Purpose The study aims to examine the role of cryptocurrency, specifically Bitcoin, as an asset and a currency. Design/methodology/approach The dynamic conditional correlation-generalised autoregressive conditional heteroskedasticity model was used to assess the role of Bitcoin as an asset. The study assesses the dynamic correlation between Bitcoin, bonds, Gold, the S&P 500 and crude oil in the extreme market events. A theoretical approach was used to evaluate Bitcoin on the functions of money. Findings The study found that cryptocurrency functions more like an asset, as it does not yet fulfil the role of money and still has a long way to go on this front. As an asset, cryptocurrency plays an effective role as a diversifier in the case of gold, as well as a weak safe hedge and a weak safe haven in the case of bonds. In the case of the S&P 500, Bitcoin plays the role of a diversifier, whereas it plays the role of a diversifier and a weak safe haven for crude oil investments. Practical implications We suggest that Bitcoin be included solely as a diversifier within a portfolio of traditional assets and that a cautious investment approach be adopted, considering its volatile nature. For regulators, we emphasise the necessity of promoting the innovative aspects of cryptocurrencies, particularly regarding cross-border transactions. Originality/value The study provides evidence about the dynamics of cryptocurrency markets, indicating that even after the pandemic, cryptocurrency acts mainly as a diversifier and does not yet perform the functions of money.
The decentralized finance ecosystem has fundamentally transformed traditional financial paradigms by eliminating intermediaries and enabling permissionless financial services through smart contracts deployed on blockchain networks. However, the explosive expansion has simultaneously exposed critical vulnerabilities in existing quality assurance methodologies, which were originally designed for centralized systems with predictable failure modes and controlled environments. Traditional quality assurance approaches rely heavily on static testing protocols, periodic audits, and human-mediated verification processes that prove fundamentally incompatible with the dynamic, autonomous nature of DeFi ecosystems. The inherent characteristics of DeFi platforms create a unique risk landscape that demands innovative approaches to quality assurance, particularly given the complex interconnected protocol dependencies across major DeFi applications. This article introduces a novel dynamic risk-adaptive quality assurance framework specifically engineered for DeFi platforms that transcends traditional static analysis by implementing a self-adjusting architecture capable of continuously monitoring, evaluating, and responding to emerging threats in real-time. The framework integrates artificial intelligence-driven risk prediction algorithms with behavioral analytics to create a comprehensive defense mechanism that evolves alongside the threat landscape. Through establishing dynamic risk thresholds and implementing automated response protocols, this system represents a paradigm shift toward autonomous, intelligent quality assurance in decentralized financial ecosystems, addressing critical security challenges through four interconnected layers, including data ingestion, AI-driven risk prediction, dynamic threshold management, and automated response mechanisms.
Abdullah A. Aljughaiman, Mosab I. Tabash, Suzan Sameer Issa, Abdulateif A. Almulhim
Most prior studies explain cross-country volatility interconnectedness without accounting for exogenous global uncertainty factors that influence equity returns. This study is the first to explore how major global uncertainty indicators such as U.S. and European financial market uncertainty indices (CBOE volatility index (VIX), VSTOXX-50), Global Financial Stress Indices (FSI) and Bitcoin Sentiment Indices (BSI) transmit shocks to the conditional volatility of Gulf Cooperation Council (GCC) stock markets. Using a novel ‘Extended Joint’ time-varying parameter vector autoregression (TVP-VAR) connectedness framework, the analysis addresses rolling-window limitations, enhances robustness to outliers, accommodates structural shifts and explains the shock transmission mechanism for the overall investment horizon. To capture transitory (short-term) and enduring (long-term) shock transmission channels from global uncertainty indicators toward the GCC financial system, a frequency-domain TVP-VAR is also employed. Furthermore, for the portfolio optimization, we also employ the hedge ratio and optimal portfolio weight strategy based on the DCC-GARCH-t copulas. Findings reveal that the conditional volatility of equity markets in Oman, Qatar, Saudi Arabia and the UAE is more sensitive to shocks from global uncertainty indicators such as VIX, VSTOXX-50 and the FSI, while Bahrain’s market shows relatively lower exposure. Kuwait’s equity market volatility exhibits the highest long-term sensitivity to FSI, VIX and VSTOXX-50, whereas the UAE demonstrates the highest sustained exposure to VIX and VSTOXX-50. Results from the DCC-GARCH-t copula model indicate that in stable periods (pre-COVID-19), optimized portfolio allocations significantly improved diversification, reducing risk by up to 83%. However, during financial stress events like COVID-19, hedge ratio strategies provided more effective risk mitigation, with reductions ranging from 3% to 43%.
