Traditional portfolio optimization techniques predominantly rely on the classical meanâvariance framework introduced by Markowitz, which focuses on balancing expected returns against risk, typically measured by variance. However, in volatile and structur-ally unstable markets such as cryptocurrencies, this approach often fails to capture the full spectrum of uncertainty and diversification potential. This paper introduces an al-ternative methodology grounded in entropy, a fundamental concept in information theory that quantifies uncertainty and disorder. By incorporating entropy into the portfolio optimization process, we offer a more generalizable, distribution-free approach that enhances diversification and resilience.We develop and analyze three distinct en-tropy-based models: the maximum Shannon entropy model, the second-order entropy (Tsallis) model, and the maximum weighted Shannon entropy model. These formula-tions extend the traditional meanâvariance approach by integrating nonlinear uncer-tainty measures, enabling a richer representation of investor preferences and asset in-terdependencies. Analytical solutions to the proposed models are derived using the method of Lagrange multipliers, ensuring mathematical rigor and interpretability.The proposed models are empirically validated using a portfolio composed of four leading cryptocurrenciesâBitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB)âwith market data from January to March 2025. The case studies demonstrate how entropy-based optimization leads to well-diversified portfolios, robust under market turbulence and heavy-tailed return distributions. Notably, the models facilitate dynamic adjustments in asset allocation in response to shifts in returnârisk characteristics and entropy levels. This study contributes to the ongoing generalization of portfolio theory by positioning entropy as both a diversification enhancer and a structural risk measure. It provides theoretical insight, practical tools for asset allocation in high-volatility environments, and paves the way for future research in entropy-driven financial optimization frameworks.
In this paper, we design and implement a web crawler system based on the Solana blockchain for the automated collection and analysis of market data for popular non-fungible tokens (NFTs) on the chain. Firstly, the basic information and transaction data of popular NFTs on the Solana chain are collected using the Selenium tool. Secondly, the transaction records of the Magic Eden trading market are thoroughly analyzed by combining them with the Scrapy framework to examine the price fluctuations and market trends of NFTs. In terms of data analysis, this paper employs time series analysis to examine the dynamics of the NFT market and seeks to identify potential price patterns. In addition, the risk and return of different NFTs are evaluated using the mean-variance optimization model, taking into account their characteristics, such as illiquidity and market volatility, to provide investors with data-driven portfolio recommendations. The experimental results show that the combination of crawler technology and financial analytics can effectively analyze NFT data on the Solana blockchain and provide timely market insights and investment strategies. This study provides a reference for further exploration in the field of digital currencies.
Hong Qu, Krzysztof Gogol, Florian GrĂśtschla, Claudio J. Tessone
Decentralized Finance (DeFi) lending enables permissionless borrowing via smart contracts. However, it faces challenges in optimizing interest rates, mitigating bad debt, and improving capital efficiency. Rule-based interest-rate models struggle to adapt to dynamic market conditions, leading to inefficiencies. This work applies Offline Reinforcement Learning (RL) to optimize interest rate adjustments in DeFi lending protocols. Using historical data from Aave protocol, we evaluate three RL approaches: Conservative Q-Learning (CQL), Behavior Cloning (BC), and TD3 with Behavior Cloning (TD3-BC). TD3-BC demonstrates superior performance in balancing utilization, capital stability, and risk, outperforming existing models. It adapts effectively to historical stress events like the May 2021 crash and the March 2023 USDC depeg, showcasing potential for automated, real-time governance.
Portfolio optimization is a fundamental problem in financial theory, aiming to balance risk and return in asset allocation. Traditional models, such as MeanâVariance optimization, are effective, but often fail to account for diversification adequately. This study introduces the MeanâVarianceâEntropy (MVE) model, which integrates Tsallis entropy into the classic MeanâVariance framework to enhance portfolio diversification and risk management. Entropy, specifically second-order entropy, penalizes excessive concentration in the portfolio, encouraging a more balanced and diversified allocation of assets. The model is applied to a portfolio of five major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Solana (SOL), Cardano (ADA), and Binance Coin (BNB). The performance of the MVE model is compared with that of the traditional MeanâVariance model, and results demonstrate that the entropy-enhanced model provides better diversification, although with a slightly lower Sharpe ratio. The findings suggest that while the entropy-adjusted model results in a slightly lower Sharpe ratio, it offers better diversification and a more resilient portfolio, especially in volatile markets. This study demonstrates the potential of incorporating entropy into portfolio optimization as a means to mitigate concentration risk and improve portfolio performance. The approach is particularly beneficial for markets such as cryptocurrency, where volatility and asset correlations fluctuate rapidly. This paper contributes to the growing body of literature on portfolio optimization by offering a more diversified, robust, and risk-adjusted approach to asset allocation
Samet GĂźnay, Emrah Ä°smail Ăevik, Mehmet Fatih BuÄan, Sel DibooÄlu ¡ 5 authors
Abstract Utilizing blockchain technology is transforming traditional business practices into a new paradigm, giving rise to what we refer to as blockchained models. This paper uses wavelet coherence analysis to identify the connectedness of blockchained sectoral indices with Bitcoin and the Fear and Greed Index that represents investor sentiment in the cryptocurrency market. Results show persistent and positive correlations between sector returns and investor sentiment and sectoral return series lead investor sentiment. The relationship between Bitcoin and sectoral indices is consistent for return series and suggests an in-phase (positive) relationship between these variables at all frequencies. We usually have found negative correlations for the co-movements of investor sentiment and sectoral volatility, where investor sentiment leads to sector return volatilities. The application of blockchain technology across various sectors, coupled with the proliferation of altcoins, appears to drive distinct price developments in these cryptocurrency sectors. These developments are predominantly influenced by sentimental factors, often diverging from the trends of Bitcoin.
