ABSTRACT This paper employs deep learning and machine learningâbased NLP models to investigate the impact of the news sentiment on the Bitcoin price. The lagged Bitcoin variables, news indicators, macroeconomic, and financial factors were taken into account to explain the importance of news sentiment on the Bitcoin price. Moreover, FinBERTâbased sentiment scores and semantic features extracted from over 650,000 financial news headlines were integrated with financial and macroeconomic variables. The importance scores of the investigation showed that Bitcoin was largely explained by its lagged price movements, which suggests the speculative nature of the cryptocurrency. However, the investigation also revealed that Bitcoin was significantly influenced by the news sentiment score. In other words, the paper indicates that the movements in the Bitcoin price can be predominantly explained by the news sentiment. Advanced hybrid models (all ML and DL models with the addition of variables obtained with the FinBERT model) were optimized using Optuna and RandomizedSearchCV. The FinBERTâLSTM model achieved the best prediction accuracy. Nevertheless, the main findings indicated that the response of the Bitcoin price to negative news was much stronger than to positive and neutral news. This finding suggests that the asymmetric relationship between the Bitcoin price and news sentiment was evident. GARCHâbased volatility and whatâif scenario analyses further demonstrated that negative sentiment leads to sharper fluctuations in the Bitcoin price. The paper provides important implications for policymakers, portfolio managers, investors, and academics.
This study investigates the application of the Light Gradient Boosting Machine (LGBM) model for both deterministic and probabilistic forecasting of Bitcoin realized volatility. Utilizing a comprehensive set of 69 predictors -- encompassing market, behavioral, and macroeconomic indicators -- we evaluate the performance of LGBM-based models and compare them with both econometric and machine learning baselines. For probabilistic forecasting, we explore two quantile-based approaches: direct quantile regression using the pinball loss function, and a residual simulation method that transforms point forecasts into predictive distributions. To identify the main drivers of volatility, we employ gain-based and permutation feature importance techniques, consistently highlighting the significance of trading volume, lagged volatility measures, investor attention, and market capitalization. The results demonstrate that LGBM models effectively capture the nonlinear and high-variance characteristics of cryptocurrency markets while providing interpretable insights into the underlying volatility dynamics.
Ala Alrawajfi, Mohd Tahir Ismail, Sadam Al Wadi, Saleh Atiewi
Ethereum and other cryptocurrencies are volatile, making Ethereum-USD rate evaluation difficult.Due to unsuccessful data collection and exchange downtimes, financial time series data are incomplete and lacking critical values.Thus, assessments may be incomplete, and trends may be miscalculated.This research builds and tests an ARIMA-random forest data imputation method to overcome these concerns.This innovative strategy uses AutoRegressive Integrated Moving Average (ARIMA) to describe the linear chronologic sequence relationship and random forest to solve nonlinearity.The suggested method uses ARIMA to handle the linear time-dependent data feature and random forest to reduce estimation errors to improve Ethereum-USD closing price estimates.The mean absolute error (MAE) and mean absolute percentage error (MAPE) results demonstrate that the proposed hybrid model significantly outperforms conventional imputation approaches across all missing data levels (10%-50%).For example, at 30% missing data, the hybrid model achieved an MAE of 0.91 and a MAPE of 0.00074, compared to ARIMA's MAE of 2.21 (MAPE 0.00185) and Random Forest's MAE of 2.34 (MAPE 0.00186).Across all scenarios, the hybrid model reduced MAE by up to 60% and MAPE by over 55% relative to the best single-method baseline, indicating superior robustness and accuracy in handling incomplete Ethereum-USD datasets.By providing precise market and result knowledge, these insights help financial analysts, traders, and researchers make accurate, efficient decisions.
