Blockchain Papers

Achraf Yahia, Yassine Mouhssine, Abdelkader El Alaoui, Saïd Ouatik El Alaoui

No abstract is available for this record.

Rabia Arshad, Muhammad Milhan Afzal Khan, Saman Rasheed, Irtaza Ijaz · 5 authors

Blockchain technology has transformed decentralized data exchange and digital payments but the consistently high gas prices pose a significant challenge to its scalability and efficiency. This research explores the role of AI-driven gas price prediction and data compression methods on gas utilization in blockchain systems with special emphasis on Ethereum transactions. Using actual Ethereum transaction history, we compare the performance of compressed versus uncompressed payloads with three different compression algorithms: Zlib, Brotli, and Gzip. Beyond that, a linear regression model is also trained to forecast hourly gas Price fluctuations given past transaction history. The methodology includes thorough statistical analysis to provide accurate and reproducible results. Our results show that compressing text data over 141 bytes using the Zlib algorithm prior to making transactions on the Ethereum network decreases the amount of gas Used without altering system time. This validates the efficiency of combining data compression with gas price forecasting in minimizing transaction costs without affecting performance. Moreover, our study further encompasses investigation of actual gas Price trends and provides real-world insights for optimizing timing strategies for economic transaction execution. These results enhance the knowledge of Ethereum gas dynamics and provide valuable solutions for enhancing economic efficiency and resource utilization in applications based on blockchain. Future efforts will involve applying the framework to the Ethereum mainnet, using deep learning models for increased prediction accuracy, and adaptive compression dependent on network state and transaction size.

Fozia Zeeshan, R Yalda, Narayan Nepal

This study presents a comprehensive framework that integrates deep learning and blockchain security to address key challenges in cryptocurrency forecasting and privacy preservation. A state-of-the-art ensemble machine learning model, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, is proposed for Bitcoin price prediction. The model achieves 92.1% accuracy on out-of-sample data following rigorous validation, demonstrating strong forecasting performance. To address fundamental security and privacy concerns in blockchain systems, a dynamic privacy framework is proposed, which integrates Zero-Knowledge Proofs (ZKPs) and adaptable consensus methods to improve transaction confidentiality, scalability, and adherence to regulations.

Kareem Kamal, Khaushbakht Kamal, Kainat Mustafa, Rashid Kamal · 9 authors

Cryptocurrency price prediction poses significant challenges due to the inherent volatility and nonlineardynamics of the market. This study introduces a hybrid stacked modeling framework that integrates machine learning (ML) and deep learning (DL) techniques, capitalizing on their complementary strengths-ML models are effective at capturing nonlinearfeature interactions in structured data, while DL architectures are adept at modeling temporal dependencies in sequential data. The proposed model leverages historical price data, technical indicators, macroeconomic variables, and sentiment metrics, with feature engineering applied to enhance predictive capability. Empirical evaluation was conducted through two experimental setups: (i) short-term, monthly segment analysis and (ii) long-term generalization via five-fold cross-validation. The hybrid model outperformed individual baseline models, achieving up to 18.3% lower RMSE and 6.7% higher directional accuracy. Additionally, it yielded superior risk-adjusted returns, with Sharpe Ratios reaching 0.094 on the Ethereum dataset. Beyond technical improvements, this research offers foresight into digital financial markets, providing a robust tool for investors, institutions, and policymakers navigating the evolving cryptocurrency landscape. The model supports more informed decision-making, enhances market oversight, and contributes to the development of adaptive regulatory frameworks for digital finance.

Alexandr Kuznetsov, Олексій Костенко, K.O. Klymenko, Zoriana Hbur · 5 authors

Blockchain-based cryptocurrency markets present unique analytical challenges due to their decentralized nature, continuous operation, and extreme volatility. Traditional price prediction models often struggle with the binary trade execution problem in these markets. This study introduces a confidence-based classification framework that separates directional prediction from execution decisions in cryptocurrency trading. We develop a neural network system that processes multi-scale market data, combining daily macroeconomic indicators with a high-frequency order book microstructure. The model trains exclusively on directional movements (up versus down) and uses prediction confidence levels to determine trade execution. We evaluate the framework across 11 major cryptocurrency pairs over 12 months. Experimental results demonstrate 82.68% direction accuracy on executed trades with 151.11-basis point average net profit per trade at 11.99% market coverage. Order book features dominate predictive importance (81.3% of selected features), validating the critical role of blockchain microstructure data for short-term price prediction. The confidence-based execution strategy achieves superior risk-adjusted returns compared to traditional classification approaches while providing natural risk management capabilities through selective trade execution. These findings contribute to blockchain technology applications in financial markets by demonstrating how a decentralized market microstructure can be leveraged for systematic trading strategies. The methodology offers practical implementation guidelines for cryptocurrency algorithmic trading while advancing the understanding of machine learning applications in blockchain-based financial systems.

