Non-Fungible Tokens (NFTs) enable the decentralized representation and exchange of real-world assets, supporting features like fractional ownership and programmable logic that underpin emerging digital finance ecosystems. However, the openness of NFT markets makes them susceptible to manipulation tactics like wash trading, where coordinated trades distort prices. This undermines valuation accuracy and erodes trust in decentralized finance. To counter these challenges, we propose NFTGuard, a unified framework for detecting wash trades and producing manipulation-resilient NFT price predictions. First, NFTGuard filters out manipulated transactions using a rule-based detector that identifies self-dealing and cyclic trading patterns prevalent in decentralized marketplaces. Second, NFTGuard prepares for price prediction by constructing a multi-modal representation that integrates temporal trading dynamics, transactional metadata, and asset-specific semantic signals. Third, NFTGuard performs prediction using a Multi-Layer Perceptron (MLP) mixing backbone that fuses these heterogeneous cues into manipulation-resilient forecasts. Experiments on real-world NFT datasets from platforms like Rarible and Opensea show that NFTGuard achieves a 90.9% F1-score in detecting wash trades and improves price prediction accuracy by over 10% compared to the baselines.
Bitcoin and other Crypto-Currency Price Prediction has been a concern for many financial analytics and business owners. This becomes critically important due to the volatile nature of the Bitcoin. This paper focuses on predicting the next-day Bitcoin price predictions using historical OHLC data from 2019 to 2024 using eleven machine learning algorithms. We have applied fourteen technical features including moving averages, volatility indicators, and lag variables to capture market statistics and price fluctuations. Models that were used in this paper include linear methods (Linear, Ridge, and Lasso regression), ensemble techniques (Random Forest, XGBoost, Gradient Boosting, AdaBoost), instance-based learning (KNN), support vector machines (SVR), decision trees, and deep learning (LSTM networks). Robust performance is ensured by the Five-fold cross-validation. The results clearly show that Lasso regression outperforms other algorithms with a RMSE of $727.33 and R2of 0.971, achieving superior performance in comparison to complex ensemble methods.
BPN), MATLAB, mean absolute percentage error (MAPE) In this study, we investigate Bitcoin price volatility from December 15, 2014 to January 29, 2024 using an integrated, multisource feature set and an optimization-learning pipeline that couples Taguchi orthogonal arrays with a backpropagation network (BPN) implemented in MATLAB.Publicly available market variables were prioritized and nonquantifiable exogenous shocks were not modeled; Taguchi screening identified critical predictors and simultaneously tuned control factors (network specification, hidden-neuron count, and currency inclusion), after which the BPN was trained on aligned weekly (n = 573) and monthly (n = 108) datasets to ensure cross-market comparability.Model accuracy, assessed by mean absolute percentage error (MAPE), improved substantially after Taguchi-guided selection and configuration-weekly MAPE decreased from 3.23% to 0.36% and monthly MAPE from 6.32% to 0.07%demonstrating the efficacy of the proposed optimization framework.Out-of-sample forecasts for February-April 2025 achieved predominantly sub-10% MAPE, while high-error instances were analyzed and attributed to contributing factors, yielding decision-relevant insights for practitioners and researchers.Collectively, the results show that systematic variable selection and orthogonal-array-based model design materially enhance neural forecasts of cryptocurrency prices and provide a reproducible pathway to accurate, time-efficient prediction.
This paper applies wavelet quantile correlation to research on the relationship among renewable energy stocks, investor sentiment, and the cryptocurrency market. The empirical results indicated that under extremely negative conditions, in both the short and medium run, renewable energy stocks and cryptocurrencies are negatively correlated, implying that during such periods, renewable energy stocks can be used as a safe haven for cryptocurrencies. The opposite happens when the market is average or booming. This indicates that investors tend to invest simultaneously in these two promising asset classes when the market performs well. Under varied market conditions, FGI correlates positively with cryptocurrency, demonstrating sentiment influences price patterns. Moreover, the correlation between FGI and renewable energy stocks further validates the relationship between cryptocurrencies and renewable energy stocks. These findings can be used to improve the prediction of market trends by investors using sentiment indices and to devise more effective portfolio diversification strategies that minimize risk amid an evolving market.