The rise of cryptocurrencies over the past decade has transformed the global financial landscape, introducing new paradigms in investment, value storage, and monetary exchange. This study investigates the role of cryptocurrencies—specifically Bitcoin (BTC) and Ethereum (ETH)—as alternative investment assets within modern portfolio frameworks. As digital currencies continue to gain legitimacy and acceptance among retail and institutional investors, it becomes imperative to examine their financial performance, volatility characteristics, and correlation with conventional asset classes such as equities, bonds, and commodities. This research adopts a hybrid methodological approach, combining rigorous quantitative analysis with qualitative review. Using historical market data from 2015 to 2024, it evaluates key performance indicators such as average returns, standard deviation, Sharpe and Sortino ratios, Value at Risk (VaR), and maximum drawdown. It further explores the utility of cryptocurrencies in enhancing portfolio efficiency through diversification benefits, while also considering risk mitigation through dynamic asset allocation and rebalancing. The study extends beyond price metrics to include macroeconomic factors, such as inflation trends and monetary policy shifts, which influence crypto markets. It also addresses behavioral finance phenomena—including herd behavior, market sentiment, and media impact—that contribute to the observed volatility and price surges. The emergence of decentralized finance (DeFi), stablecoins, and central bank digital currencies (CBDCs) are also discussed to contextualize the evolving ecosystem and its implications for future investment strategies. Key findings indicate that while cryptocurrencies have historically outperformed traditional assets in terms of absolute returns, they also exhibit significantly higher volatility and downside risk. Despite these risks, their low to moderate correlation with conventional financial instruments enhances their value as diversification tools in modern portfolios. However, the study cautions that this benefit may diminish during times of extreme market stress when cross-asset correlations tend to rise. Moreover, the research highlights critical regulatory, technological, and environmental challenges associated with crypto adoption, including inconsistent global regulations, concerns over energy-intensive proof-of-work systems, and vulnerabilities in smart contracts. These factors underscore the need for robust governance frameworks and investor education to support sustainable growth in the digital asset market. In conclusion, the paper asserts that cryptocurrencies can serve as high-risk, high-reward components of a diversified portfolio, particularly for investors with higher risk tolerance and a long-term investment horizon. The future integration of cryptocurrencies into mainstream finance will depend largely on regulatory clarity, technological innovation, and the maturation of supporting infrastructure such as custody services, derivative markets, and institutional-grade investment vehicles
This study compares two approaches for measuring Conditional Value-atRisk (CoVaR), emphasizing the role of high-frequency intraday data in assessing systemic risk within financial systems. The first approach, AB CoVaR, estimates the risk of an asset Y conditional on another asset X being exactly at its Value-at-Risk (VaR) threshold. In contrast, the GE CoVaR refines this measure by capturing the risk of Y when X exceeds its VaR threshold, thereby accounting for more extreme scenarios and larger potential losses. To estimate these CoVaR measures, we employ high-frequency data sampled at five-minute intervals from major cryptocurrencies, including Bitcoin, Ethereum, Ripple, Solana, and Binance Coin. The results indicate that the GE CoVaR approach systematically yields higher risk estimates and exhibits superior predictive performance when applied to intraday data. Moreover, the analysis reveals strong interconnectedness among cryptocurrency returns. Bitcoin and Ethereum emerge as the primary sources of systemic risk, whereas Solana and Binance Coin are the most heavily affected assets. These findings underscore the granular risk dynamics captured through intraday analysis.
