Blockchain Papers

Janesh Sami

No abstract is available for this record.

Subramanya V. Odeyar, P. K. Lolakshi, L. Swetha, K. M. Thejaswini · 6 authors

Abstract The Bitcoin has recently garnered significant media and public attention due to its dramatic price increases and declines. As a result, many researchers have examined the various factors influencing Bitcoin’s price and the patterns behind its fluctuations, often using machine learning techniques. This study explores several machine learning algorithms for Bitcoin price prediction, including logistic regression and long short-term memory (LSTM) models. While LSTM-based models have shown superior performance in predicting Bitcoin prices (regression), this research provides a detailed investigation into Bitcoin’s evolution and a comprehensive review of the machine learning methods used for price prediction. Additionally, the study includes a Bitcoin price prediction model, which is developed using specific algorithms to forecast Bitcoin’s price, along with insights into the factors affecting its price movements. The proposed LSTM model has achieved 98% accuracy.

Xinpeng Huang, Wangjie Qiu, Wanqing Jie, Qing Xia · 9 authors

The rapid growth of decentralized finance (DeFi) has spurred innovation but also exposed blockchain systems to severe security threats. As of November 2025, cumulative losses from blockchain security incidents have exceeded${\$}$36.89 billion. Flash loan attacks account for 135 reported cases and rank fourth among all attack methods. Existing detection approaches either analyze contract source code, which is unavailable for many deployed contracts, or use transaction pattern matching tailored to specific scenarios, and therefore generalize poorly to diverse flash loan attacks. In this paper, we presentFlashShield, a general flash loan attack detection framework based on Hypergraph Neural Networks (HGNNs). We construct comprehensive datasets containing attack and benign transactions across multiple chains, and systematically analyze flash loan attack mechanisms along four DeFi protocol layers: code implementation, business logic, economic mechanisms, and cross protocol interactions.FlashShieldrepresents each transaction as a hypergraph of transfer actions and semantic relations, and employs a hybrid architecture that integrates spectral, spatial, and original features together with both node level and graph level representations. Experiments show thatFlashShieldimproves recall by 29% over leading methods and identifies 43 previously unknown malicious or suspicious activities (18 confirmed flash loan-related exploits and 25 suspected address poisoning incidents), demonstrating its effectiveness and scalability for automated DeFi security monitoring.

Hongtao Tang, Yuxiang Liu, Mingdong Tang

No abstract is available for this record.

Arch Promchan

The volatility and continuous operation of digital asset markets make manual trading inefficient, creating a strong need for reliable automated trading systems. However, determining whether simple reactive algorithms or complicated predictive models are more effective in these volatile conditions remains a significant challenge. To address this problem, the research aimed to design and evaluate a web based algorithmic trading dashboard capable of directly comparing a Simple Moving Average (SMA) crossover strategy against an Autoregressive Integrated Moving Average (ARIMA) forecasting model. A custom Python backtesting engine utilizing Walk Forward Analysis was developed, forcing both algorithms to continuously adapt to unseen historical Bitcoin data while simulating realistic compounding returns. The complicated predictive model was outperformed by the mathematically simpler trend following approach. The SMA strategy achieved a higher return on investment and a better win rate. This confirms the effectiveness of filtering market noise to capture sustained directional momentum. In contrast, the ARIMA model produced lower returns and fewer successful trades. The backend statistical solver processed the entire dataset without requiring a historical mean drift fallback. However, the statistical model lost its predictive accuracy when forced to project prices across multiple days. This resulted in a high measurement error. These findings indicate that for volatile digital assets, structurally lagging but reactive indicators are financially more effective and functionally more reliable than attempting statistical price prediction. The successful development of the dashboard also provides a functional architectural blueprint for separating complicated quantitative Python backends from responsive graphical user interfaces.

Ammar Ahmed Othman, Seddiq Hassan Al-Banna Ali, Mohammed Bakr Youssef

In the digital currency, Bitcoin (BTC) is called the gold of the digital currency. It is possible to make some profits in trading of bitcoins, though this market is a very illiquid market and it is very challenging to determine the price of a bitcoin. The current work uses historical data and technical indicators to predict Bitcoin prices in a broad approach. BTC-USD price data were obtained using Yahoo Finance API and covered from 01/01/2015 till 07/01/2024. The concept of feature engineering was applied to improve the dataset by including vital financial characteristics, including Moving Averages, RSI, and Bollinger Bands for higher forecasting precision. The forward-looking model for the Bitcoin price was developed using machine learning and deep learning algorithms. The efficiency of the model was assessed with the help of Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The overall values of Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error were 0.0062, 8.39e-05 and 0.0092 respectively which suggest that the proposed model is accurate in forecasting the future prices.

