Abstract This study investigates the relationship between Facebook sentiment and Bitcoin market dynamics using AI-based emotion detection. We analyze 120,000 Facebook posts collected via CrowdTangle alongside Bitcoin financial data from the Blockchain Research Center, covering 2015–2023. Employing FinBERT for sentiment classification, we develop novel compound sentiment scores that integrate text-based sentiment with Facebook’s multi-reaction engagement system, then apply four analytical components: sentiment analysis, Dynamic Topic Modeling, sentiment-based trading strategies, and machine learning volume prediction. Results demonstrate that Facebook sentiment has substantial predictive power for Bitcoin trading volume. Sentiment-based trading strategies significantly outperform buy-and-hold, achieving superior cumulative returns and risk-adjusted performance. For volume prediction, Linear Regression and Bidirectional LSTM achieve comparable test performance, indicating that model complexity does not guarantee superior prediction. Topic modeling reveals that cryptocurrency investment and trading discussions dominate Bitcoin discourse on Facebook, with themes evolving over time in response to market conditions. This research contributes by being the first to apply post-level NLP sentiment analysis of Facebook data to cryptocurrency markets, extending beyond the Twitter and Reddit focus of prior research. The findings provide practical tools for traders and analysts navigating volatile digital asset markets while demonstrating that Facebook’s demographically diverse user base and rich reaction system offer unique advantages for sentiment quantification.
A. B. Hajira Be A. B. Hajira Be, S.Bhuvaneshwari S.Bhuvaneshwari, Sankari.S Sankari.S
Cryptocurrency markets have gained significant global attention due to their decentralized nature and high financial value. Among various cryptocurrencies, Bitcoin is the most widely traded and exhibits highly volatile price behavior. Accurate analysis and prediction of Bitcoin price trends are challenging because the market is influenced by rapid trading activities, large data streams, and complex temporal patterns. This paper presents a streaming data collection and analysis system for Bitcoin using the Long Short-Term Memory (LSTM) deep learning algorithm. The proposed system continuously collects real-time Bitcoin market data from online cryptocurrency exchanges through streaming APIs. The collected data is then preprocessed and analyzed using an LSTM-based predictive model capable of learning long-term dependencies in time-series data. The LSTM network processes sequential historical price data to forecast future market trends and provide analytical insights into Bitcoin price movements. The system integrates data acquisition, preprocessing, deep learning-based prediction, and visualization modules to create an efficient cryptocurrency analysis framework. The proposed approach focuses on improving prediction accuracy by combining real-time streaming data with advanced neural network models. This system can assist researchers, financial analysts, and investors in understanding cryptocurrency market behavior and making informed trading decisions. The proposed design demonstrates the feasibility of integrating streaming data technologies with deep learning models for real-time financial market analysis. Keywords— Cryptocurrency, Bitcoin, Streaming Data, LSTM Algorithm, Deep Learning, Time-Series Prediction, Financial Data Analysis.
SAMUEL OBOH, Boniface Dondo, S. Yakura Bassa, Gambo I. Bature
Ethereum, a leading digital asset by market value, has gained increasing attention from investors and researchers because of its high price volatility and market unpredictability. This study forecasts Ethereum cryptocurrency daily closing prices using the Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) methodology, drawing on data from January 1, 2019, to December 31, 2025. Stationarity analysis via the Augmented Dickey-Fuller ADF and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) tests confirmed that first differencing was required to render the series suitable for the modeling. Through systematic model identification, estimation, and comparison of ten candidate ARIMA specifications, the ARIMA(1,1,0) model emerged as the optimal fit, yielding the lowest information criterion values of Akaike information criterion, Bayesian information criterion (AIC = 24,943.883; AICc = 24,943.84; BIC = 24,955.12). Residual diagnostic tests, including the Ljung-Box test for serial correlation, the Autoregressive Conditional Heteroskedasticity (ARCH-LM) test for heteroscedasticity, and the Shapiro-Wilk test for normality, confirmed that the model residuals are free of serial dependence, although they exhibit time-varying volatility and non-normal distribution, features commonly associated with financial time series. The fitted model was subsequently applied to generate 30-day ahead forecasts with 95% confidence intervals, revealing relatively stable price expectations in the near term alongside progressively widening prediction bands that reflect growing uncertainty over longer horizons. These findings underscore the practical utility of the parsimonious ARIMA(1,1,0) model as a transparent and accessible tool for short-term Ethereum-price forecasting and investment risk assessment.
