Abstract Cryptocurrency speculation involves investing in assets with highly volatile price movements in which large sums can be gained or lost in short periods. Although fear of missing out (FOMO) has been positively linked to this type of activity, less is known about the role of regret, such as how people react to actions taken (acts of commission) or not taken (acts of omission). Anticipated regret was investigated in a study involving 403 investors ( M = 325, F = 73, Other = 5) recruited from an online panel and presented with meme coin scenarios that manipulated omission (not buying) or commission (sold early) while also examining the roles of social comparison and temporal framing. Scenarios were arranged in a 2 Ă 2 Ă 2 factorial design with FOMO, risk tolerance, impulsivity, financial literacy and problem gambling included as covariates to control for potential individual differences. Acts of commission were associated with greater regret and negative emotion but not with FOMO-based investment decisions. No effects were found for temporal distance or social comparison. At-risk and problem-gambling investors were also found to be more vulnerable to negative emotions and risky intention decision-making than non-risk gamblers. FOMO and risk tolerance were related to making decisions based on FOMO, whereas cryptocurrency literacy appeared to mitigate this tendency. These findings underscore the potential value of consumer education in raising awareness of psychological biases that are likely to lead to riskier speculative decisions.
Portfolio optimization is a cornerstone of modern financial decision-making, tradition-ally based on the meanâvariance model introduced by Markowitz. However, this framework relies on restrictive assumptionsâsuch as normally distributed returns and symmetric risk preferencesâthat often fail in real-world markets, particularly in volatile and non-Gaussian environments such as cryptocurrencies. To address these limitations, this paper proposes a novel multi-objective model that combines expected return max-imization, mean absolute deviation (MAD) minimization, and entropy-based diversifi-cation into a unified optimization structure: the MeanâDeviationâEntropy (MDE) model. The MAD metric offers a robust alternative to variance by capturing the average mag-nitude of deviations from the mean without inflating extreme values, while entropy serves as an information-theoretic proxy for portfolio diversification and uncertainty. Three entropy formulations are consideredâShannon entropy, Tsallis entropy, and cumulative residual SharmaâTanejaâMittal entropy (CR-STME)âto explore different notions of uncertainty and structural diversity. The MDE model is formulated as a tri-objective optimization problem and solved via scalarization techniques, enabling flexible trade-offs between return, deviation, and en-tropy. The framework is empirically tested on a cryptocurrency portfolio composed of Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB), using daily data over a 12-month period. The empirical setting reflects a high-volatility, high-skewness regime, ideal for testing entropy-driven diversification. Comparative outcomes reveal that entropy-integrated models yield more robust weightings, particularly when tail risk and regime shifts are present. Comparative results against classical meanâvariance and meanâMAD models indicate that the MDE model achieves improved di-versification, enhanced allocation stability, and greater resilience to volatility clustering and tail risk. This study contributes to the literature on robust portfolio optimization by integrating entropy as a formal objective within a scalarized multi-criteria framework. The proposed approach offers promising applications in sustainable investing, algorithmic asset allo-cation, and decentralized finance, especially under high-uncertainty market conditions.
We formulate automated market maker (AMM) \emph{rebalancing} as a binary detection problem and study a hybrid quantum--classical self-attention block, \textbf{Quantum Adaptive Self-Attention (QASA)}. QASA constructs quantum queries/keys/values via variational quantum circuits (VQCs) and applies standard softmax attention over Pauli-$Z$ expectation vectors, yielding a drop-in attention module for financial time-series decision making. Using daily data for \textbf{BTCUSDC} over \textbf{Jan-2024--Jan-2025} with a 70/15/15 time-series split, we compare QASA against classical ensembles, a transformer, and pure quantum baselines under Return, Sharpe, and Max Drawdown. The \textbf{QASA-Sequence} variant attains the \emph{best single-model risk-adjusted performance} (\textbf{13.99\%} return; \textbf{Sharpe 1.76}), while hybrid models average \textbf{11.2\%} return (vs.\ 9.8\% classical; 4.4\% pure quantum), indicating a favorable performance--stability--cost trade-off.
