Muhammad Diaz Syahmi Oktavian, Rizky Parlika, Firza Prima Aditiawan
The extreme price volatility of Bitcoin frequently prevents its widespread adoption. The persistent "Digital Gold" narrative often dominates its price analysis, largely ignoring the predictive value of strategic industrial commodities like Platinum Group Metals. This study aims to investigate whether integrating industrial metals specifically platinum and rhodium enhances the short-term forecasting accuracy of Bitcoin prices. Utilizing high-frequency 5-minute interval data over 729 days, this research applies a comparative quantitative approach using univariate and multivariate Long Short-Term Memory (LSTM) deep learning architectures. Results demonstrate the multivariate LSTM model achieves highly accurate forecasting, recording a Mean Absolute Percentage Error (MAPE) of 3.95% and a Root Mean Squared Error (RMSE) of 0.0598. Compared to the univariate baseline model (MAPE of 5.14%, RMSE of 0.0725), the multivariate approach demonstrates a notable decrease in error rates. This improvement suggests platinum and rhodium price movements contain useful informational value for Bitcoin forecasting, rather than mere random noise. Specifically, rhodium demonstrates strong predictive relevance for Bitcoin market movements. In conclusion, while not strictly proving causal structural integration, these findings highlight Bitcoin's sensitivity to the global real-sector economic cycle. Practically, these findings suggest investors can refine short-horizon forecasting and mitigate risk by monitoring industrial commodity prices. Given persistent nominal offset deviations, future research should prioritize explicit connectedness testing (e.g., lead-lag analysis) and develop a hybrid model incorporating Natural Language Processing (NLP) for news sentiment analysis.
Abstract Financial time-series forecasting lies between AI and market microstructure, but most studies optimise generic error metrics instead of risk-adjusted economic value under realistic frictions. Unlike NLP and vision, the field lacks a shared, reviewer-enforced standard for data handling and evaluation, leading to persistent problems such as data leakage, backtest overfitting and metric-chasing on RMSE/MAE. This paper introduces QFRS a novel, enforceable by reviewers and editors, seven-standard framework and checklist for evaluating and reporting financial asset forecasting and trading claims. QFRS covers quantitative studies on equities (stocks), forex, cryptocurrencies, rates, derivatives (futures, forwards, options, swaps), energy prices, and commodities (gold, oil and silver) and other asset classes. The seven standards specify an end-to-end experimental pipeline, covering (i) dataset construction, (ii) labelling, (iii) point-in-time feature engineering, (iv) leakage-free scaling or normalisation, (v) time-respecting data splits, (vi) evaluation metrics and (vii) cost and slippage-aware backtesting with explicit execution assumptions and decision rules mapping predictions to positions. To validate the standardâs diagnostic value, a compliance audit of Scopus-indexed forex forecasting papers published in 2025 is presented. None of these papers achieved full compliance across all seven standards, with economic backtesting (12.2%) and causal scaling (31.7%) recorded the lowest pass rates. QFRS underpins a public state-of-the-art leaderboard, ensuring that only studies satisfying these standards are ranked, with the goal of shifting the literature from opaque, error-metric-driven results to transparent, economically meaningful and comparable benchmarks. The accompanying leaderboard is available and updated regularly at http://mkhushi.github.io .
This paper introduces the Dynamic External Memory LSTM (DEM-LSTM), a novel deep neural architecture designed to address the hidden state information bottleneck and temporal context decay inherent to standard LSTMs in financial time-series forecasting. By decoupling sequence processing from persistent state storage via an addressable external memory matrix ($M_t$), DEM-LSTM dynamically reads, erases, and updates market context across long sequences without corrupting internal hidden representations. Evaluated across four distinct asset classesâForeign Exchange (EUR/USD), Commodities (XAU/USD and USOIL), and Cryptocurrencies (BTC/USD)âDEM-LSTM consistently outperforms standard LSTM baselines across all metrics, achieving up to a 41.4% reduction in RMSE on Gold spot prices while maintaining superior stability across high-volatility market regimes.