The paper aimed to investigate the statistical relationship between Bitcoin prices and Ethereum trading volumes, as well as to create a simple predictive model for Ethereum trading volumes based on Bitcoin prices. To perform Spearman’s rank correlation analysis and to construct an artificial neural network (ANN) model, daily closing prices of Bitcoin in USD and daily trading volumes of Ethereum were utilized. The timeframe covered by the data starts May 1, 2020 and ends November 22, 2025. In this study, Ethereum volumes were treated as the dependent variable, while Bitcoin prices served as the independent variable. The findings indicate a significant, moderate, positive correlation between Bitcoin prices and Ethereum volumes, and the ANN model successfully predicted Ethereum volumes with a high level of accuracy. These results reinforce existing evidence regarding the relationships among cryptocurrencies. Furthermore, by confirming the efficacy of artificial neural networks (ANN) in predicting trends within the cryptocurrency market, the study also makes a methodological contribution. In addition, the study also offers a simpler modelling approach that highlights the significance of bilateral interactions among major cryptocurrencies through a single-input model. Based on the impressive performance of the ANN model, exchanges, fintech companies, and investment firms could incorporate lightweight machine-learning systems into their forecasting tools to provide real-time analytics with minimal processing requirements.
Cryptocurrencies have transformed the modern financial system by introducing decentralized digital payment methods that operate without the need for traditional banking institutions. Bitcoin, introduced in 2009 by Satoshi Nakamoto, was the first successful cryptocurrency and remains the most dominant digital currency in the market. Built on blockchain technology, Bitcoin enables secure peer-to-peer transactions through cryptographic techniques and distributed ledger systems. As the popularity of cryptocurrencies has grown, large volumes of transaction data, market trends, and online user activity have created opportunities for advanced data analysis. Artificial Intelligence (AI) and Machine Learning (ML) techniques are increasingly being applied to cryptocurrency-related challenges such as price prediction, trend analysis, fraud detection, volatility forecasting, portfolio management, and mining optimization. At the same time, issues such as privacy, security, scalability, and cyber threats continue to affect the cryptocurrency ecosystem. This paper explores the relationship between Bitcoin, blockchain technology, and artificial intelligence, while examining how AIbased approaches can improve the efficiency, reliability, and security of cryptocurrency systems. It also discusses important concepts such as blocks, blockchain structure, proof of work, and the Bitcoin mining process.
Subrat Kumar Jena, Gayatri Palai, Asst. Prof. Rumana Hasinullah Shaikh
Abstract-The rapid expansion of the global gig economy has fundamentally changed the structure of personal finance management. Unlike salaried professionals who operate within predictable monthly income cycles, freelancers and independent contractors face highly volatile cashflow patterns characterized by delayed client payments, irregular project pipelines, seasonal fluctuations, and unstable liquidity reserves. Traditional Personal Financial Management (PFM) systems primarily focus on historical transaction tracking and static budgeting, making them ineffective for proactive financial survival planning in modern freelance ecosystems. This project introduces Prophet AI v1.1, an AI-driven financial intelligence platform engineered specifically to simulate, forecast, and analyze unstable freelance cashflow environments using distributed cloud infrastructure, cryptographic verification, and real-time neural intelligence. The proposed system functions as a Financial Flight Simulator that allows freelancers to model financial risk before it becomes catastrophic in real life. The platform combines machine learning-based forecasting, stochastic risk simulation, cryptographic integrity validation, asynchronous AI orchestration, and multilingual neural voice synthesis within a single integrated ecosystem. The system architecture follows a distributed deployment model consisting of a Next.js 14 frontend hosted on Vercel, a FastAPI Intelligence Gateway hosted on Render, and a Supabase PostgreSQL secure transaction vault. This decoupled architecture ensures scalability, modularity, low frontend latency, and reliable handling of long-running AI inference tasks. The financial forecasting engine utilizes a hybrid intelligence pipeline combining statistical forecasting principles and ensemble-based analytical logic. The platform generates 30-day rolling liquidity forecasts, safe spending corridors, and stress-based runway simulations that help users evaluate financial survival scenarios under varying burn conditions. Unlike conventional financial dashboards, Prophet AI