Abstract Despite its importance, there has been little research on the relationship between Bitcoin’s risk and returns. Therefore, it is necessary to investigate the risk–return trade-off of Bitcoin. In the existing limited literature, a negative risk–return relationship in Bitcoin for high-frequency intraday time-series data has been reported. In this paper, we use lower time–frequency data and suitable models for the data frequency to examine the risk–return trade-off of Bitcoin. Specifically, this paper examines the time-series volatility risk–return trade-off of Bitcoin using standard Markov switching (MS) and MS–GARCH models with weekly Bitcoin data from 2010 to 2024. Consequently, the study reveals several new findings. Firstly, the volatility risk–return trade-off relationship is identified for Bitcoin’s log returns. Secondly, the risk–return trade-off is also found for Bitcoin’s simple returns. Thirdly, the risk–return trade-off is uncovered for Bitcoin’s risk premiums as well. Fourthly, the study shows that the risk–return trade-off relationships for Bitcoin’s log returns, simple returns, and risk premiums hold true for all business days from Monday to Friday, indicating the robustness of the results. Furthermore, the study presents significant interpretations, implications, and discussion. We emphasize that we have discovered positive weekly risk–return relationships for Bitcoin using Markov switching models for the first time. This demonstrates the novelty of our work.
Purpose This study examines the performance of pair trading strategy in the cryptocurrency market under three statistical approaches including distance, cointegration and a hybrid method combining both distance and cointegration approaches. Design/methodology/approach The research uses daily, 4-h, 1-h, 15-min and 5-min data from the top 50 cryptocurrencies (by market capitalization) listed on Binance during three distinct periods: the bullish period of 2020, the stable period of 2021 and the bearish period of 2022. To perform a sensitivity analysis of the model, four approaches were implemented. First, both fixed and dynamic thresholds were applied across all three methods to assess their impact on trading results. Second, three standard deviations of 1.44, 1.65 and 2 were used, representing the coverage of normal data points in 85%, 90% and 95% of the time, respectively, to evaluate their influence on model profitability. Third, three different exit thresholds were employed to determine the extent to which changes in trade closure thresholds affect profitability. Fourth, the effect of the number of pairs in the portfolio on the model’s profitability was examined. Findings The findings from these approaches highlight the inefficiency of the cryptocurrency market and demonstrate the profitability of pair trading across various time frames, particularly in high-frequency time frames such as 15-min and 5-min intervals. Moreover, the results show that using a fixed threshold significantly outperforms a dynamic threshold in terms of both returns and Sharpe ratio. Additionally, the findings indicate a positive impact of altering the entry thresholds, exit thresholds and the number of pairs in the portfolio on the profitability of the models. Practical implications Due to the increasing attention to cryptocurrencies in investment management, the proposed model and the results of this study can be significantly used by cryptocurrency market traders, portfolio managers and fintech to achieve significant returns along with the increase in market liquidity. Originality/value This article has used pair trading strategies in the cryptocurrency market as a form of high-frequency trading for the first time. In addition, this article has proposed a hybrid approach based on the combination of distance and cointegration criteria to increase the efficiency of pair detection in the cryptocurrency market. It has evaluated the pair trading strategy in the form of different entry and exit criteria for a cryptocurrency.