Purpose This study aims to fill a gap in cryptocurrency regulation research by establishing a dynamic framework that balances market stability and capital flight. It seeks to derive an optimal, adaptive regulatory strategy that reconciles stringent enforcement with its unintended consequences. Ultimately, the study provides insights to guide policymakers in designing interventions that sustain financial stability while supporting efficient market functioning in the evolving digital asset environment. Design/methodology/approach This study develops a differential game-theoretic model to capture the dynamic interplay between cryptocurrency regulators and market participants. Using stochastic differential equations to model market stability and capital flight, the framework derives Nash equilibrium conditions for optimal regulatory intensity and liquidity migration. The model is validated through Monte Carlo simulations that examine various market scenarios and sensitivity analyses for robustness across different parameter settings. Findings Results indicate that an aggressive initial regulatory stance rapidly enhances market stability, albeit at the cost of a temporary increase in capital flight. Over time, adaptive regulatory adjustments lead to a self-stabilizing equilibrium where volatility diminishes and liquidity migration is contained. The Nash equilibrium analysis confirms that a balanced enforcement strategy can effectively mitigate the adverse impacts of capital flight while maintaining overall market resilience, as supported by consistent outcomes from the simulation experiments. Research limitations/implications The modelâs simplifying assumptions, including a homogeneous market and single regulator framework, limit its immediate real world applicability. It uses a continuous time approach and normally distributed shocks, which may not capture discrete regulatory events or extreme market disruptions. Additionally, the analysis is sensitive to parameter calibration. These limitations suggest further research is needed to incorporate multi-agent dynamics, market microstructure factors and alternative stochastic processes to improve empirical validation and practical relevance. Practical implications The study provides policymakers a dynamic framework for calibrating regulatory intensity to balance market stability with the risk of capital flight. It emphasizes that while strict initial enforcement can stabilize markets, subsequent moderation is key to sustaining resilience. The derived equilibrium conditions provide actionable insights for designing adaptive, real-time interventions that minimize liquidity outflows and improve overall market integrity, supporting a regulatory approach that promotes innovation while mitigating systemic risks. Originality/value This research pioneers the application of differential game theory to cryptocurrency regulation, integrating market stability and capital flight into a single dynamic model. By deriving Nash equilibrium conditions and validating the framework through numerical experiments, the paper advances current literature and provides a novel, theoretically rigorous tool. Its innovative perspective equips policymakers with a nuanced approach to designing responsive regulatory strategies in the fast-evolving digital asset space.
ABSTRACT Cryptocurrency markets are known for their wide price fluctuations, lack of central control, and fastâpaced development. These characteristics present serious challenges to traditional theories about how markets work and how prices reflect available information. Understanding how information is processed in these markets is essential for investors, policy makers, and academic researchers. This paper offers a thorough review on the extent to which cryptocurrency markets reflect information, based on 977 peerâreviewed articles published between 2015 and 2024 and indexed in Scopus. Using a combined method of bibliometric analysis and thematic review, the study identifies key research directions and common methods used to explore how information affects cryptocurrency prices. The review goes beyond the Efficient Market Hypothesis (EMH) and includes related topics such as volatility modelling, behavioral dynamics, spillovers, liquidity, and institutional influences. It presents a detailed overview of the most influential publications and organises the literature into six thematic research clusters, highlighting conceptual tensions and new methodological approaches. Finally, the paper outlines a future research agenda that connects market efficiency with changing regulatory environments, innovations in market structure, and the increasing role of institutional actors in the cryptocurrency space.
Accurate and efficient cryptocurrency price prediction is vital for investors in the volatile crypto market. This study comprehensively evaluates nine modelsâincluding baseline, zero-shot, and deep learning architecturesâon 21 major cryptocurrencies using daily and hourly data. Our multi-dimensional evaluation assesses models based on prediction accuracy (MAE, RMSE, MAPE), speed, statistical significance (DieboldâMariano test), and economic value (Sharpe Ratio). Our research found that the optimally fine-tuned TimeGPT model (without variables) demonstrated superior performance across both Daily and Hourly datasets, with its statistical leadership confirmed by the DieboldâMariano test. Fine-tuned Chronos excelled in daily predictions, while TFT was a close second to TimeGPT for hourly forecasts. Crucially, zero-shot models like TimeGPT and Chronos were tens of times faster than traditional deep learning models, offering high accuracy with superior computational efficiency. A key finding from our economic analysis is that a modelâs effectiveness is highly dependent on market characteristics. For instance, TimeGPT with variables showed exceptional profitability in the volatile ETH market, whereas the zero-shot Chronos model was the top performer for the cyclical BTC market. This also highlights that variables have asset-specific effects with TimeGPT: improving predictions for ICP, LTC, OP, and DOT, but hindering UNI, ATOM, BCH, and ARB. Recognizing that prior research has overemphasized prediction accuracy, this study provides a more holistic and practical standard for model evaluation by integrating speed, statistical significance, and economic value. Our findings collectively underscore TimeGPTâs immense potential as a leading solution for cryptocurrency forecasting, offering a top-tier balance of accuracy and efficiency. This multi-dimensional approach provides critical, theoretical, and practical guidance for investment decisions and risk management, proving especially valuable in real-time trading scenarios.
