Abstract This proposal introduces a novel decision-making framework to advance safe economic activities in cyberspace. We focus on identifying anomalies within crypto-asset trading, recognized as potential sources of criminal activity, severely undermining the credibility of such assets. Detecting and mitigating such anomalies holds significant societal implications, particularly in fostering trust within blockchain networks. We aim to bolster the “social trust” inherent to blockchain technology by facilitating informed economic activities in cyberspace. To achieve this, we propose integrating two artificial intelligence (AI) systems into a blockchain-based decentralized autonomous organization (DAO). The first AI application involves amalgamating various anomaly indicators, spanning from cluster coefficient, entropy, triangular motif analysis, correlation tensor analysis, loop component by Hodge decomposition, loop causality detection, network classification using graph Laplacian, and persistent homology analysis, into a comprehensive indicator using a Boltzmann machine. The second AI application entails deploying conversational AI to guide and support traders, aiding them in making informed trading decisions. This system is designed to alert DAO members to anomalies based on the integrated indicators, especially during massive price fluctuations. We operate under the assumption of close collaboration between governments, experts, traders, system developers, and operators to effectively organize DAOs. The primary technical challenge in our proposal lies in developing a wallet assisted by an intelligent software agent capable of safe interactions with traders within a unified DAO. With this organization, we envision fostering a global economic ecosystem where physical and cyber worlds converge, allowing democratic economic participation.
Abstract The asymmetries of factors influencing the return of cryptocurrencies have already been well documented; however, in the case of NFTs, only information asymmetries and hedging properties related to asymmetries were studied. Therefore, the present study examines factors affecting NFT returns, from market-related factors (crypto-market index return and stock market index return) to the Amihud illiquidity ratio and Google search trends during different market conditions. The wavelet coherences-based methodology was applied separately during the boom, bust, normal, and turbulent periods identified by structural breakpoints. Based on 14 NFT projects between April 2019 and July 2022, results show two fundamental asymmetries influencing these NFT returns. First, there is an asymmetry in the behavior of the factors in different periods; second, there is an asymmetry in how illiquidity manifests itself over NFTs that do or do not possess cash flow-generating potential.
Cryptocurrencies have introduced a transformative paradigm in financial technology, challenging traditional financial structures and creating novel transactional frameworks. With the rapid expansion of the cryptocurrency market, the need for objective assessment and comparative analysis of leading digital assets has become increasingly pertinent. This study presents a detailed, data-driven evaluation of five prominent cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Tether (USDT), USD Coin, and Lido Staked Ether (STETH). Drawing on an extensive dataset sourced from IntoTheBlock, a leading platform for cryptocurrency analytics, we assess these cryptocurrencies based on selected efficiency indicators. Our research methodology encompasses a systematic exploration of financial and network metrics, including market capitalization, volatility, daily active addresses, and transaction statistics. The results provide nuanced insights into the relative performance of these assets, identifying Bitcoin as the most efficient based on the selected criteria. This work emphasizes the significance of empirical, data-centric methodologies, eschewing subjective judgments, to deliver actionable insights for investors, policymakers, and scholars in the domain of decentralized finance.
This study examines the volatility dynamics of Bitcoin (BTC-USD) and MicroStrategy (MSTR) from September 2019 to September 2024 using the GARCH (1,1) model. Volatility is a key measure of risk in the financial market, understanding its patterns is crucial for effective portfolio management, risk management, and corporate financial strategies. Bitcoin, while known to be volatile, is very unpredictable, and given the high holding of that on MicroStrategy's balance sheet, it is closely tagged to the volatility of Bitcoin. Critical periods, such as the COVID-19 and the subsequent crypto market downturn between 2022 and 2023, demonstrate the linkages between traditional equities and digital assets. The findings of such analysis will prove that MicroStrategy's volatility has indeed closely followed the footsteps of Bitcoin, especially during the 2024 rally in that market, including all its shocks and recoveries. These find great importance in understanding volatility due to the growing integration of digital assets into corporate portfolios. This research will offer investors and corporate managers alike extensive insight into risk management and portfolio diversification by accounting for volatility dynamics between cryptocurrencies and stocks.
This study shows that returns of cryptocurrencies with similar prices move together. This price-based comovement is independent of comovements caused by other cryptocurrencies’ well-known common risk factors including size, momentum, past returns, past trading volume, or market returns. The results are robust to alternative estimation methods and data frequencies. Additional analysis shows that the relationship becomes stronger during periods of high investor sentiment, exhibits a long-run reversal, and holds within a sample of memecoins. These findings support a sentiment-based explanation of return comovement.
This paper investigates the speed of price discovery when information becomes publicly available but requires costly processing to become common knowledge. We exploit the unique institutional setting of hacks on decentralized finance (DeFi) protocols. Public blockchain data provides the precise time a hack’s transactions are recorded—becoming public information—while subsequent social media disclosures mark the transition to common knowledge. This empirical design allows us to isolate the price impact occurring during the interval characterized by information asymmetry driven purely by differential processing capabilities. Our central empirical finding is that substantial price discovery precedes common knowledge: approximately 36 percent of the total 24-hour price decline (∼27 percent) materializes before the public announcement. This evidence suggests sophisticated traders rapidly exploit their ability to process complex, publicly available on-chain data, capturing informational rents. We develop a theoretical model of informed trading under processing costs which predicts strategic, slow information revelation, consistent with our empirical findings. Our results quantify the limits imposed by information processing costs on market efficiency, demonstrating that transparency alone does not guarantee immediate information incorporation into prices.