The rapid evolution of digital assets transforms cryptocurrencies into one of the most volatile and data-rich financial markets. Their nonlinear and unpredictable nature limits the effectiveness of traditional forecasting models, motivating the use of machine learning methods to identify hidden patterns and short-term price movements. This study compares the performance of Logistic Regression (LR), Random Forest (RF), XGBoost, Support Vector Classifier (SVC), K-Nearest Neighbors (KNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models in predicting the daily price directions of Bitcoin (BTC), Ethereum (ETH), and Ripple (XRP). Extensive data preprocessing and feature engineering are performed, integrating a broad set of technical indicators to enhance model generalization and capture temporal market dynamics. The results show that XGBoost achieves the highest classification accuracy of 55.9% for BTC and 53.8% for XRP, while LR provides the best result for Ethereum with an accuracy of 54.4%. In trading simulations, XGBoost achieves the strongest performance, generating a cumulative return of 141.4% with a Sharpe ratio of 1.78 for Bitcoin and 246.6% with a Sharpe ratio of 1.59 for Ripple, whereas LSTM delivers the best results for Ethereum with a 138.2% return and a Sharpe ratio of 1.05. Compared to recent studies, the proposed approach attains slightly higher accuracy, while demonstrating stronger robustness and profitability in practical backtesting. Overall, the findings confirm that through rigorous preprocessing machine learning-based strategies can effectively capture short-term price movements and outperform the conventional buy-and-hold benchmark, even under a simple rule-based trading framework.
The rise in Bitcoin and Ethereum is well-known. Research shows that public sentiment greatly affects their price changes. Thus, public sentiment analysis is a key factor in making investment decisions. This study analyzes public sentiment towards Bitcoin and Ethereum on platform X using Graph Neural Networks (GNN), specifically the GCN and AGN-TSA models. GCN is utilized for its ability to capture syntactic relationships between words, while AGN-TSA integrates textual content with user-level social interactions through an attention mechanism. The dataset is collected from$X$using the keywords Bitcoin, BTC, Ethereum, and ETH, followed by preprocessing and labeling based on FinBERT and Vader as a benchmark labeling technique to construct the training and test sets. Evaluation employs a confusion matrix to compare model performance. The results show that GCN without addressing class imbalance achieves 83.89 % accuracy, whereas AGN-TSA achieves 86.21%. However, confusion matrix analysis revealed severe bias toward the majority class, so we needed to use extreme class weighting (ratio 17:1:10), which improved minority-class recall. However, it caused training instability and reduced accuracy: GCN dropped to 80.58 %, and AGN-TSA dropped to 86.15% (a decrease of$0.06 \%)$. Despite the decrease, AGN-TSA still achieves the best accuracy compared to GCN, this result reinforcing our initial hypothesis that attention-based graph modeling which leverages social ties yields superior performance for sentiment classification in crypto related discourse. Furthermore, the accuracy of the data labeling technique and the imbalance in the label distribution also affect the final accuracy results.
Abstract This paper investigates the optimization of data sampling and target labeling techniques to enhance algorithmic trading strategies in cryptocurrency markets, focusing on Bitcoin (BTC) and Ethereum (ETH). Traditional data sampling methods, such as time bars, often fail to capture the nuances of the continuously active and highly volatile cryptocurrency market and force traders to wait for arbitrary points in time. To address this, we propose an alternative approach using information-driven sampling methods, including the CUSUM filter, range bars, volume bars, and dollar bars, and evaluate their performance using tick-level data from January 2018 to June 2023. Additionally, we introduce the Triple Barrier method for target labeling, which offers a solution tailored for algorithmic trading as opposed to the widely used next-bar prediction. We empirically assess the effectiveness of these data sampling and labeling methods to craft profitable trading strategies. The results demonstrate that the innovative combination of CUSUM-filtered data with Triple Barrier labeling outperforms traditional time bars and next-bar prediction, achieving consistently positive trading performance even after accounting for transaction costs. Moreover, our system enables making trading decisions at any point in time on the basis of market conditions, providing an advantage over traditional methods that rely on fixed time intervals. Furthermore, the paper contributes to the ongoing debate on the applicability of Transformer models to time series classification in the context of algorithmic trading by evaluating various Transformer architectures—including the vanilla Transformer encoder, FEDformer, and Autoformer—alongside other deep learning architectures and classical machine learning models, revealing insights into their relative performance.
