using two real-world datasets: a cryptocurrency network and bank transfer network.
Blockchain technology and decentralized finance (DeFi) are reshaping global financial systems. Despite their impact, the spatial distribution of public sentiment and its economic and geopolitical determinants are often overlooked. This study analyzes over 150 million geo-tagged, DeFi-related tweets from 2012 to 2022, sourced from a larger dataset of 7.4 billion tweets. Using sentiment scores from a BERT-based multilingual classification model, we integrated these tweets with economic and geopolitical data to create a multimodal dataset. Employing techniques like sentiment analysis, spatial econometrics, clustering, and topic modeling, we uncovered significant global variations in DeFi engagement and sentiment. Our findings indicate that economic development significantly influences DeFi engagement, particularly after 2015. Geographically weighted regression analysis revealed GDP per capita as a key predictor of DeFi tweet proportions, with its impact growing following major increases in cryptocurrency values such as bitcoin. While wealthier nations are more actively engaged in DeFi discourse, the lowest-income countries often discuss DeFi in terms of financial security and sudden wealth. Conversely, middle-income countries relate DeFi to social and religious themes, whereas high-income countries view it mainly as a speculative instrument or entertainment. This research advances interdisciplinary studies in computational social science and finance and supports open science by making our dataset and code available on GitHub, and providing a non-code workflow on the KNIME platform. These contributions enable a broad range of scholars to explore DeFi adoption and sentiment, aiding policymakers, regulators, and developers in promoting financial inclusion and responsible DeFi engagement globally.
Dimitar Kitanovski, Igor Mishkovski, Viktor Stojkoski, Miroslav Mirchev
Maintaining a balance between returns and volatility is a common strategy for portfolio diversification, whether investing in traditional equities or digital assets like cryptocurrencies. One approach for diversification is the application of community detection or clustering, using a network representing the relationships between assets. We examine two network representations, one based on a standard distance matrix based on correlation, and another based on mutual information. The Louvain and Affinity propagation algorithms were employed for finding the network communities (clusters) based on annual data. Furthermore, we examine building assets' co-occurrence networks, where communities are detected for each month throughout a whole year and then the links represent how often assets belong to the same community. Portfolios are then constructed by selecting several assets from each community based on local properties (degree centrality), global properties (closeness centrality), or explained variance (Principal component analysis), with three value ranges (max, med, min), calculated on a maximal spanning tree or a fully connected community sub-graph. We explored these various strategies on data from the S\&P 500 and the Top 203 cryptocurrencies with a market cap above 2M USD in the period from Jan 2019 to Sep 2022. Moreover, we study into more details the periods of the beginning of the COVID-19 outbreak and the start of the war in Ukraine. The results confirm some of the previous findings already known for traditional stock markets and provide some further insights, while they reveal an opposing trend in the crypto-assets market.
In the digital era, where innovative technologies like blockchain are revolutionizing traditional organizational paradigms, Decentralized Autonomous Organizations (DAOs) emerge as avant-garde models of collective governance. However, their unique structure challenges existing legal frameworks, especially concerning the liability of participants. This study focuses on analyzing the legal implications of the decentralized nature of DAOs, with a particular emphasis on the aspects of participant liability. Such considerations are essential for understanding how current legal systems might be adapted or reformed to effectively address these novel challenges. The paper examines the specificity of DAOs, highlighting their decentralized governance structure and reliance on smart contracts, which introduce unique issues related to the blurring of liability boundaries. It underscores how the anonymity of DAO participants and the automatic execution of smart contracts complicate the traditional concept of legal liability, both within the DAO context and in interactions with external parties. The analysis also includes a comparison between DAOs and traditional organizational forms, such as corporations and associations, to identify potential analogies and differences in participant liability. It explores how existing regulations on partner liability might be insufficient or inapplicable in the DAO context, prompting the search for new, innovative legal solutions.