introduces dynamic What-If simulation controls, allowing users to manipulate variables such as liquidity lag, expense escalation, and delayed client payments in real time. To establish institutional-grade trust and forensic-grade auditability, the system implements an Integrity Shield powered by the SHA-256 cryptographic hashing algorithm. Every transaction entered into the system generates a unique digital fingerprint using transaction attributes including amount, date, category, and user identification. This verification mechanism ensures that tampered or manipulated financial records cannot enter the intelligence pipeline, thereby maintaining a Verified Ledger architecture. The project additionally documents real-world deployment challenges involving decimal precision mismatches between JavaScript and Python environments and explains the implementation of strategic normalization bypass mechanisms for stable production deployment. The intelligence layer of Prophet AI is powered using Llama 3.3-70B via Groq infrastructure, enabling high-speed financial reasoning and structured JSON-based strategy generation. The platform utilizes a carefully engineered Ruthless Financial Strategist system prompt designed to deliver direct, survival-oriented financial recommendations rather than emotionally comforting advice. This design philosophy reflects the real-world operational needs of freelancers who require accurate liquidity warnings and actionable strategic insights during financial instability. The generated intelligence is converted into multilingual audio briefings using the edge-tts neural voice synthesis engine, supporting both English and Hindi voice outputs. To avoid cloud timeout failures and synchronous processing bottlenecks, the platform implements an asynchronous polling architecture using UUID-based job orchestration. The frontend submits a /briefing request and continuously polls a /briefing-status/{job_id} endpoint until the AI-generated strategy and MP3 briefing become available. This architecture enables the system to safely execute computationally expensive large language model inference and neural voice generation workflows even on limited-resource cloud infrastructure. The completed system demonstrates the practical integration of distributed AI infrastructure, cryptographic verification, asynchronous backend engineering, financial forecasting, and multimodal intelligence synthesis within a real-world production environment. Prophet AI v1.1 represents a transition from passive financial recordkeeping to proactive survival-oriented financial intelligence. The project establishes a scalable blueprint for next-generation AI-powered fintech systems capable of delivering real-time strategic decision support for the rapidly growing global freelance economy.Keywords-Freelance finance; cashflow forecasting; stochastic simulation
Abstract : This article investigates the efficacy of implementing an AI-powered automated trading system on the blockchain using advanced machine learning algorithms and smart contract technology. The work addresses the challenges of cryptocurrency market volatility, the need for real-time decision making and the limitations of traditional trading approaches that often result in suboptimal returns and exposure to increased risk. This work develops a comprehensive trading platform that combines Long Short-Term Memory (LSTM) neural networks, Q-Learning reinforcement learning algorithms and blockchain-based smart contracts to create an autonomous, intelligent trading system. The methodology follows a multi-layered approach that integrates real-time market data collection from CoinGecko and Snowtrace APIs, advanced AI model training using TensorFlow.js, and smart contract deployment on the Avalanche C-Chain using Hardhat and OpenZeppelin libraries. LSTM model is used for price prediction and Q-Learning agent is used for trading strategy optimization, while comprehensive risk management is implemented using Value at Risk (VaR) calculations, portfolio rebalancing algorithms and automated stop-loss mechanisms. The trading execution is facilitated through direct integration with Pangolin DEX smart contracts to ensure decentralized and trustless trade execution. The performance of the system is evaluated using a sophisticated backtesting engine with Monte Carlo simulations, comparing the AI-driven strategy against traditional buy-and-hold approaches. The performance metrics used were Sharpe ratio, maximum drawdown, win rate, and total return. The AI-powered token prediction system demonstrates a superior performance due to its ability to process complex, non-linear market patterns and adapt to changing market conditions through reinforcement learning, and execute trades with minimal latency through blockchain integration. The findings are expected to provide cryptocurrency traders and institutional investors with a robust and automated trading solution that leverages the benefits of both artificial intelligence and blockchain technology for improved investment outcomes and risk management.