As the blockchain and decentralized finance (DeFi) ecosystems continue to expand and mature, rug pull scams involving meme coins are occurring with increasing frequency, posing a threat to the security of investors' assets and the healthy development of the industry. Rug Pull scams are characterized by extremely low deployment costs, covert execution, rapid fund transfers, and high detection difficulty. Traditional manual reviews or fixed rules struggle to meet real-time early warning requirements, and existing detection methods generally suffer from issues such as a single feature dimension, inadequate handling of class imbalance, and weak model generalization and interpretability. To address these shortcomings, this paper focuses on the detection of Ethereum-based rug pull scams. First, we clarify their definitions, types, and harm mechanisms, and construct a multi-dimensional feature system based on dimensions such as malicious smart contract design, on-chain transaction anomalies, liquidity manipulation, and social media disclosures. Next, using the "Second Uncle Coin"(token symbol: BOBU) case as an example, we reconstruct the attack process and derive quantitative detection metrics. Subsequently, a risk detection model based on a Multi-Layer Perceptron (MLP) is designed. We employ a combined strategy of SMOTE oversampling and Focal Loss to address the issue of sample imbalance, dynamically search for optimal thresholds to balance precision and recall, and incorporate gradient pruning and early stopping to enhance training stability. Experiments show that the model achieves an accuracy of 0.927, an F1 score of 0.787, and an AUC-ROC of 0.952 on the test set, outperforming traditional methods. Finally, a visualizable web-based detection system is developed using the Flask framework, enabling batch risk assessment, high-risk ranking display, and result export functions.
This paper investigates whether prediction market settlements create incentives for temporary price pressure in Bitcoin spot markets. Using high-frequency data from February 2025 to January 2026 and actual contract-level data from Polymarket and Kalshi to identify economically relevant contract strikes, we document basis divergence between settlement oracle exchanges (Coinbase) and non-constituent exchanges (Binance) during expiry windows. Employing a difference-in-differences framework with month fixed effects, we find that a one standard deviation increase in strike proximity is associated with a 6.7 basis point constituent exchange price deviation during settlement windows. The estimate is precise under the baseline minute-level HAC specification, while exact paired-month permutation inference based on 12 settlement events yields p=0.0256; equal-weight event aggregation produces a larger negative estimate, indicating event heterogeneity. Monthly directional patterns are suggestive, though stricter event-level and above-versus-below-strike tests provide mixed evidence on directional asymmetry. Taken together, these findings provide reduced-form evidence consistent with settlement-related incentives and may raise broader settlement-design considerations for decentralized financial systems. However, the analysis does not directly observe trader intent or the underlying mechanism.
Rukhsar Zaka, Faiza Irfan, Sidra Rehman, Muhammad Ahsan Hayat
Cryptocurrency markets are highly volatile, nonlinear, and affected by several internal and external market factors, making price forecasting a challenging task. Accurate cryptocurrency price forecasting can support investors, traders, and financial analysts in making informed decisions. This research paper presents a comparative analysis of machine learning and deep learning models for cryptocurrency price forecasting using historical Aave (AAVE) cryptocurrency data. The dataset consists of 275 records and 10 features, including Date, High, Low, Open, Close, Volume, and Marketcap. The Close price is selected as the target variable, while High, Low, Open, Volume, and Marketcap are used as predictor variables. Five models are implemented and compared: Linear Regression, Support Vector Regression, Random Forest Regressor, XGBoost Regressor, and Long Short-Term Memory. The models are evaluated using Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, R-squared score, and directional accuracy. Experimental results show that the LSTM model achieved the best performance with the lowest RMSE of 2.74, MAE of 1.78, MAPE of 3.91%, and R-squared score of 0.965. The results indicate that deep learning models, especially LSTM, are more suitable for capturing temporal dependencies and nonlinear patterns in cryptocurrency price data.