The sharing economy is sprawling across almost every sector and activity around the world. About a decade ago, there were only a handful of platform driven companies operating on the market. Zipcar, BlaBlaCar and Couchsurfing among them. Then Airbnb and Uber revolutionized the transportation and hospitality industries with a presence in virtually every major city. Access over ownership is the paradigm shift from the traditional business model that grants individuals the use of products or services without the necessity of buying them. Digital platforms, data and algorithm-driven companies as well as decentralized blockchain technologies have tremendous potential. But they are also changing the rules of the game. One of such technologies challenging the legal system are AI systems that will also reshape the current legal framework concerning the liability of operators, users and manufacturers. Therefore, this introductory chapter deals with explaining and describing the legal issues of some of these disruptive technologies. The chapter argues for a more forward-thinking and flexible regulatory structure.
This paper surveys products and studies on cryptoeconomics and tokenomics from an economic perspective, as these terms are still (i) ill-defined and (ii) disconnected from economic disciplines. We first suggest that they can be novel when integrated; we then conduct a literature review and case study following consensus-building for decentralization and token value for autonomy. Integration requires simultaneous consideration of strategic behavior, spamming, Sybil attacks, free-riding, marginal cost, marginal utility and stabilizers. This survey is the first systematization of knowledge on cryptoeconomics and tokenomics, aiming to bridge the contexts of economics and blockchain.
This paper asks why startups in the blockchain industry are exiting to Decentralized Autonomous Organizations (DAOs), an outstanding phenomena in the wider digital economy which has tended to retain centralized ownership and governance rights of many platforms, products and protocols. Drawing on a narrative analysis of three case studies, I find three possible drivers: (1) exit to DAO is motivated by both financial and stewardship goals which it simultaneously promises to realize via the issuance of tokens; (2) exit to DAO adds an additional layer of ownership and governance rights via tokens, without requiring existing rights to be relinquished, thus making it a lucrative strategy; and (3) markets, laws and social norms underpinning the broader environment in which exits to DAO occur, seem to play an important role in driving the decision. This paper contributes to the academic literature by situating DAOs as a hybrid (and perhaps incomplete) entrepreneurial exit strategy and identifying plausible drivers of the phenomenon which warrant further dedicated research.
Blockchain technology is essential for the digital economy and metaverse, supporting applications from decentralized finance to virtual assets. However, its potential is constrained by the "Blockchain Trilemma," which necessitates balancing decentralization, security, and scalability. This study evaluates and compares two leading proof-of-stake (PoS) systems, Algorand and Ethereum 2.0, against these critical metrics. Our research interprets existing indices to measure decentralization, evaluates scalability through transactional data, and assesses security by identifying potential vulnerabilities. Utilizing real-world data, we analyze each platform’s strategies in a structured manner to understand their effectiveness in addressing trilemma challenges. The findings highlight each platform’s strengths and propose general methodologies for evaluating key blockchain characteristics applicable to other systems. This research advances the understanding of blockchain technologies and their implications for the future digital economy. Data and code are available on GitHub as open source.
Riccardo De Blasis, Luca Galati, Rosanna Grassi, Giorgio Rizzini
This paper investigates the cryptocurrency network of the FTX exchange during the collapse of its native token, FTT, to understand how network structures adapt to significant financial disruptions, by exploiting vertex centrality measures. Using proprietary data on the transactional relationships between various cryptocurrencies, we construct the filtered correlation matrix to identify the most significant relations in the FTX and Binance markets. By using suitable centrality measures - closeness and information centrality - we assess network stability during FTX's bankruptcy. The findings document the appropriateness of such vertex centralities in understanding the resilience and vulnerabilities of financial networks. By tracking the changes in centrality values before and during the FTX crisis, this study provides useful insights into the structural dynamics of the cryptocurrency market. Results reveal how different cryptocurrencies experienced shifts in their network roles due to the crisis. Moreover, our findings highlight the interconnectedness of cryptocurrency markets and how the failure of a single entity can lead to widespread repercussions that destabilize other nodes of the network.