Decentralized Finance (DeFi) has undergone significant expansion, evolving from a niche market into a complex alternative financial ecosystem. This burgeoning landscape now encompasses a diverse array of financial services, including decentralized exchanges, lending and borrowing platforms, stablecoins, derivatives, yield optimization services, prediction markets, and privacy-enhancing technologies such as token mixers. While the total value locked in DeFi protocolsâestimated at approximately 77 billion USDâunderscores its increasing significance, it simultaneously highlights the critical necessity for robust security measures. This workshop aims to address the pressing security challenges in the maturing DeFi space by convening leading experts from the fields of cryptography, game theory, economics, and cybersecurity. Our primary objective is to foster interdisciplinary dialogue and showcase cutting-edge research that rigorously examines the current state of DeFi security and charts a comprehensive path forward. The anticipated outcomes include a prioritized research agenda, new collaborative initiatives bridging theoretical advancements with practical implementations, and a strategic roadmap for enhancing security in the rapidly evolving DeFi ecosystem. This year's program features a keynote talk by Prof. Vassilis Zikas, two invited talks by the winners of the Best DeFi Paper Award (theoretical research track and applied research track), and four presentations of accepted original papers, showcasing both fundamental advances and real-world applications.
The rapid evolution of financial technologies (FinTech) and digital assetsâincluding cryptocurrencies, decentralized finance (DeFi), and tokenized capital marketsâhas created an unprecedented need for secure, scalable, and computationally efficient systems. This study examines the transformative potential of quantum computing in reshaping financial technology infrastructures and digital asset ecosystems. Traditional computational models, constrained by classical encryption limits and the exponential growth of financial data, face increasing inefficiencies in handling real-time risk assessment, portfolio optimization, and transaction verification. Quantum computing, with its capacity for superposition, entanglement, and parallel state evaluation, provides novel opportunities to redefine data security, financial modeling, and cryptographic mechanisms in the digital economy. The research explores how quantum algorithmsânotably Quantum Approximate Optimization Algorithm (QAOA), Quantum Fourier Transform (QFT), and Groverâs search algorithmâcan enhance financial operations such as market forecasting, fraud detection, and asset pricing. Additionally, it investigates quantum-resistant cryptography to safeguard digital asset networks against the vulnerabilities introduced by future quantum decryption capabilities. By integrating hybrid quantumâclassical frameworks, this approach enables the development of sustainable, adaptive, and transparent financial systems. The findings highlight quantum computingâs potential to advance financial inclusion, increase transaction speed, and improve systemic resilience. As global financial markets transition toward quantum readiness, the convergence of FinTech and quantum innovation is expected to redefine how digital assets are managed, traded, and securedâmarking a paradigm shift toward quantum financial intelligence.
This paper presents the revolutionary Kera Protocol, a mathematically proven blockchain architecture that fundamentally solves the dual crises of accessibility and sustainability plaguing contemporary decentralized finance systems. The work addresses the stark reality that 99.95% of humanity remains excluded from blockchain validation due to prohibitive capital requirements. The paper's core contribution establishes the first provably sustainable economic model in blockchain history through a dynamic APY allocation algorithm that maintains the fundamental invariant OBLIGATIONS = REVENUE at every 12-second block interval. This mathematical constraint creates theoretical impossibility of protocol insolvency, directly addressing the $108 billion in losses from failed DeFi protocols like Terra/LUNA, Celsius, and BlockFi that promised unsustainable fixed returns. The research introduces an innovative vault-to-pool economic architecture leveraging 20x capital efficiency to deliver mathematically certain 102% APY returnsâderived from real interest accrual rather than speculative mechanisms. Rigorous validation through the MALIV (Multi-Agent Long-term Investment Validator) model simulates 14,600 days across 40 years, incorporating realistic market cycles, black swan events (0.5% probability), and extreme stress scenarios including 99% revenue drops. Across 3,000+ simulation runs, the protocol demonstrated 100% sustainability with perfect equality maintenance. The paper details seven diversified revenue streams projected to scale from $87 million in Year 1 to $35.75 billion by Year 5, eliminating reliance on inflationary tokenomics. Technical innovations include autonomous validator bot systems that eliminate slashing risks, browser-based validation infrastructure, and deflationary token buyback mechanisms. The work represents PhD-level contributions to solving the DeFi Sustainability Trilemma, with planned submissions to leading academic journals in financial economics and computational economics.
