Abstract This paper presents a systematic literature review of 137 peer-reviewed publications from 41 journals, examining the interconnectedness between cryptocurrencies and traditional financial markets. Using a rigorous three-stage methodology for study selection, we identify key research themes including spillover effects, volatility transmission, interdependence, hedge effectiveness, and safe-haven properties of cryptocurrencies. Our analysis reveals that GARCH-based models dominate early work on volatility and contagion, while more recent studies adopt advanced approaches, such as cross-quantilogram, wavelet coherence, and multifractal detrended cross-correlation, to capture non-linear, time-varying relationships without assuming stationarity. Our review offers three major contributions. First, we provide a comprehensive classification of the interconnectedness between different types of cryptocurrencies and financial markets, highlighting their evolving roles as hedges, safe havens, or diversifiers. Second, we synthesize empirical findings to show how spillovers, time-varying correlations, tail dependencies, and contagion risks intensify under major events, such as COVID-19, regulatory shifts, and geopolitical conflicts. Third, we draw attention to overlooked areas, including emerging market dynamics and macroeconomic determinants. We recommend that policymakers implement early warning systems and proactively monitor volatility and connectedness in crypto markets to reduce contagion risks and maintain financial stability. Policy frameworks should consider the unique features of crypto markets and the time-varying interlinkages between cryptos, commodities, fiat currencies, and equities. Investors, in turn, should track cryptocurrency price movements closely, as they provide valuable signals for forecasting broader market trends and improving portfolio risk management. These insights have practical implications for risk mitigation and decision-making in increasingly integrated financial systems.
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.
The FTX collapse marked a significant shock to global crypto markets, prompting concerns about systemic contagion. This paper investigates the dynamic connectedness between cryptocurrencies, DeFi tokens, and tech stocks, focusing on the systemic impact of the FTX collapse. We decompose total, internal, and external connectedness across asset groups using a time-varying parameter VAR model. The results show that post-FTX, Bitcoin and Ethereum intensified their roles as core shock transmitters, while Tether consistently acted as a volatility absorber. DeFi tokens exhibited heightened intra-group spillovers and occasional external influence, reflecting structural fragility. Tech stocks remained largely insulated, with reduced cross-market linkages. Network visualizations confirm a post-crisis fragmentation, characterized by denser internal crypto-DeFi ties and weaker inter-group contagion. These findings have important policy implications for regulators, investors, and system designers, indicating the need for targeted risk monitoring and governance within decentralized finance.
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.
The rapid rise of the prices of cryptocurrencies has intensified the need for robust forecasting models that can capture the irregular and volatile patterns. This study aims to forecast Bitcoin prices over a 15-day horizon by evaluating and comparing two distant predictive modeling approaches: the Bayesian State-Space model and Long Short-Term Memory (LSTM) neural networks. Historical price data from January 2024 to April 2025 is used for model training and testing. The Bayesian model provided probabilistic insights by achieving a Mean Squared Error (MSE) of 0.0000 and a Mean Absolute Error (MAE) of 0.0026 for training data. For testing data, it provided 0.0013 for MSE and 0.0307 for MAE. On the other hand, the LSTM model provided temporal dependencies and performed strongly by achieving 0.0004 for MSE, 0.0160 for MAE, 0.0212 for RMSE, 0.9924 for R2 in terms of training data and for testing data, and 0.0007 for MSE with an R2 of 0.3505. From the result, it indicates that while the LSTM model excels in training performance, the Bayesian model provides better interpretability with lower error margins in testing by highlighting the trade-offs between model accuracy and probabilistic forecasting in the cryptocurrency markets.
We examine how Bitcoin and Ethereum volatilities react to macroeconomic data releases from the US, Germany, and Japan before, during, and after their official announcements. Analyzing 5-minute observations from 2016 to 2023, we find that volatility responds significantly to select news categories, particularly in the pre-announcement period. US monetary policy news consistently drives volatility across all phases, with a heightened impact during the pandemic. Ethereum shows greater sensitivity to US announcements than Bitcoin but remains unresponsive to non-US news, especially before the pandemic. Our findings highlight the need to account for both pre- and post-announcement periods when evaluating the intraday price impact of macroeconomic news on cryptocurrencies. ⢠We examine the response of Bitcoin and Ethereum volatilities to macroeconomic figures. ⢠We show that volatility reacts only to a few news categories. ⢠US monetary policy news consistently affects volatility before, during, and after its release. ⢠Ethereum volatility is more sensitive to US announcements compared to Bitcoin. ⢠Ethereum exhibits less pre-announcement volatility and less sensitivity to non-US news.