This study contributes to the growing literature on the determinants of Bitcoin volatility by examining its relationship with financial stress. Building on prior research linking Bitcoin volatility to broader economic and financial uncertainty, we employ a combination of regression analysis, a GARCH-MIDAS framework, and a Vector Autoregression (VAR) model to evaluate both the static and dynamic effects of financial uncertainty on Bitcoin. Preliminary regression results indicate that financial stress measures significantly and negatively predict Bitcoin volatility. The GARCH-MIDAS model confirms these results, showing a strong negative impact of financial stress on the long-term component of volatility. VAR analysis further reveals that Bitcoin volatility decreases in response to shocks in financial stress indicators. These findings highlight Bitcoin’s sensitivity to systemic financial conditions and carry important implications for risk management among cryptocurrency traders, institutional investors, and financial regulators.
Blockchain technology is considered a transformative innovation, offering decentralized, secure, and transparent solutions to various industries, with cryptocurrencies being its most famous application. The volatility and non-linear behavior of cryptocurrency markets pose significant challenges for predicting their prices accurately. Predicting cryptocurrencies prices based on traditional statistical methods often fail to capture the market complex dynamics. Therefore, the recent developments in Artificial Intelligence, especially in deep learning and ensemble-based approaches have presented promising results. This study delivers a comprehensive literature review focusing on applying deep learning and ensemble deep learning algorithms in cryptocurrency time series price prediction. The main deep learning models such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) are examined with a variety of time intervals and cryptocurrency types. The findings present that deep learning models, especially when used in hybrid or ensemble configurations, have obtained promising results. This review highlights the efficacy and significant potential of ensemble deep learning and its capabilities in cryptocurrencies price trend forecasting offering valuable insights for investors and researchers.
Total Value Locked (TVL) explicitly reflects the total asset users deposit in Decentralized Finance (DeFi) protocols, similarly to the Asset Under Management (AUM) in traditional finance. This exposes liquidity providers to the risk of short-term liquidity depletion and highlight the urgent need for quantifiable and predictive risk management tools. As its short-term fluctuations can be effectively characterized by on-chain static features (e.g., fee tier, volatility), dynamic features (e.g., token balance changes, slippage), and technical indicators (e.g., MA), and since posterior calibration methods based on high-accuracy point forecasting models can provide more reliable estimations of downside risk boundaries, this study focuses on the USD Coin - Ethereum pool (0.3 % fee tier) of Uniswap V3. The model is constructed using 17 features, with eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine employed for TVL growth-rate regression forecasting. The results are compared with those from Long Short-Term Memory, Gated Recurrent Unit, and Naïve baselines. Furthermore, the research introduces the Liquidity-at-Risk (LaR95) metric to estimate downside risk through both residual-based and quantile regression approaches, and conduct interpretability analysis using SHAP values. XGBoost obviously outperforms Deep learning models on directional accuracy. XGBoost demonstrates a significantly superior performance to deep learning models in predicting the direction of TVL changes. The residual-based LaR95(liquidity-at-risk at the 95% confidence level) derived from its point forecasts exhibits a coverage rate closely aligned with the theoretical level, validating the effectiveness, robustness, and interpretability of the “high-accuracy prediction and residual calibration” framework in DeFi risk management.