Maninder Singh, William Bjorndahl, Gagangeet Singh Aujla, Joseph Camp
In the era of continuously increasing demand for bandwidth and revolutionary wireless technologies, efficient spectrum management is essential. This paper proposes a novel multi-tier tokenization approach for dynamic spectrum management. Leveraging the concept of heterogeneous tokenization of spectrum bands, we develop a decentralized framework based on blockchain technology that enables the sharing of spectrum among users. The spectrum space is represented by multi-planes, the first plane consists of unique spectrum bands converted into NFTs for long-term allocations, while the second plane involves subdividing these NFT spectrum bands for short-term usage by retail users through fungible tokens. The fungible tokens are dynamically traded and mapped using particle swarm optimization (PSO) to manage demand and supply. The paper presents formal models of the involved entities and algorithms for creating multi-tier tokens, dynamic token trading and demand-supply mapping using PSO. To enhance privacy, a zero-knowledge proof (ZKP) based approach is employed for user authentication. The proposed framework offers a secure, transparent, and scalable solution for spectrum management, addressing the limitations of traditional centralized approaches. Simulation results demonstrate the effectiveness of the framework in dynamic spectrum access, while providing privacy-aware and scalable solutions suitable for future wireless networks, including 6G.
This paper presents a novel approach to cryptocurrency trading by introducing a hybrid deep learning architecture that combines state-of-the-art sequence modeling techniques with reinforcement learning. Our model integrates Mamba State Space Models (SSM), Temporal Convolution Networks (TCN), and multi-head attention mechanisms to capture complex temporal dependencies in market data, while leveraging Deep Q-Network variants for optimal decision making. We implement a sophisticated signal processing pipeline with adaptive smoothing and feature fusion mechanisms, followed by a reinforcement learning framework for trading strategy optimization. The proposed architecture demonstrates superior performance in capturing market dynamics and generating robust trading signals, as validated through comprehensive backtesting on high-frequency cryptocurrency data.
Using a variety of parametric and non-parametric tests, this study investigates the price effects of one-day abnormal returns and the day-of-the-week effect in selected non-fungible token (NFT) coins. The results, based on the data of four NFT coins (Mana, Theta, Enj, and Waxp) observed from January 2018 to July 2022, show that there are differences in pricing patterns across the four NFTs. First, NFT coinsâ prices tend to exhibit contrarian movements following one-day abnormal returns, especially in Theta, in line with the overreaction hypothesis. Second, the day-of-the-week effect is significant for Mana, where prices tend to abnormally increase during the weekend. Finally, trading strategies based on price patterns identified in Theta and Mana generate abnormal profits.
Abstract We use transaction-level data from the Bitcoin exchange Mt.Gox, including over 1.4 million transactions from more than 45,000 traders, to investigate the role of technical chart patterns in the early Bitcoin market from April 2011 to September 2013. Employing a pattern recognition algorithm, we identify hourly trading signals for five major chart patterns. Buy signals of these patterns are associated with an average increase in abnormal trading volume of more than 53%. Trades executed during buy signal periods yield significantly higher average returns than those made during non-signal periods. Traders who use chart patterns more frequently are more likely to generate right-skewed return distributions, engage in more active trading, and achieve higher average roundtrip returns. Our research suggests that chart pattern trading was a crucial tool for Mt.Gox clients, highlighting the importance of technical heuristics in shaping the dynamics in a less efficient and unregulated market environment. By leveraging a comprehensive transaction dataset from a major cryptocurrency exchange, we provide unique insights into the actual trading behavior of the first Bitcoin adopters. This sets our work apart from previous studies that mainly rely on backtesting technical strategies using publicly available price data.