This paper surveys the academic literature concerning the bubble periods in the cryptocurrency market. This study aims to understand the historical and developmental trajectory of the cryptocurrency market through its various bubble periods. This study also identifies the factors contributing to bubble formation. The study is based on the PRISMA framework for literature review. Based on the review, the cryptocurrency market experienced four major bubbles in 2011, 2013, 2017, and 2021. The enthusiasm for cryptocurrency innovation triggered the 2011 bubble. The 2013 bubble was influenced by the economic crisis that channeled funds to the cryptocurrency market due to their centralized nature. In 2017, the possibilities of Web 3.0 and altcoins increased the enthusiasm of crypto investors. The crypto winter of 2017 subsided with the rise of non-fungible tokens (NFTs), stimulating interest and driving prices in the cryptocurrency market. Specifically, speculation, media coverage, investor sentiment, herding, volatility, and coexplosivity are significant factors that trigger bubble development. Moreover, government policies and regulations can be crucial in sustaining and bursting the bubbles. This review offers a comprehensive view of academic studies on bubble periods in the cryptocurrency market. This study also provides a chronological overview of major bubble periods that have significantly influenced the market development. This study is one of the first reviews conducted to understand the development of the cryptocurrency market through bubble periods and the factors contributing to bubble formation. The study also follows the PRISMA framework for structuring the review, as the literature lacks reviews on bubble periods on the basis of this framework.
This study is the first to integrate recurrence plots, recurrence quantification analysis (RQA) and short-time Fourier Transform (STFT) to predict cryptocurrency market behaviour. Recurrence plots, RQA statistics and STFT spectrograms were calculated from return data and used as input in random forest algorithms as they are optimal tools for identifying non-linear dynamics in market data and analyse their frequency. Our optimised XGBoost algorithm provided a forecasting AUC above 76.7% and accuracy of 70% in predicting increasing or decreasing returns. This highlights the modelâs ability to support cryptocurrency investment decision-making within an interpretable machine learning framework.
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.
The cryptocurrency market, which is extremely volatile and has high price fluctuations, is transforming the financial ecosystems in the world.In contrast to traditional markets, cryptocurrencies are characterized by the unprecedented volatility due to the complicated interaction of speculative trading, regulatory changes, technological breakthroughs, and macroeconomic forces.The purpose of the current study is to build and test machine learning models to predict the price trend of cryptocurrencies, including the most popular ones, Bitcoin (BTC), Ethereum (ETH), and other top altcoins that are traded in the United States.The analysis is based on a large amount of data on historical prices at daily, hourly, and minute-by-minute intervals, including the detailed data on opening, closing, high, and low prices, and trading volumes that indicate the liquidity and the activity of investors.The most important technical indicators such as moving averages, Relative Strength Index (RSI) and Bollinger Bands are incorporated to identify the most important market signals and momentum.It uses three machine learning models, including Logistic Regression, Random Forest Classifier, and XGBoost Classifier.Directional prediction capability (upward or downward price movements) is evaluated by accuracy, precision, recall, and F1-score measures of model performance.Logistic Regression was the most accurate among the models that were tested, which highlights its comparative effectiveness in this application.The introduction of AI-based predictive analytics into cryptocurrency trading can be a great way to improve the process of decision-making by traders and institutional investors and help them comply with regulations in the U.S. financial system.This study sheds light on the transformational nature of machine learning in cryptocurrency prediction and also points out the research opportunities in the future, especially the use of deep learning models like the Long Short-Term Memory (LSTM) network in time-series analysis.
This study employs novel quantile time-frequency connectedness approach to explore the dynamic connectedness among sustainable assets (sustainable, green bond, and clean energy index), traditional assets (traditional index and crude oil), and cryptocurrency. This method assesses the impact of uncertain events on asset relationships. Findings indicate median connectedness of 36.94% in the short run and 4.81% in the long run, with short-term dynamics dominating system transmission. The traditional index is the primary transmitter of short-run shocks, while the green bond index leads in long-run shocks. Diversification across asset classes is recommended for effective hedging and optimal returns during extreme market conditions.