Maximal Extractable Value (MEV) in decentralized finance (DeFi) enables searchers to profit from transaction ordering and arbitrage opportunities across Automated Market Makers (AMMs). Among MEV strategies, atomic triangular arbitrage is widely deployed due to its deterministic execution within a single transaction. However, executing profitable arbitrage under realistic constraints, such as limited wallet balance, pool liquidity, gas costs, and blockchain latency, remains a challenging optimization problem. In this work, we formulate atomic triangular arbitrage as a constrained optimization problem that jointly selects an ordered three-pool path and trade amount to maximize net profit. To solve this non-convex problem, we propose a Deep Reinforcement Learning approach based on Proximal Policy Optimization (PPO). Experimental results show that while exhaustive grid search attains the highest returns, it requires a significantly high amount of inference time, making it infeasible for on-chain execution. In contrast, the proposed PPO agent achieves millisecond-level inference latency while generating consistent positive profit. These findings highlight a fundamental speed–profit trade-off in MEV extraction and demonstrate that PPO provides an effective and practical solution for atomic triangular arbitrage in DeFi.
This study investigates the predictive performance of decomposition-based deep learning models through a focused case study on Ethereum price forecasting. Using hourly Ethereum price data from 5 September 2020 to 13 July 2025, we develop hybrid forecasting frameworks that integrate three signal decomposition techniques—Wavelet Decomposition (WD), Variational Mode Decomposition (VMD), and Empirical Mode Decomposition (EMD)—with a Long Short-Term Memory network enhanced by an attention mechanism (LSTM–Attention). The decomposition methods are first applied to extract multiple frequency components from the original time series, allowing the forecasting model to capture both short-term fluctuations and long-term dynamics inherent in this specific digital asset. Each decomposed component is then modeled using the LSTM–Attention architecture, and the forecasts are aggregated to produce the final prediction. The predictive performance of the proposed models is evaluated using MAE, MSE, RMSE, and MAPE, and the results are compared with benchmark models including ARIMA-GARCH and standard LSTM–Attention. Forecast accuracy is assessed through out-of-sample one-step-ahead predictions, and robustness is ensured by averaging results across 10 independent runs. The empirical results demonstrate that incorporating decomposition techniques substantially improves forecasting accuracy. Among the tested models, the EMD–LSTM–Attention framework achieves the best performance, producing the lowest forecasting errors. While focused on the Ethereum market, these findings highlight the effectiveness of combining signal decomposition and attention-based deep learning architectures to enhance predictive performance in high-volatility cryptocurrency environments.
This Present Study Topic is ‘The Influence of Data Structures on Optimal Algorithm Design and Performance in Fintech’ The efficient data structures play a critical role in improving algorithm design, computational speed, scalability, and memory optimisation within fintech systems. Recent fintech studies emphasise that modern financial platforms process massive real-time transactional data, requiring optimised algorithms supported by advanced data structures such as trees, graphs, hash tables, heaps, and distributed ledgers. Financial Technology applications, including digital banking, fraud detection, blockchain, algorithmic trading, and risk management, rely heavily on these computational techniques to maintain performance and security. Artificial Intelligence and reinforcement learning demonstrated that optimal algorithm design supports decision-making, portfolio optimisation, fraud detection, and automated trading systems. Researchers concluded that the integration of suitable data structures with intelligent algorithms improves prediction accuracy, computational efficiency, and operational scalability in fintech applications. These technologies are becoming increasingly important in modern digital financial ecosystems driven by big data and real-time analytics.
Drissia Ennagoura, Kamal El Kehal, Safae Merzouk, BERDAI ABDELHAMID · 8 authors
Prices of cryptocurrencies are tough to forecast due to their high volatility and susceptibility to abrupt market changes. This paper compares four models—ARIMA, Prophet, LSTM, and XGBoost—to predict Ethereum (ETH) prices on three horizons: 15 minutes, 1 hour, and 1 day. We compared all four models concerning Root Mean Squared Error (RMSE) from the historical ETH data. The outcome shows XGBoost performs best on short-term forecasting with an RMSE of 352 in 15-minute and 357 in 1-hour data, surpassing LSTM and ARIMA. For the daily prediction, Prophet shows competitive performance with an RMSE of 941, whereas ARIMA is generally stable. The findings conclude that the ideal model depends on the forecasting horizon, and for short-term trading, using XGBoost is advisable, while Prophet is advisable for longterm forecasting. The study provides valuable recommendations to investors and researchers seeking effective cryptocurrency prediction software.