Anusuri Srinivas, K. Vijayalakshmi

No abstract is available for this record.

Gopinath Ramisetty

Modern economic ecosystems require radical hazard management systems that may take care of big streams of statistics without compromising on regulatory compliance and business transparency. Conventional batch-based risk assessment models exhibit intrinsic shortcomings in addressing millisecond-level market turbulence and intricate network interdependencies that define new trading environments. Sophisticated artificial intelligence platforms embedded in distributed computing environments offer transformational possibilities for real-time risk sensing and mitigation. The suggested architecture develops end-to-end risk analytics capacity via ensemble machine learning algorithms, graph contagion analysis, and explainable AI features to meet strict regulatory demands. Complex data pipelines ingest heterogeneous finance streams from worldwide exchanges, payment networks, and blockchain ledgers in tandem. Tailored graph neural networks examine systemic risk transmission patterns in connected financial institutions while retaining dynamic relationship mapping capabilities. Explainable AI integration presents version interpretability and regulatory adherence through function attribution strategies and robust audit trail retention. Cloud-local infrastructure layout helps elastic scaling throughout multi-cloud environments using fault-tolerant distributed orchestration systems. Performance assessments display large upgrades in detection latency and predictive accuracy relative to standard batch-processing strategies. The design embodies a paradigm shift towards forward-looking, adaptive, and transparent risk management functionality critical to ensuring financial stability in progressively complex market conditions

Fabricio Marcillo, Melissa Quiñonez, Patricio Neptali Vaca Escobar, Graciela Trujillo

El estudio analizó el comportamiento de las criptomonedas Bitcoin y Ethereum durante el año 2024 mediante la construcción de un modelo estadístico ARIMA (AutoRegressive Integrated Moving Average). La investigación utilizó un enfoque cuantitativo que se dividió en cuatro etapas: recopilación y limpieza de datos históricos, verificación de la estabilidad de los datos, identificación y estimación de los mejores parámetros usando criterios de información, y validación mediante medidas de precisión y análisis de residuos. Los resultados demostraron que el modelo ARIMA fue útil en el pronóstico de valores en mercados estables, destacando su trayectoria en el análisis de datos financieros. Además, los valores bajos de RMSE y MAPE validaron que el modelo tiene la capacidad de realizar pronósticos precisos en escenarios con alta frecuencia. En particular, el MAPE de Bitcoin fue 2,25 % y el de Ethereum 2,85 % durante la etapa de prueba, demostrando que los valores pronosticados tuvieron una ligera desviación con respecto a los reales. No obstante, el modelo puede verse afectado en periodos de alta volatilidad, como en las burbujas especulativas o en los desplomes bursátiles, ya que no tiene la capacidad de adaptarse dinámicamente a cambios súbitos en los parámetros; sin embargo, su utilidad puede mejorar al combinar modelos híbridos con ARIMA.

Haolin Huang, Basem Suleiman, Ali Anaissi, Muhammad Johan Alibasa

Predicting cryptocurrency prices has become increasingly challenging due to their limited trading history and pronounced price volatility. Similar to traditional stock markets, where investor sentiment is significantly influenced by news and social media interactions, Twitter has recently emerged as a prominent indicator of Bitcoin price movements. This study investigates the influence of notable tweets on Bitcoin price fluctuations. In this paper, we present a method to extract hourly and daily impactful tweets related to Bitcoin by leveraging the Twitter network's topology. Furthermore, we propose a Bitcoin price prediction approach that consists of (a) a Twitter sentiment index model, constructed using data from influential tweets, and (b) a time-series XGBoosting model utilising Bitcoin price features derived from historical price data. Through an experimental evaluation, conducted on a substantial dataset comprising 1.8 million tweets, 400k Twitter users, and two months of Bitcoin price data, our model demonstrated superior performance, achieving a MAPE of 0.56%, an R-Square value of 0.99, a Pearson Correlation Coefficient of 0.99, and a Direction Accuracy of 92.06%. Based on the feature importance analysis of our model, we identify that the sentiment index and Bitcoin trade volume wield significant influence over Bitcoin price dynamics. The two-month time range may limit generalization to other market conditions.