Blockchain technology, once limited to niche technological communities, has seen widespread global adoption in recent years, with the potential to reshape financial and social systems. Launched in July 2015, the Ethereum blockchain introduced programmable Smart Contracts. This innovation enabled the creation of user-defined crypto-assets adhering to the ERC-20 standard, supporting a wide range of decentralized applications beyond simple value transfer. We present a large-scale, temporally annotated dataset of ERC-20 token transactions recorded on the Ethereum blockchain. Spanning from November 2015 to December 2024, the dataset encapsulates the trading activity of 216,336,529 users trading 1,138,136 unique tokens, offering a detailed view of crypto-market activity over time. Uniquely, it enables the analysis of a financial ecosystem from its inception, providing rare insights into its structural evolution, participant dynamics, and emergent behaviors. As the largest publicly available resource of its kind, it supports research in blockchain analytics, market dynamics and temporal network analysis. The full dataset and accompanying code are released for public use.
Alessia Galdeman, Lucio La Cava, Matteo Zignani, Andrea Tagarelli ¡ 5 authors
The rapid growth of Non-Fungible Tokens (NFTs) and the extensive trading activities associated with such an intriguing domain led to the emergence of large-scale and interconnected transaction networks involving the most prominent NFT markets. Despite such interdependencies representing an inestimable source of information for the proper understanding of the NFT landscape, previous studies treated each market separately, overlooking relevant phenomena. In this study, we explore a multilayer network modeling approach to analyze transactions in multiple NFT markets. We reveal previously unnoticed macroscopic and mesoscopic traits by investigating indicators that discern whether markets are independent or linked: users trading NFTs are organized in cross-market communities where multi-market users act as bridges across marketplaces, adapting to the diverse nature of the markets they operate in. We also conduct an in-depth examination of such multi-market users, studying their specific activity patterns that leave a distinctive mark on the system: the majority of multi-market users well differentiate their earnings and expenses among the markets, while a fraction of them is directed toward a more polarized money allocation based on the typology of the markets. By offering a fresh perspective on this intricate financial system and emphasizing the importance of perceiving the NFT markets as a unique and interconnected world, our study paves the way for further contributions aimed at unraveling the complexity of cryptosystems and understanding the latent phenomena across NFT markets.
We investigate the high-frequency dynamics of Bitcoin and Ethereum perpetual futures traded on Binance from January 2020 to December 2024. After a thorough discussion of the stylized facts and particularities of Bitcoin perpetual futures, based on previous research in futures markets, we evaluate the fit of two competing models of market microstructure: the Mixture of Distributions Hypothesis (MDH) and the Intraday Trading Invariance Hypothesis (ITIH). Using intraday data at different levels of aggregation, we investigate the relationship between return volatility per transaction and trade size. We find evidence favoring the MDH in the crypto futures market.
Silvia Edelweiss Crusco dos Santos, HĂŠlder SebastiĂŁo, Nuno Silva
Using daily data from November 9, 2017 to December 31, 2022, this paper uses Granger causality in the mean and the distribution to investigate the transmission of information between return, volume, volatility, and illiquidity for Bitcoin and the nine most important altcoins in terms of market capitalization. Additionally, the forecastability of Bitcoin returns is examined using linear models with different predictor spaces estimated using LASSO and the performance of several trading strategies devised upon those forecasts is assessed. The causal relationships between returns, volumes and volatilities of Bitcoin and each altcoin are more evident in the left tail of the distribution, where Bitcoin acts mostly as a transmitter of information, and in the right tail for causality regarding illiquidity. In bullish markets, Bitcoin acts mostly as a receiver of information. The best Bitcoin trading strategy is based on the model which incorporates the information on all cryptocurrencies, exhibiting a cumulative return of 331% and an annualized Sharpe ratio of 94.59%, considering an enter/exit threshold of 0.25% and after 0.5% round-trip transaction costs. These results are statistically significant when compared with the buy-and-hold strategy, which renders a cumulative return of 121% and a Sharpe ratio of 64.74%. These results point out the importance of considering information from other cryptocurrencies to forecast and trade on Bitcoin.
Introduction This article presents a method for short-term cryptocurrency price forecasting utilizing news headlines. Methods The study analyzes the impact of news on asset prices within one hour of publication, employing machine learning-based classification with BERT and GPT models, as well as GloVe vector representations. Results The proposed cascade classifier model enhances prediction accuracy by initially assessing the strength of a news item and subsequently forecasting the direction of price movement. Experimental results demonstrate the effectiveness of the developed classification model. Discussion The model achieves an accuracy of 79% in predicting price movements, confirming the potential of leveraging news headlines to improve short-term forecasts in cryptocurrency markets.