Grigorios Rapos

No abstract is available for this record.

Waseem Kkhoso

This study develops and tests a theoretical framework linking liquidity, market integration, and return predictability in cryptocurrency markets. Analyzing high-frequency daily data for five major cryptocurrencies (Bitcoin, Ethereum, Binance Coin, Solana, and Ripple), we employ Random Forest models augmented with rigorous time-series diagnostics and economic significance tests. Our findings establish a fundamental dichotomy: Bitcoin exhibits predictability driven by macroeconomic fundamentals (global risk-free rates, economic policy uncertainty), consistent with its emergence as a macro-asset; altcoins, in contrast, are dominated by internal microstructure (realized volatility, illiquidity) and speculative sentiment. Formal hypothesis tests confirm that (i) lower liquidity predicts higher future returns, (ii) machine learning models systematically underpredict during positively skewed regimes, and (iii) macro integration attenuates microstructure-driven predictability. Out-of-sample R 2 values reach 6.8% for Bitcoin and 9.6% for Ethereum, with Diebold-Mariano statistics rejecting equal predictive accuracy against a random walk at the 1% level. A mean-variance investor would earn a certainty equivalent gain of 2.4% annually by exploiting these forecasts. The results challenge the efficient market hypothesis for 1 digital assets, establish a new taxonomy of cryptocurrency predictability, and provide critical implications for asset pricing, portfolio allocation, and risk management in decentralized finance.

Sergei Solovev

Predicting short-term mid-price movements from limit order book (LOB) data is a fundamental problem in quantitative finance and market microstructure research, with direct applicability to both traditional exchanges and cryptocurrency markets—including centralized exchanges (CEXs) and emerging on-chain LOB protocols in decentralized finance (DeFi). We present three contributions to this domain. First, we propose DA-BiGRU-CNN, a domain-aware dual-branch architecture that decomposes LOB features into price and volume information channels, processes them through dedicated bidirectional GRU encoders with shared microstructure features, and fuses temporal representations via a multi-scale convolutional bottleneck (Conv1d with kernels k = 3,5,7). Second, we provide empirical evidence for a "feature sufficiency" hypothesis: a unidirectional GRU trained on 53 basic features achieves performance statistically equivalent to one trained on 219 extensively engineered features—including rolling statistics, exponential moving averages, and lag/difference features—suggesting that recurrent hidden states implicitly learn these temporal patterns. Third, we document a "negative ensemble effect" where combining sequential (GRU) and tabular (gradient boosting) models consistently degrades prediction quality, contradicting the widely-held assumption that model diversity improves ensemble performance. On a large-scale dataset of 12,165 LOB sequences (12.1M timesteps), our GRU baseline achieves a weighted Pearson correlation of 0.266, outperforming LightGBM by 58%, while our domain-aware architecture offers an architecturally principled alternative that naturally separates price dynamics from liquidity dynamics.

Layal Youssef, Juan Páez‐Farrell

Predicting cryptocurrency crashes is challenging due to their speculative dynamics, extreme volatility,and limited regulatory structure. This study investigates the predictability of Bitcoin crashesusing standard and dynamic probit models applied to daily data from 2017 to 2023. Crash indicatorsare constructed using a 3.09𝜎 tail event rule, and model performance is evaluated through anextensive grid search over multiple crash horizons and consolidation windows, ensuring robustnessagainst horizon dependent distortions. The empirical results show that hybrid models that combinesentiment, macro-financial variables, and Bitcoin specific returns consistently outperformsentimentonly specifications.The findings highlight the importance of combining behavioral indicators with global riskmeasures and cryptocurrency specific dynamics to capture the multifaceted drivers of Bitcoincrashes. The results have implications for investors, exchanges, and policymakers looking for earlywarning mechanisms for systemic risk in digital asset markets.

Mohd. Rahimie Abd. Karim, Saizal Pinjaman, Izaan Jamil, Azmi Abd. Majid · 5 authors

This study examines the weak-form efficiency and international price integration of Malaysia’s regulated Bitcoin market. Daily closing prices for Bitcoin traded in Malaysian ringgit (BTC/MYR), the international Bitcoin price in US dollars (BTC/USD), and the USD/MYR exchange rate are analysed over the 2021–2026 period using secondary market data. The international Bitcoin price is converted into ringgit to provide a currency-consistent benchmark for the local market. Random-walk behaviour is evaluated using the runs test, Ljung–Box test and variance-ratio test. Market integration is examined through unit-root tests, Engle–Granger cointegration analysis and an error-correction model. The daily results provide mixed evidence regarding weak-form efficiency. Although the runs test does not reject randomness in return signs, the Ljung–Box and variance-ratio results indicate dependence at selected horizons. This dependence becomes weaker in the weekly analysis, suggesting that the efficiency assessment is sensitive to data frequency. The local and international Bitcoin prices are cointegrated, with a long-run coefficient close to unity. The error-correction results further show that deviations from the long-run relationship are corrected over time and that international Bitcoin returns significantly influence short-run local price movements. Nevertheless, a small local price premium and residual volatility clustering remain. Overall, Malaysia’s Bitcoin market is closely integrated with the international market but is not perfectly efficient at all horizons. The findings support policies promoting transparent benchmark pricing, market surveillance, adequate liquidity and volatility-risk controls among Malaysian digital asset exchanges.