This paper empirically compares four architectures for financial time-series forecasting: LSTM, CNN, the original Regularized Self Attention Regression (RSAR) model, and a multiobjective optimized RSAR variant, denoted MO-RSAR, obtained using the Non dominated Sorting Genetic Algorithm II (NSGA-II). The models are evaluated on six datasets covering Forex, equity index and cryptocurrency markets, for short and long horizons. All models share a common preprocessing pipeline and evaluation framework and are assessed using standard error metrics, with emphasis on Mean Absolute Percentage Error (MAPE). MORSAR yields the lowest average prediction error across all datasets and provides significant gains for longer, more volatile horizons, while simpler architectures remain competitive for short-term forecasts. The key methodological contribution is the first empirical integration of the RSAR architecture with NSGA-IIbased multi-objective hyperparameter optimization for financial time-series forecasting. The proposed framework treats RSAR configuration as a bi-objective search over accuracy and generalization (via the train-validation gap), and evaluates the resulting model under a unified protocol across heterogeneous markets and horizons.
This study proposes a hybrid forecasting framework that integrates sentiment analysis with deep learning to predict Bitcoinâs hourly and daily closing prices. Hourly BTC/USD market data spanning June 2021 to November 2025 were combined with approximately 326,000 Bitcoin-related news headlines published over the same period. Sentiment scores in the range of [-1, +1] were generated for each headline using FinBERT, a transformer-based language model trained on financial texts, and were subsequently integrated with technical indicators such as trading volume, MACD, and RSI. The resulting combined feature set was modeled using an LSTM network to capture temporal dependencies. Empirical results demonstrate that sentiment-enhanced hybrid models consistently outperform models based solely on technical indicators across RMSE, MAE, MAPE, and RÂē metrics. The hourly hybrid model achieved the best performance, with an RMSE of 1,009 USD and an RÂē of 99.23%. Furthermore, a 30-day out-of-sample real-time evaluation yielded an RMSE of 941 USD. The consistency between in-sample and out-of-sample results indicates that the proposed framework maintains stable predictive performance over time.
Akhter Javed, Huma Gul, Ali Husnain, Rahmat Said · 5 authors
Background: In this study, the increased complexity of today supply chains and explain why conventional forecasting and inventory management techniques are inadequate in today's dynamic and uncertain market conditions. As globalization and data increase, AI has become a gamechanger in delivering better demand forecasting and inventory management, in turn driving a better operation and cost savings. Objectives: This study seeks to assess the performance of AI-based demand forecasting models combined with inventory optimization methods on improving the overall performance of the supply chain. Methods: A quantitative, data-driven methodology was employed, and secondary data were used, including historical demand, inventory levels, and other external data that included seasonality and economic indicators. Demand forecasting models: Advanced machine learning and deep learning models such as Long Short-Term Memory (LSTM), Random Forest and Gradient Boosting were used for demand forecasting. The results of the forecasts were fed into an inventory optimization system using reinforcement learning for dynamic decision-making. Standard deviations like Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used to measure the model's performance along with cost-performance analysis. Results: The accuracy of the prediction is significantly higher in AI-based models, especially the LSTM model, than the traditional models, which decreases the errors of the prediction and enhances its responsiveness. AI-powered inventory optimization resulted in significant savings on inventory holding and shortage/cost of order, and improved service levels and inventory stockout rates. The use of external data had yet further improved predictive performance. 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Victor Michelle, Natalie Michelle, Emilie Michelle, Elias Michelle