ABSTRACT In the context of ChinaâUS trade friction, we use the TVPâVAR, SHAP, DECO, and CDB model to test the spillover volatility, hedging, safe haven, and portfolio returns of cryptocurrencies based on daily data of Bitcoin, Ethereum, Litecoin, Ripple, CSI300 Index, Shanghai Composite Index, S&P500 Index, and Nasdaq Index. The results show a significant shortâterm timeâvarying asymmetric volatility spillover effect between cryptocurrencies and the US and Chinese stock markets. Cryptocurrencies can be used as shortâterm hedging assets for the Chinese stock market. The evidence also shows no longâterm correlation between cryptocurrencies and the stock market. Therefore, in periods of volatility caused by trade friction between the two countries, such as the imposition of high tariffs, investors can regard cryptocurrencies as shortâterm hedging assets and longâterm safe haven assets to mitigate losses caused by stock market fluctuations. In addition, adding cryptocurrencies to stock index portfolios can significantly diversify risks and increase returns.
This paper investigates the dependence structure between returns and trading volumes for five major cryptocurrencies: Bitcoin, Cardano, Ethereum, Litecoin, and Ripple. Using a copula-based framework, we focus on a mixture of the Joe copula and its 90-degree rotation to capture asymmetric relationships, especially in the tails of the distribution. Our findings reveal significant upper and lowerâupper tail dependencies, suggesting that extreme trading volumes are associated with both positive and negative return extremes. The results confirm a nonlinear and asymmetric volumeâreturn relationship, which traditional linear models fail to capture.
Traditional portfolio optimization models, rooted in the meanâvariance framework of Markowitz, rely heavily on variance as a risk measure. Although theoretically elegant, this approach becomes fragile in volatile and structurally unstable markets such as cryptocurrencies, where return distributions deviate significantly from normality, cor-relations are unstable, and concentration risk emerges. These limitations have motivated the search for alternative frameworks capable of capturing uncertainty in a more flexible and distribution-free manner. Entropy, originally introduced by Shannon as a measure of information, has gradually been recognized in the financial literature as a suitable proxy for diversification and systemic uncertainty. To address the shortcomings of variance-based models, this paper introduces the Weighted Shannon Entropy (WSE) model as a diversification-oriented alternative. By extending the classical Shannon entropy with asset-specific informational weights, the WSE framework provides additional flexibility for modeling heterogeneous asset char-acteristics, such as liquidity, informational value, or perceived reliability. Using the principle of maximum entropy and the method of Lagrange multipliers, we derive ex-ponential-form solutions for portfolio weights that naturally discourage concentration, ensure balanced allocations, and remain analytically tractable. The methodology is validated empirically on a portfolio of four leading cryptocurren-ciesâBitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB)âusing market data from January to March 2025. The results demonstrate that the entropy-based optimization framework produces well-diversified portfolios, robust to volatility and structural instability, and provides a distribution-free alternative to the classical meanâvariance model. Beyond its empirical performance, the WSE formulation highlights the conceptual advantage of entropy in integrating return, risk, and diversification into a single unified framework. The paper contributes both theoretically and practically: it strengthens the mathematical foundation of entropy-based portfolio selection, extends its applicability to digital asset markets, and illustrates how weighting schemes can enrich the classical Shannon measure. Future research may extend this approach to multi-period optimization, gen-eralized entropies such as Tsallis and Kaniadakis, or integration with machine learning models for dynamic portfolio management.