Guo Chen

No abstract is available for this record.

Vimal Dwivedi, Karuna Kadian, A K Adas Gupta, G Vishwanath Gupta

No abstract is available for this record.

Boon Chuan Lim

No abstract is available for this record.

Timothy A. Adeyi, Adrian D. Cheok, Huihui Song, Steven Z. Zhou · 8 authors

No abstract is available for this record.

Asmaa Alkholy, Mariam Essam

Bitcoin price prediction is a popular topic in finance and technology circles. Developing an accurate bitcoin price prediction algorithm is crucial for the cryptocurrency market's growth and development. The development of bitcoin price prediction algorithms is challenging because bitcoin prices fluctuate heavily. Many researchers have attempted to predict the future price of Bitcoin using a variety of methods. This paper presents a web-based application for Bitcoin price prediction using a Hybrid LSTM-GRU model. The experimental results show the results of the Hybrid LSTM-GRU model compared to other models, such as LSTM and GRU. The models were evaluated using various metrics such as mean absolute error, root mean squared error, and mean squared error. The findings indicated that our model outperformed other deep learning models with RMSE, MSE, and MAE values of 0.136, 0.018, and 0.105, respectively. A web application was built using the Streamlit library.

Öykü Yücel, Hasibe Aysan

No abstract is available for this record.

Richard Hanna Beainy, Cesar Kamel

This study investigates the relationship between two of the most important assets in the modern market, Bitcoin and gold. While gold has historically been considered a safe-haven asset, Bitcoin has emerged as a new digital alternative to gold. Using daily data from 2014 to 2025, the study applies a purely quantitative comprehensive mathematical framework that includes risk and return analysis, correlation analysis, regression models, granger causality tests, cointegration analysis, vector autoregression and impulse response functions. The results indicate that Bitcoin is a poor alternative to gold for central banks and hedgers, and a successful substitute for gold for speculators and investors, providing higher returns at a higher risk. Furthermore, even though correlation is low, investors may achieve substantial return by focusing on short term market shocks. One of the important results also include that a significant change in Bitcoin price may influence the price of Gold, but changes in the latter do not impact the former. In conclusion, the study recommends the use of Bitcoin and Gold as complements, not as substitutes.

Darshan BG, Thanushree G, TarunReddy S, Soniya C

The AI-Powered Financial Insights Platform is designed to address the increasing complexity of decentralized applications and digital asset management systems. As blockchain ecosystems expand, users often struggle to interpret detailed transaction data, understand staking mechanisms, or navigate complex on-chain information. This platform leverages advancements in Artificial Intelligence, real-time blockchain indexing, and decentralized protocols to convert unintuitive data into easily interpretable financial insights while maintaining security and trust. By utilizing the Cardano network as its foundation, the platform provides a research-driven, layered architecture that ensures scalability and sustainability as separate principles. This platform represents a paradigm shift in wealth management and fiscal oversight by transitioning from reactive reporting to predictive intelligence. At its core, the system utilizes a sophisticated multi-agent AI architecture designed to ingest, normalize, and analyze massive volumes of heterogeneous financial data. By synthesizing information from global market indices, real-time news sentiment, and individual spending patterns, the platform constructs a 360-degree financial profile. It employs advanced Long Short-Term Memory (LSTM) networks and Transformer-based models to forecast cash flow trajectories and identify potential liquidity risks before they manifest. This proactive approach allows users-whether institutional investors or private individuals-to navigate volatile markets with a data-backed roadmap rather than relying on lagging indicators. Beyond mere data aggregation, the platform emphasizes contextual relevance. The "Insight Engine" utilizes Natural Language Generation (NLG) to translate complex algorithmic outputs into high-level executive summaries, effectively democratizing access to professional-grade financial analysis. Security is woven into the fabric of the application through a hybrid backend-combining the raw computational speed of C++ for high-frequency data processing with the flexibility of Python for AI model deployment. This ensures that the system remains scalable and responsive under heavy loads.