Purpose (1) How can high-frequency data be utilized more effectively to identify and extract various risks in the cryptocurrency market? (2) Do the risk characteristics of different cryptocurrencies exhibit consistency or variability across multiple dimensions? (3) Based on these risk characteristics, how can more precise risk prevention and hedging strategies be provided to investors? Design/methodology/approach (1) The threshold optimal detection (TOD) model can accurately separate jump and continuous behaviors by setting appropriate threshold values, effectively identifying extreme price fluctuations in the market. (2) The method for separating trend and cyclical behaviors can be achieved through filter design and application. This approach effectively distinguishes between long-term and short-term fluctuations in the cryptocurrency market, enabling a clearer analysis of market risks across different time scales. (3) We use linear regression to estimate the sensitivity of cryptocurrency returns to different risk factors, represented by the β coefficient. (4) The time-frequency domain characteristics of wavelet coherence analysis allow for simultaneous examination of risk frequencies in the cryptocurrency market, providing a more comprehensive understanding of market behavior. Findings First, by integrating high-frequency data with multidimensional risk decomposition techniques, this study systematically identifies and analyzes various risk features within the cryptocurrency market, enriching the existing literature on high-frequency volatility and risk identification. Second, this paper innovatively decomposes continuous risk into trend risk and cyclical risk, providing a more refined framework for managing market volatility risks. Finally, the paper proposes differentiated response strategies tailored to various risk characteristics, particularly in jump risk management, offering practical guidance on the use of derivatives such as options to provide actionable solutions for investors. Originality/value This paper proposes a multidimensional risk extraction and analysis method by examining high-frequency data of nine major cryptocurrencies from December 2020 to July 2024. It not only explores the characteristics of jump risk and continuous risk but also further decomposes continuous risk into trend risk and cyclical risk using filtering techniques, revealing the heterogeneous performance of different cryptocurrencies in both long-term and short-term volatility. This multidimensional risk analysis allows for a more comprehensive capture of various market fluctuation patterns, providing investors and risk managers with more effective response strategies.
Bitcoin and other cryptocurrency returns show higher volatility than equity, bond, and other asset classes. Increasingly, researchers rely on machine learning techniques to forecast returns, where different machine learning algorithms reduce the forecasting errors in a high-volatility regime. We show that conventional time series modeling using ARMA and ARMA GARCH run on a rolling basis produces better or comparable forecasting errors than those that machine learning techniques produce. The key to achieving a good forecast is to fit the correct AR and MA orders for each window. When we optimize the correct AR and MA orders for each window using ARMA, we achieve an MAE of 0.024 and an RMSE of 0.037. The RMSE is approximately 11.27% better, and the MAE is 10.7% better compared to those in the literature and is similar to or better than those of the machine learning techniques. The ARMA-GARCH model also has an MAE and an RMSE which are similar to those of ARMA.
The social need to transform the global monetary and financial system, which is at the stage of rapid self-destruction against the background of growing economic and digital inequality, global challenges, structural shifts and polycrisis revealed the stability of the cryptocurrency industry, which manifested itself in the public acceptance of cryptocurrency assets both as a financial product and as a new ideological doctrine, in accordance with the theory of diffusion of innovation. The impact of the cryptocurrency ecosystem modification on the configuration of the global monetary and financial system has become the subject of this study. The authors analyzed panel data, which is based on 79 socio-cultural, political, demographic and economic indicators in dynamics for 2014—2024. As a result, individual factors have been identified that stimulate the smart society to recognize cryptocurrency realities not from a technological basis, but from the perspective of unique consumer and functional properties. Emphasis is placed on the specificity of the formation of the cryptocurrency landscape: the bidirectional world movement “retail users ↔ institutional players”; the paradoxical effect of tight regulation; conflict between economic reality and “beliefs”, etc. It has been established that the speed and depth of the cryptocurrency assets adoption by society is variable to a greater extent not from objective factors, but from subjective characteristics that affect decision-making and express the need for a new configuration of the global monetary and financial system. A breakthrough direction of socio-economic development is the ideology and design adaptation of the cryptocurrency channel of cross-border money transfers, which ensures the transition to a human-centered ecosystem of a multipolar order with a unique currency transfer standard.