This study investigates Granger-causality relationships between crypto-assets (Bitcoin and Ethereum) and traditional financial assets (stock indices and exchange rates) in BRICS-T countries over the 2016â2024 period. The findings highlight significant interlinkages: bidirectional causality exists between Bitcoin and Russia's stock market, and between Ethereum and both Brazil's stock market and the USD/INR exchange rate. Unidirectional causality is observed from Bitcoin to the stock markets of Brazil, India, and China, while the USD/TRY exchange rate influences Bitcoin. Similarly, Ethereum affects the stock markets of Russia, India, and South Africa, while the USD/TRY exchange rate also Granger-causes Ethereum. These results indicate a growing synchronization between crypto-assets and conventional financial markets. The presence of both unidirectional and bidirectional causalities emphasizes the increasing integration of global financial systems and highlights the importance for investors to consider cross-market interactions when making decisions. Crypto-assets are no longer isolated but are embedded in broader financial dynamics.
This paper develops a novel modeling framework that integrates time-varying quantile-based spillover effects into a regime-switching realized volatility model. A dynamic spillover factor is constructed by identifying the most influential contributors to Bitcoinâs realized volatility across different quantile levels. This quantile-layered structure enables the model to capture heterogeneous spillover paths under varying market conditions at a macro level while also enhancing the sensitivity of volatility regime identification via its incorporation into a time-varying transition probability (TVTP) Markov-switching mechanism at a micro level. Empirical results based on the cryptocurrency market demonstrate the superior forecasting performance of the proposed TVTP-MS-HAR model relative to standard benchmark models. The model exhibits strong capability in identifying state-dependent spillovers and capturing nonlinear market dynamics. The findings further reveal an asymmetric dual-tail amplification and time-varying interconnectedness in the spillover effects, along with a pronounced asymmetry between market capitalization and systemic importance. Compared to decomposition-based approaches, the X-RV type of modelsâespecially when combined with the proposed quantile-driven factorâoffers improved robustness and predictive accuracy in the presence of extreme market behavior. This paper offers a coherent approach that bridges phenomenon identification, source localization, and predictive mechanism construction, contributing to both the academic understanding and practical risk assessment of cryptocurrency markets.
Cryptocurrencies like Bitcoin can be considered commodities under the Commodity Exchange Act (CEA) and the Commodity Futures Trading Commission (CFTC) has jurisdiction over cryptocurrencies considered to be commodities, particularly in the context of futures trading. This paper presents a method for long and short term trend prediction of certain cryptocurrencies which is predicated on an application of the Fractal Market Hypothesis. This is an area of market theory where the self-affine properties of a fractal stochastic field are used to model a financial time series. After an introduction to the underlying theory and mathematical modelling, a fundamental analysis of Bitcoin and Ethereum to U.S. Dollar exchange markets is conducted. This analysis is based on a consideration that a changes in polarity of the 'Beta-to-Volatility' and the 'Lyapunov-to-Volatility' ratios to indicate an impending change to the Bitcoin/Ethereum price trend signal. This is used to recommend a long, a short or a hold trading position for which algorithms are provided (coded in Matlab) and 'back-tested'. An optimisation of these algorithms is conducted, leading to a strategy for implementing an ideal range of the key parameters for 'driving' the algorithms developed. This is based on maximising the accuracy and profitability to assure a high level of confidence. The application of the trading strategy developed through this approach is demonstrated to provide useful information to aid cryptocurrency investments and quantify the likelihood that the market will become bull or bear dominant. Under stable conditions, Machine Learning (using the 'TuringBot') is shown to provide useful estimates of future price values and/or fluctuations over small event horizons in time. This minimises any \lq trading delay' caused by filtering the data and increases returns by providing optimal trade positions within a \lq micro-trend' that is too fast for detection otherwise. In certain cases, this increase can reach ~10%. The results presented confirm that Bitcoin and Ethereum exchanges are self-affine (fractal) stochastic fields with L\'evy distributions, displaying a Hurst Exponent of ~ 0.32, a Fractal Dimension of ~ 1.68 and Levy Index of ~1.22. They also confirm that the Fractal Market Hypothesis and its indices provide a suitable market model, that generates returns on investments that outperform all Buy and Hold strategies based on more standard market indices.