In the AI era, we contribute to the literature by uncovering that the price dynamics of most AI tokens could be fully characterized by the processes driven by fractal Brownian motion, which robustly supports the principles of the fractal markets hypothesis. Using rescaled range (R/S, i.e., Fractal) analysis and the wavelet coherence technique, we analyzed daily log-returns from seven major AI tokens and Bitcoin over the period 2020–2024. Our empirical results rejected the weak form of the Efficient Market Hypothesis (EMH), supporting the Fractal Market Hypothesis (FMH) as a better explanation for the dynamics of AI crypto tokens. More importantly, the log-returns of all analyzed AI tokens, each exhibiting a Hurst exponent exceeding 0.58, provided evidence of persistent behavior and an inherent tendency toward positive price trajectories. These results implied that Fractal analysis can enhance investors' ability to model return dynamics and identify potential appreciation in AI tokens, particularly as short-term trading activity intensifies during episodes of elevated market turbulence. Finally, this work reveals that AI tokens exhibit strong coherence patterns with Bitcoin, varying across time and frequency domains, suggesting Bitcoin's limited role as a hedge against AI tokens. Crucially, this study highlights the significant role of AI tokens as potential safe-haven assets during market turmoil, offering valuable insights for portfolio diversification for crypto investors with intuitive and plausible results that carry strong policy implications.
This article examines the transformative evolution of transaction processing systems from traditional batch processing to real-time payment mechanisms. The historical progression and architectural distinctions between these paradigms while analyzing the critical transition factors that facilitated this evolution. The discussion encompasses the enabling technologies—including API-driven banking, distributed ledger solutions, and cloud computing infrastructure—that have revolutionized payment processing capabilities. Through the demonstration of current implementation cases across peer-to-peer transfers, business transactions, and international remittances, the article provides insights into practical applications and market adoption patterns. The exploration extends to emerging trends, including central bank digital currencies, artificial intelligence for fraud detection, and enhanced security frameworks. The article concludes with a forward-looking discussion of research imperatives addressing cross-border payment efficiency, monetary policy implications in real-time environments, and financial inclusion opportunities through modernized payment infrastructure. This comprehensive article provides valuable perspectives on the technological, operational, and policy dimensions of payment system evolution for financial professionals, technology implementers, and policy researchers.
Abstract The objective of this study was to examine the level and behaviour of the weak-form efficiency of the 16 most capitalised cryptocurrencies using intraday data. The study employed martingale difference hypothesis tests utilising the rolling window method. The predictability of high frequency returns varied over time. For most of the time, the cryptocurrencies were unpredictable. Nevertheless, their weak-form efficiency appeared to decrease along with an increase in frequency. In general, most cryptocurrencies were marked by high levels of unpredictability. However, there were some significant differences between the most and least efficient ones. To exploit market inefficiencies, investors should focus on higher frequencies. Higher frequencies should also be a concern to regulators when it comes to ensuring market efficiency.
Since the introduction of Modern Portfolio Theory (MPT) in 1952, its practical applications, associated challenges, and computational efficiency on large and high-frequency datasets, particularly datasets not used to develop and optimize the model, have drawn extensive research interest. This study has examined the performance of various portfolio models that have explored the concept of MPT on U.S. stock and cryptocurrency markets, i.e., discrete Markowitz portfolio selection (DMPS), the optimal dynamic portfolio (ODP), the binary unconstrained ODP (BUODP) with a quantum annealing solver, and the 1/N naive diversification (ND). Their performance is then compared to the indices that measure the performance of corresponding market exchange-traded funds (ETFs) for stock markets. Our findings show that the DMPS and ODP perform better than other models, delivering better returns in a shorter period. Both run significantly faster than the BUODP (with quantum annealing) with computation time of approximately 0.5 seconds for the S&P 400, 500, and 600 markets whereas BUODP takes 30 seconds; they also mitigate risk and outperform ETFs and ND model for the out-of-sample test with diversified portfolios combining NASDAQ stocks and cryptocurrencies. Furthermore, we have analyzed the impact of data with different frequency intervals, i.e., weekly, daily, hourly, and one-minute, on portfolio performance of the stock markets. The results suggest that data collection frequencies do not make differences in portfolio selections and weights. This study contributes to the advancement of portfolio theory, providing insights and practical values, especially in addressing computational efficiency for high-frequency and large-scale datasets and saving computational costs.
The cryptocurrency market poses a huge challenge to portfolio optimization due to its high volatility and complex market dynamics. To address these issues, this paper uses reinforcement learning (RL) algorithms for dynamic portfolio optimization, aiming to improve the return and risk control capabilities of the portfolio through intelligent decision-making. This paper adopts a strategy based on deep reinforcement learning. By interacting with the cryptocurrency market, the agent can continuously optimize asset allocation, maximize investment returns while controlling volatility. The experimental results show that compared with traditional strategies, the reinforcement learning model has obvious advantages in key indicators such as cumulative return rate, annualized volatility, maximum drawdown and Sharpe ratio. Specifically, the cumulative return rate of the reinforcement learning model reaches 85.12%, the annualized volatility is 45.76%, and the maximum drawdown is controlled at -22.34%, showing strong income acquisition and risk management capabilities. In addition, the dynamic adjustment of asset allocation has optimized the weights of various cryptocurrencies, effectively dispersed risks, and improved the overall performance of the investment portfolio.