Predicting the price of Bitcoin is crucial, primarily because of the market’s rapid volatility and non-linear environment. For enhanced prediction of the price of Bitcoin, this research proposed a novel interpretable hybrid technique that combines long short-term memory (LSTM) networks with convolutional neural networks (CNN). Deep variational autoencoders (VAE) are used in the stage of preprocessing to determine noticeable patterns in datasets by learning features from historical Bitcoin price data. The CNN-LSTM model additionally implies Shapley additive explanations (SHAP) to promote interpretability and clarify the role of various features. For better performance, the methodology used data cleaning, preprocessing, and effective machine-learning techniques. The hybrid CNN + LSTM model, in collaboration with VAE, obtains a mean squared Error (MSE) of 0.0002, a mean absolute error (MAE) of 0.008, and an R-squared (R2) of 0.99, based on the experimental results. These results show that the proposed model is a good financial forecast method since it effectively reflects the complex dynamics of primary changes in the price of Bitcoin. The combination of deep learning and explainable artificial intelligence improves predictive accuracy as well as transparency, thus qualifying the model as highly useful for investors and analysts.
A common assumption in cryptocurrency markets is a positive relationship between total-value-locked (TVL) and cryptocurrency returns. To test this hypothesis we examine whether the returns of TVL-sorted portfolios can be explained by common cryptocurrency factors. We find evidence that portfolios formed on TVL exhibit returns that are linear functions of aggregate crypto market returns, that is they can be replicated with appropriate weights on the crypto market portfolio. Thus, strategies based on TVL can be priced with standard asset pricing tools. This result holds true both for total TVL and a simple TVL measure that removes a number of ways TVL may be overstated.
Purpose This study aims to examine Bitcoin’s demand and price dynamics as it transitions from a growth state to a mature state, focusing on user base expansion and inventory levels. It refines valuation models and financial strategies by analyzing Bitcoin’s shift from network-driven asset characteristics to commodity-like price behavior, offering insights for regulatory oversight. Design/methodology/approach Using the Pruned Exact Linear Time algorithm to identify regime shifts, instrumental variable (IV) regression models to address endogeneity and derivatives data to estimate convenience yield and implied volatilities, the study analyzes blockchain and market-level data from 2013 to 2020. Five hypotheses on Bitcoin’s demand, returns, inventory effects, convenience yield and implied volatility are tested. Findings In the growth state, Bitcoin demand rises with user base expansion, with 100 unique users increasing demand by 0.23%. In the mature state, inventory levels negatively impact returns, with a 133-bitcoin increase lowering returns by 1 basis point. Convenience yields decline with inventory, while implied volatility slopes increase, confirming Bitcoin’s commodity-like behavior. Research limitations/implications Findings rely on historical data and future research can explore similar patterns in other cryptocurrencies. Blockchain data limitations, such as address clustering and transaction anonymity, may impact results. Practical implications Results provide insights for traders, risk managers and policymakers. Portfolio managers can align investments with Bitcoin’s lifecycle, while derivative traders can leverage insights into convenience yields and implied volatility. Originality/value This study empirically tests Bitcoin’s transition from a growth-driven financial asset to a commodity-like asset. It integrates network effect and commodity pricing models, offering a unified framework for understanding Bitcoin’s lifecycle.
This study investigates the price dynamics of Bitcoin, a highly volatile and speculative digital asset. Using daily closing price data from January 2023 to January 2024, we apply the Bates model, which combines stochastic volatility with jump-diffusion processes, to better capture both continuous fluctuations and sudden, large price changes in the market. The model parameters are calibrated using historical data and evaluated through Monte Carlo simulation with 10,000 generated price paths over a 31-day forecast horizon. The results demonstrate a strong short-term predictive performance, with a Mean Absolute Percentage Error (MAPE) of 4.32%. This indicates that the Bates model can capture both volatility clustering and abrupt shifts, which are characteristic of Bitcoin. The findings suggest that this approach provides a valuable tool for risk management and investment decision-making in highly uncertain and dynamic markets.