An Pham Ngoc Nguyen, Martin Crane, Thomas Conlon, Marija Bezbradica
Herding behavior has become a familiar phenomenon to investors, with potential dangers of both undervaluing and overvaluing assets, while also threatening market stability. This study contributes to the literature on herding behavior by using a recent dataset, covering the most impactful events of recent years. To our knowledge, this is the first study examining herding behavior across three different types of investment vehicle and also the first study observing herding at a community (subset) level. Specifically, we first explore this phenomenon in each separate type of investment vehicle, namely stocks, US ETFs and cryptocurrencies, using the Cross-Sectional Absolute Deviation model. We find mostly similar herding patterns for stocks and US ETFs. Subsequently, the same experiment is implemented on a combination of all three investment vehicles. For a deeper investigation, we adopt graph-based techniques including the Minimum Spanning Tree and Louvain community detection to partition the combination into smaller subsets to detect herding behavior for each subset. We find that herding behavior exists at all times across all types of investment vehicle at a subset level, although perhaps not at the superset level, and that this herding behavior tends to stem from specific events that solely impact that subset of assets. Lastly, we explore herding by examining the financial contagion effects between these types of investment vehicle. Results show that US ETFs not only have a tendency to propagate similar trading behaviors in stocks and especially cryptocurrencies but also show self-reinforcing herding behavior, acting as drivers of their own trends.
The rapid advancements in artificial intelligence, big data analytics, and cloud computing have precipitated an unprecedented demand for computational resources. However, the current landscape of computational resource allocation is characterized by significant inefficiencies, including underutilization and price volatility. This paper addresses these challenges by introducing a novel global platform for the commodification of compute hours, termed the Global Compute Exchange (GCX) (Patent Pending). The GCX leverages blockchain technology and smart contracts to create a secure, transparent, and efficient marketplace for buying and selling computational power. The GCX is built in a layered fashion, comprising Market, App, Clearing, Risk Management, Exchange (Offchain), and Blockchain (Onchain) layers, each ensuring a robust and efficient operation. This platform aims to revolutionize the computational resource market by fostering a decentralized, efficient, and transparent ecosystem that ensures equitable access to computing power, stimulates innovation, and supports diverse user needs on a global scale. By transforming compute hours into a tradable commodity, the GCX seeks to optimize resource utilization, stabilize pricing, and democratize access to computational resources. This paper explores the technological infrastructure, market potential, and societal impact of the GCX, positioning it as a pioneering solution poised to drive the next wave of innovation in commodities and compute.
Darcy W. E. Allen, Jason Potts, Julian Waters-Lynch, Max Parasol
Decentralised Autonomous Organisations (DAOs) are a new type of digital organisation that uses blockchain infrastructure (e.g. smart contracts, tokens) to coordinate a group of people around a shared mission. Like all organisations, DAOs must attract sources of funding and other resources, and discover and retain a talented community and workforce. To do this, they must signal their true quality. Yet the characteristics of the environment that DAOs operate in (pseudonymous actors, global scale, permissionless entry and exit) makes this difficult. We apply costly signalling theory to explore the information asymmetry problem in DAOs and some of the strategies (behaviours and investments) and institutional solutions (including better signalling mechanisms) that have evolved to solve this problem.
Decentralized autonomous organizations (DAOs) are emerging innovative organizational structures, enabling collective coordination, and reshaping digital collaboration. Despite the promising and transformative characteristics of DAOs, the potential technological advancements and the understanding of the business value that organizations derive from implementing DAO characteristics are limited. This research applies a systematic review of DAOs' business applicability from an open systems perspective following a best-fit framework methodology. Within our approach, combining both framework and thematic analysis, we discuss how the open business principles apply to DAOs and present a new DAO business framework comprising of four core business elements: i) token, ii) transactions, iii) value system and iv) strategy with their corresponding sub-characteristics. This paper offers a preliminary DAO business framework that enhances the understanding of DAOs' transformative potential and guides organizations in innovating more inclusive business models (BMs), while also providing a theoretical foundation for researchers to build upon.