This study examined the dynamic interconnectedness and portfolio implications within the cryptocurrency ecosystem, focusing on five representative digital assets across the core functional categories: Layer 1 cryptocurrencies (Bitcoin (BTC) and Ethereum (ETH)), decentralized finance (Uniswap (UNI)), stablecoins (Dai), and crypto infrastructure tokens (Maker (MKR)). Using the Extended Joint Connectedness Approach within a Time-Varying Parameter VAR framework, the analysis captured time-varying spillovers of return shocks and revealed a heterogeneous structure of systemic roles. Stablecoins consistently acted as net absorbers of shocks, reinforcing their defensive profile, while governance tokens, such as MKR, emerged as persistent net transmitters of systemic risk. Foundational assets like BTC and ETH predominantly absorbed shocks, contrary to their perceived dominance. These systemic roles were further translated into portfolio design, where connectedness-aware strategies, particularly the Minimum Connectedness Portfolio, demonstrated superior performance relative to traditional variance-based allocations, delivering enhanced risk-adjusted returns and resilience during stress periods. By linking return-based systemic interdependencies with practical asset allocation, the study offers a unified framework for understanding and managing crypto network risk. The findings carry practical relevance for portfolio managers, algorithmic strategy developers, and policymakers concerned with financial stability in digital asset markets.
Axie Infinity is a blockchain-based video game offering players the chance to earn crypto tokens in exchange for their time spent playing the game. During the COVID-19 lockdowns, the game's popularity surged alongside the crypto market and stories of early adoptersâ quick returns on investments circulated among online crypto and Web3 communities. As the game's rapidly growing userbase plateaued, the community experienced several growth-related crises, one of which saw the value of the game's tokens crash. But players were not passive victims of these developments. They responded by creating a âscholarshipâ program to secure the flow of new players to the platform and actively commented on their commitment to the âgrindâ of playing the game to recoup their investments. This article treats the trajectory of Axie Infinity as both an exemplar case study of broader dynamics in the crypto gaming landscapeâa process we call the economization of play âand as a unique site in which players were not simply duped by the promise of the game, but were responding to crises proactively with risk mitigating and rationalizing strategies.
Abstract This research examines the dynamics of the non-fungible tokens (NFT) market by utilizing key financial metrics such as Bitcoin prices, the Crypto Fear-Greed Index, and DeFi indicators. It analyzes NFT-USD values, the Crypto Fear-Greed Index, total value locked in DeFi, and Bitcoin interactions between February 2021âJuly 2023. Employing ordinary least squares regression, quantile regression, Johansen cointegration, and VECM Granger analysis, the study uncovers complex relationships shaping the NFT market. The findings reveal a positive correlation between Bitcoin prices and NFT values, a negative relationship between total value locked in DeFi and NFT values, and an inverse connection between the Crypto Fear-Greed Index and NFT values. Additionally, cointegration exists among the variables, and causality analysis indicates that Bitcoin influences total value locked, while shifts in the Crypto Fear-Greed Index reflect market sentiment changes. These insights contribute to a deeper understanding of behavioral finance by illustrating how psychological factors, such as investor sentiment and the bandwagon effect, interact with digital asset markets. From a practical perspective, the results emphasize the importance of recognizing these interdependencies for policymakers and market participants striving to foster innovation in the rapidly evolving NFT ecosystem. By aligning with the transformative potential of Blockchain and DeFi, this study provides strategic insights for optimizing resource allocation, enhancing market efficiency, and shaping regulatory frameworks within innovative financial landscapes.
Alisa Kalacheva, Pavel Kuznetsov, Igor Vodolazov, Yury Yanovich
The rise in cryptoasset valuations and the ease of creating new tokens have spurred an increase in illicit activities within the market. Decentralized exchanges (DEX) facilitate the trading of a vast array of tokens, including those with minimal liquidity, amplifying the risk of fraudulent schemes. Fraudulent practices take various forms, including counterfeit tokens, rug pulls, and pump-and-dump schemes, all lacking functional innovation and relying heavily on aggressive social media marketing. This study contributes to the identification and profiling of deceitful tokens on DEX platforms. Our approach involved compiling on new tokens with an active trading start and attracted competition to buy them in first blocks spanning multiple years from the Ethereum blockchain, tracking all associated purchase and sale transactions. Our analysis revealed that Uniswap V2 predominantly hosts the trading of new tokens, with an alarming discovery that over 98% of tokens minted daily exhibit fraudulent characteristics. Subsequently, a machine learning model was developed to predict the likelihood of a rug pull occurring shortly after trading commencement. Although the dataset labeling methodology and detection problem statement are exploratory, we demonstrate the economic significance of the proposed approach within trading pipeline. The findings highlight the importance of identifying fraudulent activities and emphasize the need for collaboration between decentralized exchanges and regulatory bodies to mitigate financial losses for investors.