Abstract This research analyzes the performance and interconnectedness of major global stock market indices and decentralized finance assets, specifically cryptocurrencies, over the period from 2015 to 2025. The study includes indices such as the S&P 500 and Nasdaq Composite from the United States, the FTSE 100, DAX, and CAC 40 from Europe, and the Nikkei 225 from Japan, and two more indices from China and India representing different economic regions. Additionally, Bitcoin and Ethereum are included to assess the impact of decentralized finance on traditional financial indices and asset allocation strategies. By employing Artificial Intelligence algorithms like ConvLSTM, the research measures the dynamic asset allocation and volatility management through an interconnected spillover matrix. The findings reveal that integrating ConvLSTM enhances the understanding of the interconnectedness between cryptocurrencies and traditional assets, offering improved diversification opportunities due to their low correlation, decentralization, and inflation-hedge characteristics. The study’s results suggest that investors can make more informed decisions regarding dynamic asset allocation in high-volatility portfolios, providing indicators of rising systemic risk and market stress.
Sovereign AI-Native Multi-Asset Trading, Execution, and Financial nfrastructure Charter Sovereign AI‑Native Financial Execution Infrastructure PARRALAX‑AIHFTFUND is a multi‑asset, AI‑native financial organism engineered to operate across traditional and blockchain‑based markets. It provides a unified execution layer where autonomous agents can observe markets, interpret structure, execute trades, manage risk, govern portfolios, issue digital assets, coordinate token economies, and maintain verifiable proof‑of‑computation. This repository contains the core infrastructure, protocol stack, and governance architecture for building sovereign, agent‑driven financial systems. Mission To build a sovereign AI‑native financial infrastructure capable of coordinating autonomous trading agents, multi‑asset execution, fund governance, risk control, digital‑asset creation, and market intelligence across both traditional and blockchain‑native markets. The system exists to move beyond bots, dashboards, and scripts. Its purpose is to become a real execution organism for financial markets. Vision PARRALAX‑AIHFTFUND aims to create a long‑horizon financial intelligence layer where AI agents can: Observe and interpret global market structure Execute trades across heterogeneous venues Manage risk and exposure Govern portfolios and internal policy Issue and manage digital assets Coordinate internal token economies Maintain proof‑of‑computation and decision lineage Operate across crypto, fiat, equities, FX, derivatives, AI tokens, NFTs, and future asset classes Build market memory over time The system is designed to evolve as markets evolve. Foundational Premise Modern markets are: Machine‑driven Fragmented Multi‑asset Tokenized Agent‑mediated A serious financial infrastructure must therefore operate across: Traditional finance (equities, FX, derivatives, funds) Decentralized finance (DEXs, AMMs, on‑chain liquidity) Tokenized and synthetic assets AI‑native markets Autonomous agent economies High‑speed execution environments Governance‑controlled fund structures Programmable financial instruments PARRALAX‑AIHFTFUND is built to bridge old‑world and new‑world markets. What PARRALAX‑AIHFTFUND Is A sovereign trading infrastructure framework An AI‑native market execution system A multi‑asset financial operating layer A protocol stack for autonomous trading agents A fund governance and charter framework A digital‑asset issuance and management environment A blockchain‑compatible coordination layer A risk‑aware execution engine A compute‑receipt and proof‑trace system A foundation for future AI‑managed financial organisms It is built for real execution, not passive analysis. What PARRALAX‑AIHFTFUND Is Not Not a research repo Not a toy trading bot Not a simulation Not a dashboard Not a signal script collection Not a crypto hype project Not a single‑asset system Not a prediction‑only model Research supports the system. Research does not define the system. Status Active development. Core modules stabilizing. Execution