We investigate a multi-class machine learning (ML) framework to generate daily Bitcoin trading signalsâBuy, Sell, or Hold. Three algorithmsâXGBoost, LightGBM, and Random Forestâare compared with a naive buy-and-hold strategy. Using BTC/USD daily data (2015â2024), we apply a range of technical indicators across trend, momentum, volatility, and volume, later pruned by correlation analysis. A Âą1% threshold defines the "Hold" zone to avoid minor fluctuations. Empirical tests show that LightGBM outperforms other models and even surpasses buy-and-hold in final portfolio value. Our findings support the design of tri-class ML strategies tailored for high-volatility markets like cryptocurrency.
What are the key factors determining cryptocurrency prices? This study presents a novel perspective that considers the unique characteristics of the cryptocurrency market. While previous studies have used the value-weighted return of the entire cryptocurrency market as a proxy for the market return, this study demonstrates that Bitcoin (BTC) related features serve as the primary determinant of other cryptocurrenciesâ prices. Furthermore, we find that BTCâs own price dynamics are primarily driven by trend-related factors. This finding highlights a fundamental difference in market structure compared to traditional equity markets, where market return as the value-weighted return of the entire stock market is dominant in shaping individual stock prices. To derive these conclusions, this study employs factor analysis using machine learning models such as Random Forest, LightGBM, and Transformer, in addition to a traditional linear predictor, to better capture the complexity of the cryptocurrency market. The findings of this study call for a reconsideration of analytical methods in cryptocurrency pricing and suggest practical implications for BTC-based market analysis and ETF design.
This study examines the intricate relationships between cryptocurrency and various uncertainties related to economic policy and global risk factors. It explores the interactions between cryptocurrency and global risk factors, comparing these with their relationships to different measures of economic policy uncertainty (EPU). We find that cryptocurrency returns are more sensitive to global risk factors than to the country-level EPU. Notably, gold exhibits bidirectional causality with cryptocurrency in returns and volatility. The research sheds light on the dynamic interactions within cryptocurrency markets, underscoring the importance of continuous monitoring and adaptive strategies to navigate the evolving financial landscape of the digital ecosystem.
This study explores how integrating cryptocurrencies into traditional financial portfolios can influence investment performance. Focusing on Bitcoin and Ethereum alongside key European stock indices (BUX, DAX, and FTSE), the analysis examines whether blockchain-based assets can enhance diversification and improve the balance between risk and return. Using weekly market data from 2019 to 2023, the research applies Markowitz meanâvariance optimization to identify optimal asset allocations under different objectives such as maximizing the Sharpe ratio, minimizing risk, and maximizing returns. The findings reveal that cryptocurrencies show weak correlations with European stock indices, suggesting meaningful diversification potential. When included in portfolios, Bitcoin and Ethereum can significantly boost returns, though they also increase volatility. Portfolios optimized for risk reduction favored traditional indices, while those targeting higher returns relied predominantly on cryptocurrencies. Overall, combining digital and conventional assets produced a more balanced performance, with the Sharpe-ratioâmaximized portfolio demonstrating the best tradeâoff between stability and profitability. These results indicate that cryptocurrencies can play a valuable complementary role in modern portfolio construction. They are most suitable for investors willing to accept higher risk in exchange for potentially greater rewards, while more riskâaverse investors may benefit from maintaining a stronger focus on traditional equity indices. The study contributes to understanding how blockchainâdriven assets can expand financial opportunities and supports a broader view of diversification in contemporary investment strategies.
This paper evaluates the performance of classical time series models in forecasting Bitcoin prices, focusing on ARIMA, SARIMA, GARCH, and EGARCH. Daily price data from 2010 to 2020 were analyzed, with models trained on the first 90 percent and tested on the final 10 percent. Forecast accuracy was assessed using MAE, RMSE, AIC, and BIC. The results show that ARIMA provided the strongest forecasts for short-run log-price dynamics, while EGARCH offered the best fit for volatility by capturing asymmetry in responses to shocks. These findings suggest that despite Bitcoin's extreme volatility, classical time series models remain valuable for short-run forecasting. The study contributes to understanding cryptocurrency predictability and sets the stage for future work integrating machine learning and macroeconomic variables.