Dasril Aldo, Dimas Fanny Permadi Hebrasianto, Dedy Agung Prabowo, Miftahul Ilmi · 5 authors

Cryptocurrency markets are highly volatile, posing significant challenges for accurate price prediction. Solana (SOL), one of the largest cryptocurrencies by market capitalization, experiences sharp fluctuations that limit the effectiveness of traditional linear models such as Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH). To address this, the present study evaluates twelve Long ShortTerm Memory (LSTM) architecture variants, including vanilla LSTM, stacked LSTM, bidirectional LSTM, and hybrid models with attention and dropout mechanisms, for forecasting daily closing prices of the SOL/USD pair. Five years of OHLCV data from Coinbase were used, with an $80 / 10 / 10$ split for training, validation, and testing. Performance was assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and $\mathbf{R}^{\mathbf{2}}$. Results show that the LSTM-Base model (60-day window, 1-day horizon) achieved the best accuracy (MAE $=5.38$, RMSE $=6.73$, MAPE $=3.27 \%, \mathbf{R}^{\mathbf{2}}=\mathbf{0. 8 2}$), while LSTM-LB120-H7 (120-day window, 7-day horizon) performed poorly (MAE $=14.09$, RMSE $=17.44, \mathbf{R}^{2}=-0.34$). These findings highlight that simpler LSTM configurations are more effective for volatile crypto assets, offering both academic insight and practical benchmarks for traders, analysts, and policymakers.

Samsudin Samsudin, Muhammad Dedi Irawan, Muhammad Irwan Padli Nasution, Raissa Amanda Putri

Bitcoin’s extreme price volatility has long posed challenges for both investors and researchers seeking reliable forecasting models. Conventional financial approaches often fail to capture the highly complex, nonlinear, and fast-moving nature of cryptocurrency markets. To address this gap, this study develops a Bitcoin price prediction model using Random Forest Regression based on on-chain market data. The dataset was obtained from publicly available historical Bitcoin daily trading records spanning more than five years. Key features include opening price, daily high and low ranges, trading volume, and percentage change. The research was carried out in several stages. First, data preprocessing was conducted through normalization, handling of missing values, and feature engineering. Second, model training was performed with Random Forest, including parameter tuning to optimize predictive accuracy. Third, model evaluation employed R² and Mean Absolute Percentage Error (MAPE) as primary performance indicators. Fourth, visualization was implemented using interactive charts to allow users to observe short-term price fluctuations and long-term market patterns. The system development followed an iterative methodology inspired by the Streamlit Framework, which is an open-source Python library that simplifies building interactive web applications for data science and machine learning. This approach provides flexibility, enabling rapid experimentation and adaptation to evolving market conditions. The results show that the proposed model achieves near-perfect R² values (approaching 1.0) with consistently low MAPE, highlighting its reliability. Beyond predictive performance, the framework is designed to be scalable, supporting future integration with deep learning methods such as LSTM and external macroeconomic indicators, thus offering both practical utility for investors and academic contributions to decentralized finance research.

Puguh Hiskiawan, Jovan William, Louis Feliepe Tio Jansel

Bitcoin, a highly volatile and decentralized digital asset, presents considerable challenges for accurate price forecasting. This study proposes an applied data science framework that compares traditional statistical approaches with modern Artificial Intelligence (AI)-based models to predict Bitcoin’s daily closing price. Using BTC-USD historical data from January 2020 to December 2024, we converted prices into Indonesian Rupiah (IDR) to increase local relevance. Our forecasting horizon is 30 days, based on a 60-day lookback window. We evaluate six models: Linear Regression, ARIMA, and Prophet as traditional techniques, alongside Random Forest, XGBoost, and Long Short-Term Memory (LSTM) networks as AI approaches. All models were trained using lag-based or sequence-based time series features and evaluated using MAE, RMSE, R², MAPE, and SMAPE. Results show that AI models, particularly LSTM and XGBoost, offer better performance in capturing short-term non-linear dynamics compared to traditional models. LSTM provides high accuracy, though with greater computational demand, while XGBoost strikes a balance between speed and precision. Prophet and ARIMA remain effective for quick and interpretable forecasts but struggle with abrupt trend shift common in cryptocurrency markets. In addition to performance metrics, we include a robustness analysis based on median absolute error and outlier detection to assess model stability under extreme variations. Visual analytics—including forecast curves, error distributions, and uncertainty bounds—help interpret and communicate model behavior. This comprehensive evaluation offers practical insights for investors, analysts, and fintech practitioners, and the pipeline can be extended to other volatile assets.