A central challenge in economics and artificial intelligence is explaining how financial behaviors-such as credit, insurance, and trade-emerge without formal institutions. We argue that these functions are not products of institutional design, but structured extensions of a single behavioral substrate: reciprocity. Far from being a derived strategy, reciprocity served as the foundational logic of early human societies-governing the circulation of goods, regulation of obligation, and maintenance of long-term cooperation well before markets, money, or formal rules. Trade, commonly regarded as the origin of financial systems, is reframed here as the canonical form of reciprocity: simultaneous, symmetric, and partner-contingent. Building on this logic, we reconstruct four core financial functions-credit, insurance, token exchange, and investment-as expressions of the same underlying principle under varying conditions. By grounding financial behavior in minimal, simulateable dynamics of reciprocal interaction, this framework shifts the focus from institutional engineering to behavioral computation-offering a new foundation for modeling decentralized financial behavior in both human and artificial agents.
Zaäfri A. Husodo, Md. Bokhtiar Hasan, Humaira Tahsin Rafia, Masagus M. Ridhwan · 6 authors
This study investigates the interconnected dynamics among diverse digital currencies, specifically focusing on risk-adjusted returns, tail risks, dynamic spillovers, and portfolio implications. Unlike prior research, which typically examines individual digital currency classes separately or in limited combinations, our study integrates six distinct classes of digital currencies, namely Islamic gold-backed cryptocurrencies, green cryptocurrencies, gold-backed stablecoins, non-fungible tokens (NFTs), decentralized finance (DeFi) assets, and conventional cryptocurrencies, enabling direct comparisons of risk-return dynamics and systemic interdependencies. Using Value at Risk (VaR), Conditional Value at Risk (CVaR), quantile-based Vector Autoregression (Quantile VAR), and network connectedness analysis, we provide nuanced insights into the behavior of these assets across various market conditions (bullish, bearish, and normal states). Our results demonstrate that conventional cryptocurrencies and DeFi assets consistently deliver positive risk-adjusted returns, whereas Islamic gold-backed cryptocurrencies exhibit notably higher downside risks and negative performance. Spillover analysis reveals pronounced connectedness, particularly in extreme market states, with conventional cryptocurrencies identified as primary transmitters of market shocks and gold-backed stablecoins and Islamic gold-backed cryptocurrencies as recipients. Our findings underscore significant diversification opportunities offered by pairs of assets exhibiting low connectedness, especially in normal market conditions. Furthermore, portfolio optimization analysis highlights the superior hedging effectiveness and lower hedging costs associated with gold-backed stablecoins and conventional cryptocurrency pairs. This comprehensive investigation delivers critical implications for investors, suggesting informed strategies for asset allocation and risk management. Policymakers can also utilize our insights to design adaptive regulatory frameworks that address systemic risks arising from digital currency markets. ACKNOWLEDGMENT Gazi Salah Uddin gratefully acknowledges the Faculty of Economics and Business, Universitas Indonesia, for the academic appointment as Adjunct and Visiting Professor, and expresses sincere appreciation for the institutional support and research facilities extended during his residency, which significantly contributed to the completion of this work.