Nicolas Oderbolz, Beatrix Marosvölgyi, Matthias Hafner
This paper examines the economic and security implications of Proof-of-Stake (POS) designs, providing a survey of POS design choices and their underlying economic principles in prominent POS-blockchains. The paper argues that POS-blockchains are essentially platforms that connect three groups of agents: users, validators, and investors. To meet the needs of these groups, blockchains must balance trade-offs between security, user adoption, and investment into the protocol. We focus on the security aspect and identify two different strategies: increasing the quality of validators (static security) vs. increasing the quantity of stakes (dynamic security). We argue that quality comes at the cost of quantity, identifying a trade-off between the two strategies when designing POS systems. We test our qualitative findings using panel analysis on collected data. The analysis indicates that enhancing the quality of the validator set through security measures like slashing and minimum staking amounts may decrease dynamic security. Further, the analysis reveals a strategic divergence among blockchains, highlighting the absence of a single, universally optimal staking design solution. The optimal design hinges upon a platform's specific objectives and its developmental stage. This research compels blockchain developers to meticulously assess the trade-offs outlined in this paper when developing their staking designs.
Carlos Alberto Durigan Junior, Kumiko Oshio Kissimoto, Fernando Jose Barbin Laurindo
The fourth industrial revolution promotes the integration of Information Technology (IT) and strategic resources. New IT demands and uses have been leading to changes in business processes and corporate governance. Lately, the financial industry has adopted a new integrated banking model known as Open Banking (OB) and the advent of cryptocurrencies has led to the Digital Economy (DE) materialization. Considering these facts, this paper expects to point out through literature review some IT enabling factors that allow the conception of a new industry design (or governance) specifically in the financial industry illustrated by the cases of the Open Banking and Digital Economy. This paper is structured mostly on literature review, accompanied by results, discussions, and finally, conclusions are presented. It was found five potential enabling factors. Keywords: Digital Economy, Information Technology (IT), Open Banking.
Sid Bhatia, Samuel Gedal, H. Lee, Ravinder Chopra · 6 authors
This paper examines the dynamics of the cryptocurrency market and proposes a novel blockchain-based protocol for real estate transactions. Our analysis includes a detailed review of price trends, volatility, and correlations within the cryptocurrency market, focusing on major assets like Bitcoin, Ethereum, and Tether. We provide a critical assessment of the impact of significant market events, such as the FTX bankruptcy, highlighting the vulnerabilities and resilience of the crypto market. The study also explores the potential of blockchain technology to innovate real estate transactions by enabling the secure and transparent handling of property deeds without traditional intermediaries. We introduce a blockchain protocol that reduces transaction costs, enhances security, and increases transparency, making real estate transactions more accessible and efficient. Our proposal aims to leverage the inherent benefits of blockchain to address real-world challenges in real estate transactions, providing a scalable and secure platform for property sales in a global market.
In the distributed systems landscape, Blockchain has catalyzed the rise of cryptocurrencies, merging enhanced security and decentralization with significant investment opportunities. Despite their potential, current research on cryptocurrency trend forecasting often falls short by simplistically merging sentiment data without fully considering the nuanced interplay between financial market dynamics and external sentiment influences. This paper presents a novel Dual Attention Mechanism (DAM) for forecasting cryptocurrency trends using multimodal time-series data. Our approach, which integrates critical cryptocurrency metrics with sentiment data from news and social media analyzed through CryptoBERT, addresses the inherent volatility and prediction challenges in cryptocurrency markets. By combining elements of distributed systems, natural language processing, and financial forecasting, our method outperforms conventional models like LSTM and Transformer by up to 20\% in prediction accuracy. This advancement deepens the understanding of distributed systems and has practical implications in financial markets, benefiting stakeholders in cryptocurrency and blockchain technologies. Moreover, our enhanced forecasting approach can significantly support decentralized science (DeSci) by facilitating strategic planning and the efficient adoption of blockchain technologies, improving operational efficiency and financial risk management in the rapidly evolving digital asset domain, thus ensuring optimal resource allocation.