Binh Thanh Nguyen, Thanh Tuan Chu, Son Ha, Anh Tuan Nguyen

Purpose Our research augments the expanding body of literature concerning the capability of prevalent Large Language Models (LLM) tools in supporting financial professionals. We introduce a framework to leverage ChatGPT to assess market sentiment through the analysis of social media data. Design/methodology/approach We use the LLM models to construct market sentiment indicators based on Twitter tweets and use those indicators to explain Bitcoin return. Findings Our analysis uncovers that sentiment indicators crafted with ChatGPT4o/ChatGPT3.5 significantly affect Bitcoin returns, even when accounting for a broad array of control variables and other pre-established sentiment indicators. Originality/value These insights imply that ChatGPT4o/ChatGPT3.5 could empower financial professionals to discover sentiment information from Twitter tweets that were overlooked by previously introduced sentiment indicators concerning Bitcoin.

Mohammad Vahidpour, Amir Daneshvar, Mohsen Amini Khouzani, Mahdi Homayounfar

Purpose This study aims to enhance cryptocurrency price and trend prediction by applying advanced machine learning (ML) techniques. Given the market’s high volatility and complexity, the research identifies effective models for different conditions, providing insights for investors and risk management. Design/methodology/approach This study proposes a six-stage framework for cryptocurrency price prediction, integrating advanced ML techniques. Data from ten cryptocurrencies are processed, extracting 37 key features, including return, the Fear and Greed Index and various technical indicators. The model employs Deep Q-Networks (DQN), Long Short-Term Memory (LSTM) and multiple regression methods such as linear regression, support vector regression, ridge, LASSO, decision tree, Random Forest, multi-layer perceptron, stochastic gradient descent, elastic net and Bayesian regression. Model performance is evaluated using trading strategies and metrics like accuracy, sensitivity, recall, MSE, MAE and F1-score. Findings The results indicate that complex models like DQN and LSTM excel in volatile markets due to their ability to capture intricate price patterns, whereas simpler models such as linear regression and ridge regression perform better in stable conditions. The multi-layered parallel design enhances computational efficiency, enabling independent asset evaluation. These findings highlight the potential of artificial intelligence in improving prediction accuracy and supporting informed investment decisions. Originality/value This research introduces a novel six-stage ML framework incorporating diverse predictive models and key features for cryptocurrency forecasting. The multi-layered parallel approach enhances computational efficiency, setting this study apart from existing research. The comparative analysis of models offers valuable guidance for investors, traders and financial analysts navigating volatile cryptocurrency markets.

K IYANA

Multi-Agent AI Systems (MAS) rely on the cooperative actions of autonomous agents to meet difficult and rapidly changing issues in analysis and business strategy. In contrast to single-agent models, MAS includes different agents that team up, change as needed and function in real time. Thanks to its decentralized and modular design, businesses can scale their activities, maintain good stability and flex their operations as market situations change. With the help of advanced AI like Generative AI, MAS can examine huge datasets, perform market simulations and support smart decisions from leaders. Such algorithms are applied to everything from setting creative prices to improving supply chains, assessing risks and detecting fraud in the financial industry. The use of MAS makes it possible for tasks to be split and completed by multiple processors, which helps reduce workflow trouble spots. Additionally, its ability to respond to uncertainty and make quick, real-world decisions makes MAS a vital instrument for industries needing both agility and innovation. With MAS, organizations become stronger competitors by streamlining their work processes, encouraging innovation and solving problems on many scales. The future success of MAS comes from its power to change how businesses run smoothly by working with present technology and developing together with the company's needs.