General background: The increasing integration of digital technologies has transformed global financial systems, with cryptocurrencies, especially Bitcoin, emerging as prominent financial instruments. Specific background: Amid widespread adoption by institutions and individuals, Bitcoin has garnered attention for its potential to influence traditional financial markets, particularly during periods of global uncertainty such as the COVID-19 pandemic. Knowledge gap: While much has been discussed about the theoretical influence of cryptocurrencies, empirical evidence on their actual impact on global financial indices remains inconclusive. Aims: This study investigates the effect of Bitcoin trading volume and the COVID-19 pandemic on a composite index comprising advanced (S&P 500), emerging (KLSE), and developing (DZ) market indices from July 2018 to December 2022. Results: Using a fixed-effects panel data model, the findings reveal that past market performance significantly predicts current performance, while Bitcoin trading volume and the pandemic show no statistically significant impact. Novelty: The study uniquely combines market classifications and utilizes a composite index to empirically isolate the influence of Bitcoin across diverse economies. Implications: These results suggest that, despite Bitcoin's rising prominence, its direct influence on global financial markets may be limited in the short term, underscoring the need for continued investigation as regulatory frameworks and adoption rates evoHighlight : Minimal Impact: Bitcoin trading volume and the COVID-19 pandemic had no statistically significant effect on global financial market indices (2018–2022). Strong Market Correlation: Global financial indices showed strong interdependence, reflecting synchronized market behavior. Future Outlook: Despite current findings, evolving crypto regulations and technologies may alter their financial market influence. Keywords : Cryptocurrencies, Bitcoin, Trading Volume, COVID-19, Financial Indices
Blockchain technology has emerged as a revolutionary force in modern finance, significantly impacting financial market efficiency by enhancing transparency, reducing transaction costs, and eliminating intermediaries. However, its overall effect on market efficiency remains a subject of academic debate. This study conducts a bibliometric and network analysis to systematically assess the evolution of blockchain research in financial markets, highlighting key publication trends, influential authors, leading institutions, and dominant research themes. Using Scopus as the primary database, a structured search strategy identified 3,054 high-quality articles published between 2005 and 2025, focusing on Business, Management, and Accounting (BUSI) and Economics, Econometrics, and Finance (ECON). VOSviewer was employed to map research collaborations, co-authorship structures, and keyword co-occurrences, providing a comprehensive understanding of the intellectual development in this field. Findings reveal a sharp increase in blockchain-related financial research, particularly post-2016, driven by the expansion of decentralized finance (DeFi) and institutional interest in digital assets. The study identifies Corbet, S., and Yarovaya, L., among the most influential authors, while leading institutions include Dublin City University and Lebanese American University. China, the United States, and India dominate research output, reflecting global interest in blockchain's financial implications. The analysis further uncovers key thematic clusters, including market efficiency, liquidity, and regulatory challenges, while also highlighting blockchain’s emerging applications in sustainable finance and artificial intelligence-driven investment strategies. Despite significant academic contributions, gaps persist, particularly in empirical assessments of blockchain’s long-term impact on market stability, regulatory alignment, and integration with traditional financial systems. Future research should focus on addressing these gaps by exploring cross-border regulatory frameworks, expanding studies beyond cryptocurrencies to tokenized assets, and investigating the role of artificial intelligence in blockchain-based financial solutions. By advancing these research directions, scholars and policymakers can develop a structured approach to blockchain adoption, ensuring its long-term sustainability and effectiveness in global financial markets.
Financial market efficiency is significantly influenced by the availability and quality of information, with information asymmetry posing a major barrier to optimal market functioning. This article reviews the role of data science in mitigating information asymmetry and enhancing market efficiency, comparing traditional approaches with modern data-driven methods (e.g., machine learning, NLP, and blockchain). It systematically evaluates traditional approaches used to measure and mitigate information asymmetry and highlights their limitations in accurately capturing complex market dynamics. Traditional approaches such as statistical testing, price behavior analysis, and asset pricing models provide fundamental insights but often fail to capture complex, non-linear market dynamics, such as adverse selection, moral hazard, and asset mispricing, due to their reliance on historical data and linear assumptions. In contrast, data science has revolutionized financial market analysis by combining machine learning, natural language processing (NLP), big data analytics, and blockchain technology to solve information imbalances. It enables real-time analysis of unstructured data, improves predictive modeling, and enhances transparency through sentiment analysis, algorithmic trading, and decentralized ledgers. It concludes that integrating data science with traditional finance theory significantly reduces information gaps, offering policymakers and investors tools to foster fairer, more efficient markets. This bridges theoretical finance with computational innovations, demonstrating how data science addresses longstanding limitations in measuring and improving market efficiency.