Suwan Long, Ying Xie, Zhengyuan Zhou, Brian M. Lucey ¡ 5 authors
This paper examines the relationship between cryptocurrency market dynamics and investor sentiment, employing advanced techniques like time-variant Granger causality and asymmetric time-varying parameter vector autoregression (TVP-VAR) frequency connectivity. We create unique sentiment analysis tools, including a custom cryptocurrency sentiment lexicon, to deeply analyze content in the cryptocurrency domain, particularly focusing on investor discussions and viewpoints. Our findings demonstrate a significant, evolving link between market sentiment and cryptocurrency movements. A key observation is that the volatility of shock transmission is tightly connected to major market events, often influenced by large-scale investors, or âwhalesâ. Our study indicates that market sentiment consistently affects both short- and long-term cryptocurrency volatility, underlining the crucial influence of investor sentiment in driving the dynamics of the cryptocurrency market. This underscores the importance of understanding investor sentiment for predicting and navigating the cryptocurrency market.
The integration of Artificial Intelligence (AI) in finance is transforming digital economics by enhancing decision-making, automating processes, and optimizing financial strategies. This book chapter explores AI-driven learning techniques, including machine learning, deep learning, and reinforcement learning, and their applications in financial markets, risk management, fraud detection, and algorithmic trading. We analyze the impact of AI on financial institutions, digital banking, and decentralized finance (DeFi), highlighting how AI enhances predictive analytics, customer experience, and regulatory compliance. Additionally, the chapter discusses the ethical and regulatory challenges of AI adoption in finance, emphasizing the need for transparency and fairness in AI-driven financial systems. By examining real-world case studies and emerging trends, this chapter provides a comprehensive overview of AI's role in shaping the future of digital economics.
The increasing interaction between the equity market and cryptocurrencies has raised concerns about volatility spillovers; however, empirical evidence about sectoral-specific spillover effects in emerging markets is scarce and hard to find. Existing research mainly concentrates on developed markets and aggregate equity indices, leaving a research gap in comprehending how sectoral indices variations impact market interactions in developing financial markets like Thailand. This article investigates the mean and volatility spillover effects between the Thai stock market and leading cryptocurrencies from April 2019 to April 2024. Applying bivariate VAR (1)-BEKK-GARCH (1,1) with an asymmetry model, this study examines the aggregate and sectoral-specific mean and volatility spillovers across major Thai stock market sectors. The findings reveal the significant mean spillover effect from cryptocurrencies to the Thai stock market with sectoral variation, while sectors such as industrials and financials exerted significant linkages, and the agricultural and food sector remains unaffected. Additionally, volatility spillovers were predominantly transmitted from the Thai equity market to cryptocurrency. Moreover, asymmetry effects were observed, with the asymmetry effects mainly transmitted from the Thai equity market to cryptocurrency. These findings provide critical insights for both individual and institutional investors on risk management and portfolio diversification while also helping policymakers with guidance on regulatory measures to mitigate systemic risks in emerging financial markets.
This paper provides the first empirical evidence of whether the introduction of US spot Bitcoin ETFs affected the returns and volatility of major cryptocurrencies. Using data from December 18, 2017 to March 15, 2024, we apply an event-study methodology within a GARCH-based framework. Our results reveal a significant effect of the introduction of spot Bitcoin ETFs on cryptocurrency returns and volatility. The analysis shows a positive impact for Bitcoin, Ethereum, and Litecoin spot price returns around the event date. The volatility of Bitcoin and Ripple spot markets decreased following the introduction of spot Bitcoin ETFs, which supports the stabilization hypothesis for these two cases. We also examine the volatility spillovers using a wavelet coherence approach, and reveal significant volatility spillovers from Grayscale Bitcoin ETF to Bitcoin futures and to a lesser extend to the Bitcoin spot market. Our findings enhance the limited understanding of the price discovery and functioning of the cryptocurrency markets, which could be useful for investors, regulators, and policymakers. ⢠Study the impact of introduction of Spot Bitcoin ETFs on the cryptocurrency market. ⢠Apply event study methodology within a GARCH framework. ⢠Find a positive impact for Bitcoin, Ethereum, and Litecoin spot price returns. ⢠Volatility of Bitcoin and Ripple decreased, supporting the stabilization hypothesis. ⢠Wavelet coherence analysis reveals volatility spillovers from Bitcoin ETF to Bitcoin futures.