M Aswadi, Usman Ependi

Cryptocurrency is a highly volatile digital asset, necessitating accurate and adaptive forecasting methods. This study implements a Long Short-Term Memory (LSTM) model to predict the daily closing prices of two leading cryptocurrencies Bitcoin (BTC) and Ethereum (ETH) using historical data from Yahoo Finance and Binance. To enhance data richness and model robustness, datasets from both sources were vertically merged. The methodological framework included data preprocessing, Min–Max normalization, formation of 24-day sliding input windows, and training across three data split ratios (70:30, 80:20, and 90:10). Model performance was evaluated using the Root Mean Squared Error (RMSE). Results indicate that the LSTM model achieved high prediction accuracy, with the lowest RMSE values of 0.0137 for BTC and 0.0152 for ETH using the combined dataset with a 90:10 split. Beyond modeling, a web-based application was developed using Streamlit, enabling users to perform real-time predictions and export results. This study contributes to the field of cryptocurrency forecasting by demonstrating that multi-source data integration significantly improves predictive accuracy and model generalization. The proposed framework offers both theoretical insights and practical tools for researchers and investors in financial technology.

Dimple Tiwari, Bhoopesh Singh Bhati, Bharti Nagpal, Nazik Alturki · 5 authors

Cryptocurrencies have emerged miraculously all over the globe due to their legitimacy, transparency, immutability, and the traceability that blockchain technology provides. However, the benefits it provides are dwarfed by how unpredictable and extremely price-volatile the cryptocurrencies are. That makes it really tough for investors to find their profitable opportunities in such volatile markets. Social media sources, like Twitter and Reddit, have evolved as crucial tools of sentiment estimation above the explosively volatile price movements of decentralized currencies. Here we introduce an attention-based hybrid CNN-LSTM model optimized for social media sentiment analysis to use them towards investment decisions in a broad portfolio of cryptocurrencies. The existing Convolutional Neural Network (CNN) effectively extracts the essential features, and Long Short-Term Memory (LSTM) has the potential to capture the long dependencies between phrases. Although these models can process massive textual data, they limit treating all the features equally important. Therefore, the proposed model induces the attention mechanism into hybrid CNN-LSTM for emphasizing more or fewer weights on different words according to their contributions and optimizes the parameters of employed neural networks using grid search. In our pipeline, the attention-augmented CNN-LSTM first transforms each tweet/review into a 512-dimensional task-specific embedding; a calibrated radial-basis SVM then serves as the final decision layer, refining the margin for classes that the neural network alone tends to blur. This sequential ('deep-features-plus-SVM') architecture boosts F1 by 3.2 pp over a pure Softmax head while adding only 0.4 ms of inference time. Extensive experiments conducted on cryptocurrency-related tweets and Reddit reviews reveal the outperformance of the proposed model over existing Deep Neural Networks (DNNs) and state-of-the-art models. Trained on 9.9 k crypto-tweets and 33 k Reddit comments, AEH attains 98.7% accuracy, 0.987 F1, and κ = 0.94, outperforming strong baselines (pure LSTM + 8.3 pp; pure CNN + 19.3 pp) and the widely-used VADER toolkit (+ 11.8 pp). On the forecasting side, a complementary GRU regressor trained on eight-year price series yielded MAE = 0.0315, MAPE = 5.95%, and MSE = 0.0022 for Bitcoin, beating an ARIMA benchmark at p < 0.001. The primary objective of the proposed hybrid model attributed to processing huge social sentiments with an attention mechanism to break the dilemma of cryptocurrency investors.

Franco Farrugia, Cedric Deguara

No abstract is available for this record.