This study empirically analyzes the determinants of NFT (Non-Fungible Token) value in the collectible NFT market, focusing on investor types. Using structural equation modeling (SEM) and multi-group analysis, we examine the effects of rarity, number of attributes, and trading volume on NFT value, comparing differences between large-scale (whale) and small-scale (ant) investors. Analyzing over 88 thousand transaction data points for 10,000 NFTs from the Bored Ape Yacht Club (BAYC) collection, results show that NFT rarity positively influences value but negatively affects trading volume. Both the number of attributes and trading volume negatively impact on NFT value. Multi-group analysis reveals statistically significant differences in NFT value assessment between whale and ant investors. Whale investors showed a stronger preference for NFTs with higher rarity, particularly valuing the rarity of 'eyes', 'mouth', and 'earring' attributes. Conversely, ant investors showed more interest in NFTs with higher trading frequency and a greater number of attributes. This research contributes to improving the accuracy of NFT value assessment by modeling rarity as a latent variable and clarifying the impact of market dynamics and investor behavior on the structure of the NFT market. These findings provide practical implications for NFT creators, investors, and marketplace operators, and are expected to contribute to strategy formulation for the sustainable development of the NFT market in the future.
Ronald Ravinesh Kumar, Hossein Ghanbari, Peter Josef Stauvermann
The market for digital assets, and more specifically cryptocurrencies, is growing, although their adoption in small island countries remains absent. This paper explores the potential benefits of integrating cryptocurrencies into portfolios alongside stocks, with a focus on Fijiâs stock market. This is the first study on a small market like Fiji, which emphasizes the role of cryptocurrencies in portfolio management. We analyze the outcomes (returns and risks) of combining cryptocurrencies with stocks using 12 different techniques. We use monthly stock returns data of 18 companies listed on the South Pacific Stock Exchange from Aug-2019 to Jun-2025 (71 months) and nine cryptocurrencies from Sept-2019 to Jun-2025 (70 months). Our main analysis shows that only one cryptocurrency, albeit with a small exposure, consistently appears in the stock-cryptocurrency portfolios in the 12 methods. Using the return-to-risk ratio across methods as a guide, we find that the stocks-cryptocurrencies portfolio based on EQW, MinVar, MaxSharpe, MinSemVar, MaxDiv, MaxDeCorr, MaxRMD, and MaxASR offers better outcomes than the stock-only portfolios. Using high returns as a guide, we find that six out of 12 methods (EQW, MaxSharpe, MaxSort, MaxCEQ, MaxOmega, and MaxUDVol) support the stocks-cryptocurrencies portfolios. Portfolios satisfying both conditions (high return-risk ratio and high return) are supported by the EQW and MaxSharpe portfolios. The consistency of assets in both stock and stockâcryptocurrency portfolios is further confirmed by 24-month out-of-sample forecasts and Monte Carlo simulations, although the latter supports small exposures in two out of the nine cryptocurrencies. Based on the results, we conclude that a small exposure to certain cryptocurrencies can strengthen diversification and improve potential returns.
Accreditation has historically played a central role in securities regulation, seeking to balance investor protection, market access, and capital formation. Traditionally, regulatory frameworks have relied on wealth or income thresholds as proxies for investor sophistication, premised on the assumption that individuals with greater financial resources are better equipped to manage risk and obtain professional advice. However, in rapidly evolving crypto-asset markets, these wealth-based criteria have become increasingly misaligned with market realities. Such thresholds frequently exclude technically proficient but less affluent participants, thereby perpetuating inequality and conflicting with the inclusive ethos of digital finance. Moreover, these criteria have failed to prevent significant losses among wealthy accredited investors, as evidenced by the collapses of Terra-Luna, Three Arrows Capital, and FTX. Competence-based frameworks are still underdeveloped, unevenly applied, and can become overly formal, while traditional disclosure rules do not fully address the technical and behavioral challenges of decentralized finance. This article takes a critical look at accreditation in crypto-asset markets, drawing on legal, empirical, and normative analysis. By comparing the United States, European Union, Singapore, and Russia, and examining cases like the ICO boom, Singaporeâs regulatory sandboxes, and the Terra-Luna and FTX collapses, the article shows that wealth-based accreditation falls short in fairness and effectiveness. It proposes a hybrid approach that combines competence assessments, crypto-specific disclosure, prudential safeguards, regulatory sandboxes, and international cooperation. This article contends that reforming accreditation constitutes a fundamental transformation in the approach to investor protection, advancing principles of fairness, legitimacy, and systemic robustness. By introducing a hybrid framework grounded in fairness and empirical evidence, the article contributes to policy discourse and informs scholarly understanding of the evolution of financial regulation in the context of digital innovation.