Piotr Fiszeder, Witold Orzeszko, Radosław Pietrzyk, Grzegorz Dudek
This study advances the understanding of Bitcoin volatility forecasting by analysing an extensive set of 62 explanatory variables, including cryptocurrency market behaviour, Google search trends, financial indices, and economic indicators. We employ Bayesian Model Averaging (BMA), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF) methods to assess variable importance and forecast accuracy. Our research demonstrates that LASSO and RF models incorporating exogenous variables significantly improve both daily and weekly Bitcoin variance forecasts compared to models using only lagged Bitcoin volatilities. Key factors influencing Bitcoin volatility include lagged realised variances, trading volume, and Google search intensity. The study reveals that the impact of these variables on Bitcoin volatility is time-varying, reflecting its evolving relationship with broader economic indicators and market sentiment. Our findings contribute to the literature by providing a comprehensive analysis of Bitcoin volatility drivers, evaluating the effectiveness of variable transformations, and comparing the performance of advanced forecasting methods in handling the cryptocurrency's extreme volatility. These insights are valuable for researchers, investors, portfolio managers, and policymakers navigating the dynamic cryptocurrency market.
Decentralized finance has introduced new forms of market making through automated market makers, where users provide liquidity to decentralized exchanges such as Uniswap. In Uniswap v3, liquidity providers (LPs) can concentrate their liquidity within custom price ranges, improving capital efficiency but introducing exposure to impermanent loss and nonlinear portfolio risk. Managing this exposure dynamically poses a challenge as the LP position’s token composition changes continuously with market prices. This thesis investigates whether a Deep Reinforcement Learning (DRL) agent can effectively hedge a Uniswap v3 LP position using cryptocurrency futures. A simulated Uniswap environment is developed to model concentrated liquidity providing positions and the hedge positions tied to it. The hedging problem is framed as a sequential decision process, where the agent seeks to minimize downside portfolio variance while maintaining upside exposure. The Proximal Policy Optimization algorithm is applied to learn an adaptive hedging policy, which is evaluated against baseline strategies such as fixed-frequency rebalancing. The results show that the DRL-based hedges can outperform the baseline strategies, achieving higher average portfolio returns, with similar average drawdowns. However, the learned policies varied between agents, and although they reduced downside variance in many cases, they were also more likely to experience larger maximum drawdowns. These findings indicate that reinforcement learning offers a promising but complex approach for managing the nonlinear risks of concentrated liquidity provision in decentralized exchanges.
The increasing use of decentralized finance (DeFi) accelerates the demand for trustless, secure mechanisms for crosschain token exchange. This paper outlines a complete model for atomic token swaps based on the Hashed Timelock Contract (HTLC) scheme, allowing for intermediary-free token exchanges across disparate blockchain systems. The system makes use of the local blockchain simulation framework, Ganache, to design and test cross-chain interactions in a sandbox environment. To improve the decision-making capabilities for users, a real-time cryptocurrency price forecasting subsystem is added which utilizes machine learning models to analyze and predict the market and its volatility. Additionally, the system harnesses Generative AI capabilities through prompt engineering to tailor investment advice for individual users by analyzing the market, their preferred risk level, expected returns, and provide investment strategies aligned with users' preferences. Apart from sophisticated trading algorithms, the solution also offers a simple dashboard for market price monitoring and performs rapid token swaps at the user's command. Smart contracts are implemented using Solidity, token and price feeds are ports to Web3.js, predictive analytics is done in Python, while the frontend and backend are structured in Next.js alongside Node.js. System testing validates hypotheses on the provision of secure cross-chain swaps within one transaction without compromising.