layer expanding. Governance and digital‑asset subsystems in progress. PARRALAX‑AIHFTFUND is an AI‑native financial execution framework designed to coordinate autonomous agents across traditional and blockchain‑based markets. The system provides a unified operating layer for multi‑asset execution, risk management, fund governance, digital‑asset issuance, and verifiable compute‑traceability. System Mission To construct a sovereign financial intelligence layer capable of continuous operation across heterogeneous markets, enabling agents to observe market conditions, interpret structure, execute trades, manage exposure, and maintain internal governance. Operational Scope The system is engineered to function across: Traditional finance (equities, FX, derivatives, funds) Decentralized finance (DEXs, AMMs, on‑chain liquidity) Tokenized and synthetic assets AI‑native markets and agent economies Governance‑controlled fund structures High‑speed execution environments Programmable financial instruments System Definition PARRALAX‑AIHFTFUND comprises: A sovereign trading and execution infrastructure A multi‑asset financial operating layer A protocol stack for autonomous trading agents A governance and charter framework A digital‑asset issuance and management environment A blockchain‑compatible coordination layer A risk‑aware execution engine with compute receipts Non‑Scope The system is not a research‑only repository, simulation toy, dashboard, signal script collection, or prediction‑only model. It is infrastructure‑first and execution‑oriented.
Central bank digital currencies (CBDCs) integrated with decentralized finance (DeFi) represent a transformative development in digital financial systems. However, there is a lack of systematic frameworks for prioritizing the determinants of effectiveness and sustainability in DeFi-integrated CBDC platform investments. This study develops an integrated multicriteria decision-making framework to identify critical evaluation criteria and rank alternative platform architectures under uncertainty. The proposed model combines objective expert weighting, interaction-sensitive criteria evaluation, and fuzzy-based alternative ranking within a unified analytical structure. The results indicate that technological infrastructure (0.168) and liquidity (0.167) are the most influential criteria, while hybrid and privacy-focused platforms emerge as the most suitable investment alternatives. These findings highlight the importance of balancing technological robustness, liquidity depth, and privacy considerations in CBDC design. The study contributes by offering a structured and uncertainty-sensitive decision framework to support strategic platform selection and policy formulation in evolving digital currency ecosystems.
Modern company activities depend greatly on inventory management, which covers demand forecasting and inventory optimization to guarantee operational effectiveness and customer happiness. This paper presents a new method fusing blockchain technology with cutting-edge deep learning to overcome these restrictions for better inventory management. Initially, the data are preprocessed using Zmin–max normalization (ZMM), and then feature extraction follows. To extract the spatiotemporal features and capture long-term temporal dependencies in demand data, a hybrid deep learning architecture is presented, built on a Deep Convolutional Koopman Network (CKN) integrated with a Coordinate Attention-Based Gated Recurrent Unit (CKN-CGRU).Genetic Secretary Bird Optimization (GSBO) is used to further tune the model automatically. While the CKN captures complex spatial temporal correlations, the GRU effectively models sequential dependencies. Blockchain architecture with smart contracts and improved Proof-of-Stake consensus is integrated to guarantee data integrity and transparency in stock transactions. This makes it possible to securely, automatically, and in a tamper-proof way record inventory projections, orders, and stock updates. The suggested system improves the stakeholder trust in decentralized inventory management by ensuring complete traceability and real-time auditability throughout the process. Experimental outcomes show the efficiency of the proposed model strategy, with an accuracy of 99.94% and precision of 99.93%.