Eren Kurshan, Tucker Balch, David R. Byrd

Generative and agentic artificial intelligence is entering financial markets faster than existing governance can adapt. Current modelrisk frameworks assume static, well-specified algorithms and onetime validations; large language models and multi-agent trading systems violate those assumptions by learning continuously, exchanging latent signals, and exhibiting emergent behavior. Drawing on complex adaptive systems theory, we model these technologies as decentralized ensembles whose risks propagate along multiple timescales. We then propose a modular governance architecture. The framework decomposes oversight into four layers of "regulatory blocks": (i) self-regulation modules embedded beside each model, (ii) firm-level governance blocks that aggregate local telemetry and enforce policy, (iii) regulator-hosted agents that monitor sector-wide indicators for collusive or destabilizing patterns, and (iv) independent audit blocks that supply third-party assurance. Eight design strategies enable the blocks to evolve as fast as the models they police. A case study on emergent spoofing in multiagent trading shows how the layered controls quarantine harmful behavior in real time while preserving innovation. The architecture remains compatible with today's model-risk rules yet closes critical observability and control gaps, providing a practical path toward resilient, adaptive AI governance in financial systems.

Nia Chen, Jennifer Chan, Linh Nghiem

No abstract is available for this record.

Seungju Lee, Jaewook Lee

Spot Bitcoin ETFs, approved in January 2024, trade only during NYSE hours but track an asset that trades around the clock. We study whether this mismatch affects Bitcoin's intraday risk profile using a symmetric one-year difference-in-differences design on hourly Coinbase data. The aggregate US-hour effect is null, but hour-specific and sub-hourly decomposition reveals a volatility spike concentrated in the first 30 minutes of ETF trading (9:30-10:00 ET), the only window surviving multiple testing correction. The pre-open half-hour (9:00-9:30) is insignificant, a pattern more consistent with order flow at the open than with anticipatory positioning. Quantile analysis shows left-tail deepening at the 5th and 10th percentiles of US-hour returns while the median is unaffected, and both tails widen at the opening window. Trading volume surges at both NYSE open and close, but only the open generates a volatility spike, and an ETH/USD comparison on the same exchange, which lacked comparable ETF exposure, shows no similar pattern, together supporting a BTC-ETF-specific interpretation. The findings suggest that clock-bound financial instruments can reshape when risk concentrates in continuous markets.

Maria Andriani Uge, Ignatius Aris Dwiatmoko

In the digital age, Bitcoin remains the first and most notable cryptocurrency. Over the years, its value has increased, making it a desirable digital asset with millions of enthusiasts who trade and invest daily. Bitcoin is highly volatile in comparison with traditional assets and in absolute terms. Understanding its volatility history helps investors decide whether to buy, sell, or hold. A mathematical model that accounts for volatility is essential for these decisions. Unfortunately, Bitcoin’s vast profit potential for investors comes with the dilemma of its negative impact on global environmental health, which needs serious attention. This study aims to model Bitcoin’s return volatility that can support investment decisions and, on the other hand, the negative impact of Bitcoin mining and outline the actions necessary to mitigate it.

Xiuyuan Zhao, Jingyi Liu, Ying Wang, Jiyuan Wang

Cryptocurrency markets exhibit complex microstructural dynamics characterized by high-frequency volatility bursts, rapid regime switching, and long-range temporal dependencies, which expose several limitations of existing volatility forecasting approaches. In particular, attention-based models suffer from prohibitive quadratic computational cost on long high-frequency sequences, while many recurrent architectures struggle to adapt to regime transitions, asymmetric volatility responses, and risk-aware uncertainty estimation. To address these gaps, this paper proposesCryptoMamba-SSM, a novel volatility prediction framework built upon Mamba-based state space models with linear computational complexity. CryptoMamba-SSM integrates selective memory mechanisms with structured state space representations to effectively capture critical market microstructure signals arising from liquidity shocks and sentiment transitions, while dynamically adjusting memory retention across different volatility regimes. This design enables efficient modeling of long-sequence dependencies inherent in cryptocurrency price movements without incurring the computational bottlenecks of traditional attention-based architectures. Through comprehensive experiments on Bitcoin historical data spanning multiple market regimes, we demonstrate that CryptoMamba-SSM consistently outperforms conventional LSTM, GRU, and Transformer baselines, achieving up to a 23.7% reduction in Mean Absolute Error and a 31.2% improvement in directional accuracy. The selective memory mechanism effectively captures regime-switching behaviors and microstructural anomalies, leading to more reliable short-term volatility risk quantification. Moreover, the linear-time complexity of CryptoMamba-SSM enables real-time processing of high-frequency trading data while maintaining strong generalization across diverse market conditions.