Bitcoin’s extreme volatility continues to challenge accurate forecasting and risk management. Traditional econometric approaches struggle with the nonlinear and shifting dynamics of cryptocurrency markets, while deep learning models such as the Gated Recurrent Unit (GRU) often lack interpretability and adaptability to regime changes. To address these limitations, this study introduces a hybrid Gaussian Hidden Markov Model–Gated Recurrent Unit (HMM-GRU) framework for Bitcoin price forecasting. The HMM identifies latent market regimes from four years of daily closing prices and integrates these states as auxiliary features for the GRU network. Experimental results show that the hybrid model consistently surpasses the standalone GRU in predictive accuracy. Under the optimal configuration, HMM-GRU achieves a Mean Absolute Error (MAE) of 1,557.33 and a Mean Absolute Percentage Error (MAPE) of 1.42%, compared with 1,713.30 and 1.57% for GRU, representing an approximate 9% improvement in both absolute and relative error performance. The inclusion of regime-based features enables the model to better capture market transitions and mitigate overfitting to short-term noise. Beyond performance gains, the proposed approach enhances interpretability by linking forecasts to identifiable market regimes. These findings highlight the value of combining statistical regime detection with deep learning for volatile financial assets, providing practical insights for both investors and researchers in time-series forecasting.
Industry 4.0 and digital transformation have accelerated the emergence of virtual assets such as cryptocurrencies. Among them, Bitcoin, a virtual currency, has captured significant attention from both finance theorists and practitioners, achieving the highest market capitalization to date. The objective of this study is to examine the behavior and interrelationships between Bitcoin and several traditional financial assets within the framework of an international diversification strategy that combines conventional and crypto assets. In this context, Bitcoin is considered as a potential new asset class for portfolio diversification. To explore this relationship, we analyze the links between Bitcoin and a selection of major currencies—EUR, GBP, and JPY—as well as certain commodities. The study employs the Value at Risk (VaR) approach using three empirical methods, complemented by Conditional Value at Risk (CVaR) as a robustness measure, given its ability to capture tail risk more effectively than VaR. Using daily data from October 29, 2016, to October 23, 2020, the findings reveal that including Bitcoin in a diversified portfolio can significantly enhance risk–return characteristics. These results provide new insights for portfolio managers and investors seeking optimal diversification strategies in the context of digital finance.
Romulo de Moraes, Arthur G. Bubolz, Denner Ayres, Vinícius Teixeira Pinto · 5 authors
Este artigo apresenta uma arquitetura para loterias descentralizadas na rede Ethereum, baseada em contratos inteligentes, aleatoriedade verificável e otimização de armazenamento por meio de Merkle Tree. A proposta visa reduzir o custo médio das transações (gas fees) e aprimorar a escalabilidade onchain, comparando três abordagens distintas de armazenamento: array, mapping e Merkle Tree. Os resultados mostram que o consumo de gas evidencia uma vantagem expressiva da Merkle Tree, reduzindo em até três ordens de magnitude o custo total, o que confirma sua eficiência e potencial para aplicações descentralizadas de alta demanda.
Purpose The evolution of the cryptocurrency landscape has been innovative, dynamic and adaptable. Using performance analysis and science mapping techniques, the study aims to conduct a bibliometric analysis to examine the landscape of the cryptocurrency domain, focusing on the forecasting aspect. Design/methodology/approach The study uses metadata from the Scopus database, ranging from 2015 to 2024, comprising 849 articles. They identified significant research constituents and five major thematic clusters. Findings The findings suggest that the research in the domain has yet to reach its full potential. The clusters involve structural shifts or turbulence in cryptocurrency markets, machine learning-based cryptocurrency price prediction, forecasting Bitcoin price and volatility, Bitcoin returns analysis, and cryptocurrency: A hedge and safe haven alternative. Further empirical analysis revealed that the artificial neural network and deep neural network outperformed the traditional statistical model, the autoregressive integrated moving average (ARIMA). Originality/value The study supplement these findings with significant future research directions, which will be beneficial for upcoming studies as the field has immense potential and countless areas worth exploring.