The intricate and unpredictable nature of cryptocurrency markets has brought Bitcoin price prediction into the spotlight because of the relatively high volatility levels of cryptocurrencies. Conventional centralized machine learning solutions pose challenges on the issue of data privacy, security, and scalability, especially with financial applications. To respond to these issues, this paper outlines an overall execution of a federated learning system to predict Bitcoin prices. K-Nearest Neighbours, Decision Tree, Linear Regression, and Federated Long Short-Term Memory (FL-LSTM) model are federated, trained, and tested on past Bitcoin market data. Within the proposed framework, the process of model training is executed at each of several clients locally, and only model parameters or predictions are transmitted without data privacy. Experimental data reveal that classical federated machine learning models have poor performance in modelling complex price dynamics. Although Federated Linear Regression reflects similar goodness of-fit, the FL-LSTM proposed model is always associated with the lower prediction error and is highly close to the real price movements. Also, the FL-LSTM model is used to predict the short-term future, which proves that this model can be useful to anticipate future fluctuations in Bitcoin prices. The results affirm that federated learning, which has been combined with deep learning models, is a viable and privacypreserving solution in cryptocurrency prediction in a decentralized setting. Root Mean Square Error (RMSE) and$\mathbf{R}^{\mathbf{2}}$were used to measure the proposed models. The results of the experiment show that the FL-LSTM model possesses the lowest prediction error, and it is much closer to real Bitcoin price trends than the other federated models.
This paper develops a deep reinforcement learning framework for cryptocurrency portfolio management in which transaction costs are derived from the Riemannian geometry of the underlying volatility model rather than assumed constant. A Proximal Policy Optimisation agent is trained on a reward function grounded in non-equilibrium thermodynamics: we use the free-energy Bellman equation, in which transaction costs are the geodesic slippage on the Fisher information manifold of a maximum-entropy Markov-switching GARCH model, and regime-transition costs are the Wasserstein-2 distance between the calm and turbulent return distributions. A thermodynamic Carnot bound on portfolio efficiency is established and empirically validated. Five hypotheses are tested across Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash over January 2017 to March 2026. The geometric-cost agent achieves statistically superior Sharpe ratios relative to flat-fee baselines on four of five assets; portfolio turnover is reduced by 56 to 83 percent relative to signal-following; the thermodynamic friction point at which the agent prefers no-trade is asset-specific and ordered by turbulent half-life; a joint topological and geometric circuit breaker reduces Maximum Drawdown by 28 to 38 percent; and ablation confirms that every component of the observation vector contributes a statistically significant performance gain. The framework requires liquid cryptocurrency markets with validated parametric volatility models; transferability to other asset classes requires upstream recalibration.
Cryptocurrencies and Decentralized Finance (DeFi) currently represent a fast growing trend in finance, which enables financial services on public blockchains. In contrast to traditional financial markets, ruled by well established corporations, DeFi is completely transparent, as it keeps publicly available records of all transactions that occur in the network. This availability of the data represents an opportunity to analyze and understand the market from the point of view of the complexity that emerges from the interactions among actors (users, bots, and companies) operating in the embedded market. In this paper, we focus on Ethereum to show that the underlying transaction network bears further and useful information to forecast the evolution of the market. We aim to separate the non-redundant effects of the blockchain transaction network from technical analysis and social media trends in the future price of the Ethereum native cryptocurrency. To this end, we build two machine learning models to predict the future trend of the price time series. The first model, serving as a base, considers the set of most relevant features according to the current scientific literature-including technical analysis and social media trends. The second model considers the features of the base model, incorporating the network properties computed from the transaction network. We find that the second model outperforms the base model and can anticipate 46% more rises in the price than the base model and 19% more falls. Thus, we conclude that new indicators based on network properties provide valuable information to forecast the future direction of the market that cannot be explained neither by technical analysis nor by social media trends alone. Hence, our results represent an important first step toward the definition of a new family of DeFi market indicators based on the complexity of the underlying transaction network.
Piotr Fiszeder, Witold Orzeszko, Radosław Pietrzyk
Abstract This study investigates the use of ChatGPT as an automated tool for extracting and labeling Bitcoin-related news sentiment and examines how the resulting sentiment indicators affect Bitcoin returns and volatility. A large dataset of news headlines is processed via an API-based workflow, and the ChatGPT-derived sentiment indicators are subsequently incorporated as explanatory variables into selected statistical and machine learning models, including autoregressive (AR), heterogeneous autoregressive (HAR), Bayesian model averaging (BMA), least absolute shrinkage and selection operator (LASSO), and support vector regression (SVR). We find that while the sentiment indicators significantly improve in-sample estimation accuracy for returns and volatility, they do not lead to statistically significant gains in out-of-sample forecasting performance. This result suggests that ChatGPT-based sentiment measures primarily capture contemporaneous market-relevant information rather than persistent predictive signals, consistent with semi-strong market efficiency.