Traditional portfolio optimization techniques predominantly rely on the classical meanâvariance framework introduced by Markowitz, which focuses on balancing expected returns against risk, typically measured by variance. However, in volatile and structur-ally unstable markets such as cryptocurrencies, this approach often fails to capture the full spectrum of uncertainty and diversification potential. This paper introduces an al-ternative methodology grounded in entropy, a fundamental concept in information theory that quantifies uncertainty and disorder. By incorporating entropy into the portfolio optimization process, we offer a more generalizable, distribution-free approach that enhances diversification and resilience.We develop and analyze three distinct en-tropy-based models: the maximum Shannon entropy model, the second-order entropy (Tsallis) model, and the maximum weighted Shannon entropy model. These formula-tions extend the traditional meanâvariance approach by integrating nonlinear uncer-tainty measures, enabling a richer representation of investor preferences and asset in-terdependencies. Analytical solutions to the proposed models are derived using the method of Lagrange multipliers, ensuring mathematical rigor and interpretability.The proposed models are empirically validated using a portfolio composed of four leading cryptocurrenciesâBitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB)âwith market data from January to March 2025. The case studies demonstrate how entropy-based optimization leads to well-diversified portfolios, robust under market turbulence and heavy-tailed return distributions. Notably, the models facilitate dynamic adjustments in asset allocation in response to shifts in returnârisk characteristics and entropy levels. This study contributes to the ongoing generalization of portfolio theory by positioning entropy as both a diversification enhancer and a structural risk measure. It provides theoretical insight, practical tools for asset allocation in high-volatility environments, and paves the way for future research in entropy-driven financial optimization frameworks.
By combining behavioral finance theory and big data analysis technology, this study explores the mechanism of the impact of investor sentiment on cryptocurrency market price anomalies. Based on the fusion database of traditional exchange historical market data and social media sentiment data, the research team constructed multidimensional sentiment indicators to quantify the emotional fluctuations of market participants. The research design adopts the strict data cleaning process, feature engineering processing, and the hybrid modeling method combining the traditional statistical model and the machine learning algorithm. The empirical results show that extreme optimism or pessimism is significantly associated with abnormal price events, and the predictive ability of the composite sentiment index is better than that of the single volatility index. This research reveals the transmission pathways of cognitive biases such as overconfidence and the anchoring effect in the cryptocurrency market, confirming the significant influence of irrational psychological factors on the price formation of digital assets. The findings not only deepen our understanding of the interaction mechanism between investor psychology and market behavior, but also provide an innovative analytical framework for risk management and quantitative investment strategies in the cryptocurrency field.
Purpose This study investigates herd behavior in the Fan Tokens market, comparing it with the non-fungible tokens (NFTs) and traditional cryptocurrency markets. Design/methodology/approach This study investigates herding behavior by examining the relationship between the cross-sectional dispersion of asset returns and overall market returns, utilizing five distinct model specifications. To enhance the robustness of the findings, the regressions are re-estimated using the GARCH model, ensuring more reliable parameter estimates and capturing the impact of volatility on herding behavior. Findings The analysis reveals strong evidence of herd behavior in the Fan Token market, particularly during bearish conditions, heightened volatility, and low trading volume. Positive news was found to amplify volatility more than negative news. In contrast, no statistically significant herd behavior was identified in the NFT and traditional cryptocurrency markets, where investors showed a more cautious response to market conditions. Practical implications Understanding the unique dynamics of Fan Tokens can help investors, regulators, and market participants make informed decisions and develop strategies to mitigate risks associated with herd behavior and volatility in this rapidly evolving market. Originality/value This study highlights the unique characteristics of Fan Tokens, emphasizing their strong ties to fan sentiment and sports outcomes, as well as the role of uninformed investors in shaping market dynamics. The findings contribute to the literature on digital asset markets and investor psychology, offering novel insights into this emerging asset class.
Volatility in the cryptocurrency market poses a significant challenge to traders' ability to identify patterns while using it with all those uncertainties. The traditional methods that have been the starting point now increasingly become inadequate because they depend on manual analysis, man's emotional biases, and lack the capacity for real-time data processing. This paper suggests an innovative automated trading system based on sentiment analysis and advanced AI technologies to overcome these deficiencies. The system combines natural language processing with robust backend architecture to process unstructured sentiment data from various sources for trading strategy decisionmaking. This proposed method would be seamless, efficient, and scalable to empower traders to make real-time data-driven decisions. The architecture and methodology are described with a critical analysis of traditional trading practices and the advantages offered by automation. This paper will explore the potential of how AI-driven trading systems can be used to revamp the spectrum of cryptocurrencies, so that it may be accurate, adaptable, and usercentred.
This study provides empirical evidence that cryptocurrency market movements are influenced by sentiment extracted from social media. Using a high frequency dataset covering four major cryptocurrencies (Bitcoin, Ether, Litecoin, and Ripple) from October 2017 to September 2021, we apply state-of-the-art natural language processing techniques on tweets from influential Twitter accounts. We classify sentiment into positive, negative, and neutral categories and analyze its effects on log returns, liquidity, and price jumps by examining market reactions around tweet occurrences. Our findings show that tweets significantly impact trading volume and liquidity: neutral sentiment tweets enhance liquidity consistently, negative sentiments prompt immediate volatility spikes, and positive sentiments exert a delayed yet lasting influence on the market. This highlights the critical role of social media sentiment in influencing intraday market dynamics and extends the research on sentiment-driven market efficiency.