We investigate the predictability of cryptocurrency returns using a comprehensive set of macroeconomic and cryptocurrency-specific factors and a set of 12 machine learning models. To enhance interpretability, we employ SHAP analysis to quantify the marginal contribution of each factor to model outputs. We further assess the economic value of predictive signals by constructing long-short and long-only portfolios. Empirically, tree-based methods, particularly random forests, deliver the highest predictive accuracy and outperform neural network and linear benchmarks, with predictability substantially stronger than that documented in equity markets. Across models, the market-to-realized-value ratio, new addresses, and active addresses consistently emerge as the most influential predictors, with higher values associated with higher expected returns. Portfolio results show that neural network-based strategies achieve the highest cumulative performance, indicating meaningful investment gains. Overall, our findings demonstrate the value of machine learning for return forecasting in the cryptocurrency market and provide practical insights for investors and financial analysts operating in highly volatile and evolving cryptocurrency environments.
Richard Wiputra, Davis Kelvin, Theo Justin Amantha, Sonya Nadira Gularso
This paper introduces FOMOCheck, an artificial intelligence (AI) agent designed for the early detection of pump-and-dump (P&D) activities in the Solana blockchain ecosystem. The agent integrates CoinGecko market indicators with on-chain token-control features obtained via the Solana API to generate interpretable risk scores and explanations. In a backtesting evaluation involving 30 tokens identified as P&D schemes, FOMOCheck utilized only pre-dump information, achieving a recall of 70.0% and an F1-score of 82.4 % at a risk threshold of 6/10 or higher. A pilot deployment on 20 new token listings demonstrated end-to-end response times under 4.2 seconds (95th percentile), validating its feasibility for real-time triage and monitoring. A qualitative assessment revealed that the system generated transparent, metric-referenced explanations, although it demonstrated sensitivity to “green-flag” cues, such as price-volume alignment. The primary limitations include incomplete data on holder distribution and liquidity locks, as well as the lack of a balanced evaluation set containing non-P&D tokens. Future work will focus on expanding the dataset to include legitimate tokens, integrating streaming data connectors, and deploying a production-ready anomaly scoring API.
Crypto analysts have to deal with a variety of challenges, with the most important area being the price volatility of cryptocurrencies. Due to uncertain market trends, many studies have been conducted on forecasting techniques, and some of these techniques have been integrated with advanced analytical tools, including machine learning (ML) techniques. Making reliable predictions of the speculative behavior of financial assets, especially in non-stationary and highly volatile environments such as the cryptocurrency market, is a challenging task. In this study, ML techniques are used to identify influential features that affect the prices of cryptocurrencies, especially for Bitcoins. In addition, multivariate control charts are utilized for signal detection, allowing for a structured approach to develop trading strategies for seasonal market conditions. Unlike other studies that do not take seasonality into serious consideration when analyzing market fluctuations, the proposed approach explicitly accounts for it. The developed strategy is tested across various market conditions, including the final days of each year from 2019 to 2024, and demonstrates strong and consistent performance in all cases. By systematically identifying key on-chain features and analyzing them by means of control charts, this study develops a structured approach to anomaly-based trading strategies in Bitcoins. These discoveries address an extensive discussion on automated trading systems, demonstrating that feature selection, technical indicators, market seasonality, and halving impacts are important components in hinting at successful cryptocurrency exchange strategies.
This study conducts a bibliometric review of Bitcoin research in the Business and Economics domains, using VOSviewer to visualize network structures and Bidirectional Encoder Representations from Transformers Topic (BERTopic) to derive semantically coherent topic clusters. The analysis identifies five major research themes: (1) Diversification, hedging, and safe-haven properties; (2) Market dynamics, efficiency, and investor behavior; (3) Bitcoin price and volatility prediction attempts; (4) Environmental impact of Bitcoin; and (5) Financial impact of Central Bank Digital Currency (CBDC). Based on these themes, the study recommends further investigation into the influence of Exchange-Traded Fund (ETF) approvals, regulatory frameworks, and institutional investor participation on Bitcoin’s safe-haven potential; the role of market dynamics and regulatory interventions; early detection of herding behavior and price bubbles; the integration of machine learning and deep-learning models for price prediction; the environmental costs associated with mining; and the evolving regulatory and implementation challenges of CBDCs. Overall, this review synthesizes existing scholarship and outlines future research directions for the rapidly evolving cryptocurrency ecosystem.