This research examines deep-learning and machine-learning models for cryptocurrency price prediction, with a keen focus on Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Solana (SOL). Cryptocurrencies exhibit high volatility, non-linear behavior and are able to react strongly to exogenous events, making their prediction and forecasting challenging. The primary aim of this research is to determine which predictive models yield optimal performance in characterizing these complexities and to provide empirical guidance on real-life investment and risk-management applications. Four approaches were used for this forecasting: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), a combination of LSTM-GRU models, and Stochastic Gradient Descent (SGD) regression. The daily historical data were used to train and test each model on different forecast horizons, and performance was measured accordingly by Mean Squared Error (MSE) and Mean Absolute Error (MAE) values. As shown in the results, it can be observed that GRU exhibited the lowest error rates in the majority of the assets, particularly in short-term predictions. LSTM demonstrated a promising ability to capture long dependencies, whereas the hybrid LSTM-GRU system showed a similar performance proficiency by combining the relative superiorities of the two respective models. On the other hand, the conventional SGD regression was the worst among all the deep-learning algorithms, thereby demonstrating the extreme capability of these algorithms in modelling non-linear time sequences. The results confirm GRU as the most viable model for AI-powered crypto prediction and demonstrate the potential of hybrid architecture, at least in certain situations. This study will contribute to the existing debates about the role of deep learning in predicting financial outcomes and provide valuable insights to traders, analysts, and researchers navigating the uncertainties of the digital asset world.
Cryptocurrency markets are characterized by high volatility and complex patterns, creating both challenges and opportunities for traders and investors. This study introduces a machine learning framework for cryptocurrency trading optimization that leverages advanced analytical techniques to enhance trading decisions. We extracted historical data for 30 cryptocurrencies over a four-year period from Yahoo Finance. After preprocessing, we applied Principal Component Analysis (PCA) and K-means clustering to select representative coins. Four machine learning models (Gradient Boosting, XGBoost, Support Vector Regression, and Long Short-Term Memory networks) were trained to predict cryptocurrency price movements. Model performance was evaluated using multiple metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R 2 ). Gradient Boosting and XGBoost consistently outperformed SVR and LSTM models across all cryptocurrencies, with R 2 values of approximately 0.98 for most coins. The framework successfully identified trading signals through both moving average strategies and machine learning predictions, providing actionable insights for cryptocurrency traders. Our analysis demonstrates that ensemble-based models offer superior performance for cryptocurrency price prediction compared to neural network approaches. The integration of advanced visualization tools and trading signal generation creates a comprehensive system for data-driven cryptocurrency trading decisions.
Abstract This research paper provides a comprehensive analysis of Bitcoin, the worldâs preeminent cryptocurrency, focusing on the economic drivers of its price formation, its broader impact on the economy, and the evolving dynamics of its volatility. Drawing on high-frequency econometric modeling, time-series analysis, and network-based prediction methods, the paper synthesizes insights from leading empirical studies to elucidate the factors shaping Bitcoinâs price, including supply-demand fundamentals, investor behavior, macro-financial indicators, transaction network structure, and the influence of derivative markets. Additionally, it explores Bitcoinâs adoption in key industries, its intrinsic and extrinsic value determinants, and the implications of its volatility for financial stability. The study concludes by reflecting on the future trajectory of Bitcoin as it transitions from speculative asset to potential mainstream medium of exchange, considering regulatory, technological, and market challenges. Keywords: Bitcoin, cryptocurrency, price formation, volatility, supply-demand, GARCH, partial differential equations, transaction networks, futures markets, economic impact
This paper investigates the relationship between cryptocurrencies and other financial assets, with a particular focus on the dynamics of information flow between developed and emerging markets. To achieve this objective, the study applies a combined methodology of spillover index analysis and network topology based on graph theory. The analysis covers key cryptocurrencies (Bitcoin and Ethereum), stocks, and conventional currencies over the period November 2017 to September 2022, and distinguishes between short-term and long-run dynamics. The empirical findings show that in the short run, Bitcoin and Ethereum predominantly act as net shock transmitters, whereas in the long run, stocks and conventional currencies, together with Bitcoin and Ethereum, become the principal conveyors of spillover shocks. The network topology analysis corroborates these results by revealing the centrality of these assets in the spillover structure. By integrating spillover and network approaches across different markets and time horizons, this study contributes to the literature by providing a more nuanced understanding of how cryptocurrencies interact with traditional financial assets under varying market conditions.