Yuankui Wang, Mohd Fahmi Ghazali, Ruzanna Ab Razak, Mohd Azlan Shah Zaidi

This study applies Phase Space Reconstruction and Phase Space LSTM to analyze Bitcoin’s interactions with Gold, S&P 500, U.S. Bonds, EUR/USD, and Crude Oil, revealing hidden dependencies and chaotic structures in financial markets. Study implement a multi-method validation framework combining the Rosenstein algorithm for Lyapunov exponent estimation, 0 − 1 test for chaos and BDS test to provide robust evidence for deterministic chaos. Results indicate that most assets exhibit deterministic chaos, with price evolution highly sensitive to liquidity conditions and macroeconomic forces. Phase space analysis conducted in optimal four-dimensional embeddings uncovers stronger predictive linkages between Bitcoin and U.S. Bonds, reinforcing its growing dependence on global financial conditions. The application of PS-LSTM significantly enhances forecasting accuracy, demonstrated through rigorous validation including statistical significance testing and economic significance evaluation using risk-adjusted performance metrics. These findings suggest that cryptocurrencies are not isolated assets but deeply entangled with systemic financial fluctuations, necessitating a reassessment of market stability and risk propagation through the lens of statistical mechanics and econophysics. • PSR reveals hidden dependencies across Bitcoin, gold, stocks, bonds, exchange rate and commodities. • Phase space analysis reveals that Bitcoin-bond linkages indicate macroeconomic integration. • Phase Space LSTM (PS-LSTM) enhances forecasting accuracy, reducing overfitting and improving predictive stability across all assets. • PS-LSTM reduces overfitting and improves forecasting across all asset classes. • Chaos detection confirms the presence of nonlinear dynamics in cryptocurrency and commodity markets. • Higher-dimensional embeddings enhance the detection of causality between financial assets.

Ahmad Asadi, Reza Safabakhsh

No abstract is available for this record.

Maryamsadat Bagheri, Paolo Giudici

Forecasting the price of bitcoin assets is a difficult task, especially as bitcoins are highly volatile and speculative. In this paper we leverage the non linear capability of deep and machine learning models to enhance bitcoin forecasts. We propose a systematic comparison of different deep learning and machine learning models, based on their Accuracy, Security and Explainability characteristics. The empirical findings reveal that, while CNN-GRU, GRU and LSTM are the most accurate models, for maximum cumulative return and risk adjusted performance GRU and CNN are preferred. Whereas, for transparent and stable decision-making, Random Forest and XGboost are a good choice and, for robustness, CNN and LSTM are the best choice. Ultimately, the choice of a model depends on the objectives of the analysis.

Chen, Chi-Sheng, Aidan Hung-Wen Tsai

This study presents a comprehensive empirical comparison between quantum machine learning (QML) and classical machine learning (CML) approaches in Automated Market Makers (AMM) and Decentralized Finance (DeFi) trading strategies through extensive backtesting on 10 models across multiple cryptocurrency assets. Our analysis encompasses classical ML models (Random Forest, Gradient Boosting, Logistic Regression), pure quantum models (VQE Classifier, QNN, QSVM), hybrid quantum-classical models (QASA Hybrid, QASA Sequence, QuantumRWKV), and transformer models. The results demonstrate that hybrid quantum models achieve superior overall performance with 11.2\% average return and 1.42 average Sharpe ratio, while classical ML models show 9.8\% average return and 1.47 average Sharpe ratio. The QASA Sequence hybrid model achieves the highest individual return of 13.99\% with the best Sharpe ratio of 1.76, demonstrating the potential of quantum-classical hybrid approaches in AMM and DeFi trading strategies.

K. Nirmala Devi, Lakshmi Narasimha, N. Sujatha, Y. Geetha · 6 authors

The integration of blockchain technology into financial markets has sparked significant scholarly interest, particularly in the context of stock market prediction. This bibliometric analysis aims to provide a comprehensive overview of research trends, influential publications, and emerging themes within this interdisciplinary domain from 2018 to 2025. Drawing data from Scopus the study utilizes bibliometric tools such as Biblioshiny and VOSviewer to analyse publication outputs, citation patterns, co-authorship networks, and keyword co-occurrence. The findings reveal a consistent growth in academic contributions, especially after 2019, reflecting blockchain’s increasing relevance in financial prediction and its convergence with machine learning, deep learning, and artificial intelligence. Key research clusters identified include algorithmic trading, decentralized finance (DeFi), cryptographic modelling, and predictive analytics. The analysis also highlights leading journals, authors, and institutions contributing to the advancement of this field. However, certain limitations are acknowledged. The focus on selected databases may have excluded valuable contributions from platforms such as IEEE Xplore, SSRN, or non-indexed proceedings. Additionally, the keyword-based search strategy may have overlooked studies using alternative terminologies. The temporal scope may also bias the analysis toward recent developments while underrepresenting foundational research. The study offers a valuable reference point for scholars and practitioners, mapping the intellectual structure and thematic progression of blockchain-based stock prediction research. Future studies are encouraged to adopt multi-database approaches, combine quantitative and qualitative methods, and explore regulatory and regional variations to enrich understanding and guide practical implementation.

Samad Wali, Muhammad Irfan Khan, Noshaba Zulfiqar

No abstract is available for this record.