Predictive analysis has become an essential component in modern financial research and practice, driven by the rapid advancement of data analytics, machine learning, and artificial intelligence. This study aims to systematically map the intellectual structure, research trends, and key contributions in the field of predictive analysis in finance through a bibliometric approach. Data were collected from the Scopus database covering publications from 2000 to 2026 and analyzed using VOSviewer to examine co-authorship networks, citation patterns, and keyword co-occurrence. The results reveal a significant growth in research output, particularly in recent years, reflecting the increasing importance of data-driven decision-making in finance. Co-authorship analysis indicates the presence of collaborative research clusters, although the field remains partially fragmented. Citation analysis highlights that the most influential studies are those integrating advanced computational methods with practical financial applications, such as credit scoring, bankruptcy prediction, and stock market forecasting. Furthermore, keyword analysis demonstrates a clear shift from traditional statistical techniques toward machine learning, artificial intelligence, and emerging technologies such as blockchain and decentralized finance. This study contributes by providing a comprehensive overview of the evolution and current state of predictive analysis in finance, identifying key research themes and gaps. The findings suggest that future research should focus on enhancing model interpretability, integrating sustainability considerations, and expanding applications in real-time financial decision-making. Overall, this study serves as a valuable reference for researchers and practitioners seeking to understand the trajectory and future direction of predictive analytics in the financial domain.
The distributional specification in Markov-switching GARCH models has historically been driven by empirical convention rather than statistical theory. This paper derives the two-regime MS-GARCH specification from the Maximum Entropy Principle, providing an information-theoretic motivation for Student-t regime-conditional innovations in cryptocurrency volatility modelling. The framework is applied to five major cryptocurrencies, Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash, over the period January 2017 to March 2026, comprising 15,834 daily observations spanning six complete market cycles. Three principal findings emerge. First, a Calm-Phase Fragility pattern is identified: four of five assets exhibit calm-regime half-lives below one trading day (0.48 to 1.16 days), with turbulence the dominant long-run state (stationary turbulent probability in [0.451, 0.771] across all assets), establishing turbulence rather than calm as the structural baseline of the cryptocurrency ecosystem. Second, the Maximum Entropy derivation yields endogenous Student-t degrees of freedom, with heavy-tailed turbulent innovations (degrees of freedom approximately 4.5) confirmed across all assets, validating the MaxEnt constraint framework empirically. Third, near-unity turbulent GARCH persistence drives MS-GARCH point forecasts toward the persistence ceiling, consistent with an information-theoretic bound on predictability when the calm half-life collapses below one trading day; HAR-RV achieves the lowest QLIKE loss for three of five assets under these near-critical conditions. Cross-asset consistency is confirmed across seven statistical indicators including Hill tail exponents in [2.31, 3.26], Hurst exponents in [0.543, 0.577], and Wald tests rejecting parameter homogeneity at p < 0.001 for all assets. The framework is formalised as a deployable expert system for real-time regime monitoring and risk management.
This study rigorously formulates the complex fund-scheduling problem as a Markov decision process (MDP). It constructs a state space that integrates real-time and forecast information, an atomic action space that conforms to business logic, and a reward function that balances long-term returns against immediate risk. To address the curse of dimensionality and the credit-assignment problem in coordinated scheduling among multiple fund units, a multi-agent deep deterministic policy gradient (MADDPG) algorithm is adopted. Under a centralized-training and decentralized-execution framework, the algorithm reconciles global optimization with decentralized decision-making. In addition, a difference-reward mechanism and Kalman filtering are used to accurately measure each agent’s individual contribution and reduce the impact of environmental noise on reward signals. The results show that, compared with a static rule engine and a conventional linear programming method, the proposed deep reinforcement learning strategy reduces average daily funding costs by 50.4%, lowers the payment failure rate to 0.002%, and maintains a high liquidity buffer adequacy ratio. The strategy also demonstrates clear advantages in decision timeliness, collaborative handling of complex instructions, and self-adaptation potential, thereby providing an innovative pathway for finance-company fund scheduling to progress from intelligentization to automation.