This paper proposes a cryptocurrency portfolio trading system (CPTS) that optimizes trading performance in the cryptocurrency futures market by leveraging reinforcement learning and timeframe analysis. By employing the advantage actorâcritic (A2C) algorithm and analysis of variance (ANOVA) portfolios are constructed over multiple timeframes. Data corresponding to the trade of 18 major cryptocurrencies on Binance Futuresââbetween January 2022 and December 2023ââare used to show that trading strategies can be effectively categorized into those with high-frequency (10, 30, and 60 min) and low-frequency (daily) timeframes. Empirical results demonstrate statistically significant differences in returns between these timeframe groups, with major cryptocurrencies (e.g., Bitcoin and Ethereum) exhibiting higher returns in high-frequency trading (16â17%) than in daily trading (6â7%) during training. Performance evaluation during the test period revealed that the low-frequency group achieved a 43.06% average return, significantly outperforming the high-frequency group (5.68%). The ANOVA results confirm that both the frequency type and portfolio selection significantly influence trading performance at the 5% significance level. This study offers a novel approach to cryptocurrency trading that considers the distinct characteristics of different timeframes. The effectiveness of combining reinforcement learning with statistical analysis for portfolio optimization in highly volatile cryptocurrency markets is demonstrated.
Cryptocurrency markets are highly volatile and influenced by both price trends and market sentiment, making effective portfolio management challenging. This paper proposes a dynamic cryptocurrency portfolio strategy that integrates technical indicators and sentiment analysis to enhance investment decision-making. Market momentum is captured using the 14-day Relative Strength Index (RSI) and Simple Moving Average (SMA), while sentiment signals are extracted from news articles with VADER and further validated using the Google Gemini large language model. These signals are incorporated into expected return estimates and used in a constrained mean-variance optimization framework. Backtesting across multiple cryptocurrencies shows that the integrated approach outperforms traditional benchmarks, including momentum strategy, Bitcoin Long-Short strategy, and an equal-weighted portfolio, achieving stronger risk-adjusted returns and more consistent cumulative growth. Furthermore, comparing the sentiment-only and technical-only strategies shows that incorporating sentiment information alongside technical indicators can lead to more consistent performance gains. However, the strategies exhibit substantial drawdowns that coincide with known periods of market stress, indicating that additional risk-management components are required to improve stability.
Independent Algorithmic Trading Consultant and Quantitative Researcher serving international financial institutions Los Angeles, USA, Maksim Baradziuk
The study is devoted to identifying and analyzing the synergistic interaction between the theoretical principles of behavioral finance and applied methodologies for developing high-r eturn algorithmic strategies in the digital asset segment. In conditions where the efficient market hypothesis demonstrates limitations in its applicability, especially in environments with increased volatility and underdeveloped infrastructureâsuch as cryptocurrency markets and decentralized finance (DeFi) ecosystemsâbehavioral biases emerge as important determinants of market inefficiency. The paper presents a framework that combines the targeted exploitation of cognitive patterns, including the disposition effect and the phenomenon of herd behavior, with the application of advanced technological solutions. Based on four original case studiesâranging from the development of a proprietary backtesting mechanism incorporating elements of chaotic process modeling to the construction of a predictive risk management system for DeFiâthe practical implementation of the proposed approach is demonstrated. The results obtained confirm the superiority of the hybrid architecture over traditional methods: from effectively reducing crash risk in DeFi carry trade strategies to maintaining portfolio resilience under market stress conditions and generating ultra-high returns (CAGR exceeding 200% with MDD of 30%). The studyâs findings reinforce the validity of the adaptive markets hypothesis and confirm the applied value of the synthetic methodology for modern algorithmic trading. The information reflected in the study will be of interest to asset managers, quantitative fund specialists, and researchers focused on creating next-generation algorithms.
Among the plethora of literature on interlinkages in markets, more focus has been on peripheral factors. This study attempts to fill this gap by exploring volatility as driver for interlinkages between Bitcoin, Ethereum, Tether, USD-Coin, Binance Coin (BNB), and the crypto-volatility-index (CVI) from April 2019 to August 2022. Using various wavelet techniques, the study depicts significant interlinkages across short-term, medium-term, and long-term horizons, with relatively stronger interlinkages in the long term. The findings confirm that while CVI does not drive these interlinkages, Ethereum, Bitcoin, and CVI play dominant roles in the short, interim, and medium-term periods, respectively, offering new insights into the dynamism of cryptocurrency markets.