This research paper presents the design and development of a Cryptocurrency Dashboard that applies data analytics techniques to the financial technology sector. The goal of this project is to visualize historical cryptocurrency data such as market capitalization, trading volume, and price fluctuations through an interactive and user-friendly interface. Using tools such as Python and Power BI, data was collected, cleaned, analyzed, and visualized to provide dynamic insights for investors and analysts. The dashboard enables efficient decision-making by simplifying complex financial data into clear and interpretable visuals. The study demonstrates how data analytics enhances understanding of cryptocurrency trends and contributes to evidence-based financial analysis in the digital economy.
Sr IT Developer, First Horizon Bank, Memphis, TN, USA, Rushikesh Anantrao Deshpande
The article examines methods for optimizing PL/SQL queries in distributed banking databases, emphasizing the transition from static rule-based mechanisms to adaptive, learning-driven architectures. The studyâs relevance is defined by the increasing complexity of financial data environments that require real-time consistency, fault tolerance, and intelligent workload distribution. The research synthesizes results from seven recent works published between 2021 and 2025, covering neural cost modeling, heuristic algorithms, hybrid plan enumeration, and visualization-based diagnostics. Special attention is devoted to learned cost models and metaheuristic strategies that enhance selectivity estimation, reduce latency, and stabilize throughput in distributed ledger systems. The methodological framework integrates comparative analysis, systematization, and critical evaluation of hybrid, heuristic, and learning-based optimizers. The findings reveal a multi-layered optimization model that combines probabilistic inference, robust plan selection, and heuristic refinement. The conclusions underscore the practical applicability of adaptive PL/SQL optimization for high-volume banking infrastructures and data-intensive financial analytics.
Oct 30, 2025¡2025 1st IEEE Uttar Pradesh Section Women in Engineering International Conference on Electrical Electronics and Computer Engineering (UPWIECON)
The decentralized structure, lack of regulation, and susceptibility to manipulation of Bitcoin markets result in a high level of volatility, which presents substantial obstacles to the accurate prediction of prices. Traditional statistical models, like ARIMA, frequently fall short in describing the dynamic and nonlinear nature of bitcoin markets. In order to overcome this constraint, this research utilizes sophisticated machine learning and deep learning techniques, including as Convolutional Neural Networks (CNN), Decision Trees, Long Short-Term Memory (LSTM), and Logistic Regression, to predict changes in the price of Bitcoin. Using historical Bitcoin datasets, the suggested models are trained and assessed using performance measures like accuracy and RMSE. In comparison to traditional techniques, experimental results show that deep learning modelsâin particular, LSTMâachieve greater prediction accuracy, offering a more dependable framework for forecasting bitcoin prices.
In the rapidly evolving decentralized finance landscape-where retail traders struggle to compete alongside institutional traders, this project provides a dedicated, Artificial Intelligence (AI) -powered trading assistant equipped with the ability to unlock professional(pro) trading strategies without any need to code. By increasingly integrating a trained machine learning model, where the database consists of over 1 million Solana memecoin data points, with a conversational AI interface and on-demand, blockchain-level analytics, the system enables users to trade on tokens such as Base Mainnet or Solana assets with one natural language command. The AI queries live Decentralised Exchange (DEX) data through The Graph protocol, determines matches, and generates$\mathbf{1 5}$-minute price predictions using the Long-Short Term Memory (LSTM) neural network. The AI performs all processes autonomously, allowing it to manage wallets and make trades using an independently validated Return of Interest (ROI) of 30.57 %. The platform simplifies candlestick patterns, liquidity-level data, and market indicators into chat-based task workflows over a 3-step process. The model is created with TensorFlow for predictive analytics, Collateralised Debt Position (CDP) Agents for engagement, and uses Coinbase's Software Development Kit (SDK) for trading. This research proves that AI can level the playing field for casual traders using complex algorithmic trading strategies and data through simple human engagement.