This paper introduces Prediction Assets â a fundamentally new class of financial instruments where the underlying asset is market consensus on probability itself. Unlike traditional prediction markets, where binary event contracts terminate abruptly upon resolution, Prediction Assets are engineered as perpetual financial instruments that evolve rather than expire. Upon event occurrence, the asset does not liquidate to zero or a fixed payout; instead, it programmatically transforms into a new functional asset form (such as a currency, index, or memory asset) via smart-contract-enforced conversion ratios, establishing an infinite lifecycle and continuous capital efficiency. Key Structural & Mathematical Contributions: Core Asset Pricing Model: Establishes the foundational pricing equation \(P_{asset} = P(E) \times M\) driven entirely by open order-book decentralized exchange (DEX/AMM) spot liquidity without reliance on subjective analytical oracles. Systemic Market Efficiency: Implements an exact arbitrage condition boundary constraint (\(\sum P_{asset,i} = M\)) to incentivize algorithmic market-making and eradicate structural price variance. Programmatic Post-Event Evolution: Introduces the deterministic conversion coefficient \(C(t, state)\) locked at genesis to handle automated migration profiles (Currency, Index, Memory, and Derivative states) with zero administrative discretion. Decentralized Governance: Outlines a 4-channel Multi-Chain Consensus Verification Layer (CVL) requiring a strict 3-of-4 quorum across official APIs, open-source replicas, academic mirrors, and market sentiment vectors. Regulatory Engineering: Delivers a comprehensive compliance analysis under the U.S. Securities Framework (Howey Test and Reves Test boundaries) and CFTC Event Contract frameworks, positioning the topology as a non-security utility asset. Prospective Implementation:The paper presents AIVA (Artificial Intelligence Valuation Asset) as the world's first prospective implementation tracking the global macro-consensus probability of achieving Artificial General Intelligence (AGI), which programmatically transforms into an operational settlement currency for autonomous multi-agent economic environments upon verification. Keywords: Prediction Assets, Probability Markets, Financial Instruments, AGI, AI Agents, Decentralized Finance, Synthetic Assets, Valuation Markets. Citation Note: This specification expands upon the sovereign fintech frameworks established in IP Stock Exchange v3.3-Evolution (DOI: 10.5281/zenodo.20687136).
High-Frequency Foreign Exchange (FX) electronic execution networks process in excess of $7.5 trillion in daily spot volume across geographically distributed matching engines. Modern institutional trading infrastructure relies heavily on automated limit order book (LOB) forecasting and real-time natural language processing of macroeconomic news feeds. However, this convergence of deep learning and automated execution introduces systemic attack surfaces that traditional risk engines are unequipped to handle. In this paper, we present FOREX-SHIELD, an integrated, multi-modal cyber-defense pipeline engineered to mitigate spoofing, news injection, and regulatory privacy leaks in high-frequency FX settlement. First, we model high-frequency 40 x N LOB dynamics using a spatio-temporal DeepLOB framework combining 2D convolutional layers and recurrent units. We demonstrate that unhardened spatial price-volume representations are vulnerable to microsecond Targeted Projected Gradient Descent (PGD) perturbations, suffering an Attack Success Rate (ASR) up to 37.50% (and 15.62% under expanded 64-sample batch evaluations). To counter this, we implement dynamic on-the-fly adversarial retraining, which elevates model defense robustness up to 84.38%â100.00% (preventing 54 out of 64 prediction flips). Second, we fine-tune a domain-adapted financial Transformer (FinBERT) using class-weighted cross-entropy optimization to detect synthetic macro news attacks, achieving 85.71% accuracy, an F1-score of 85.71%, and 100.00% recall across adversarial probes. Third, we construct a Zero-Knowledge Proof (ZK-SNARK) settlement layer that deterministically validates Anti-Money Laundering (AML) risk limits and liquidity constraints (R <= 75) without leaking transaction balances or institutional counterparty metadata. End-to-end backtests show a full multi-stage evaluation latency of approximately 120 ms, proving operational viability for real-time institutional clearing.
Diky Paramitha, Etik Ipda Riyani, Nadhira Hardiana, Kan Wen Huey
Bitcoin has a tendency of price volatility that is much higher than other cryptocurrency assets, this makes a very significant difference from other financial assets that can go beyond conventional market logic thus creating a major obstacle in risk management. This study aims to dissect the extreme anomalies of bitcoin trading volume against the volatility of Bitcoin returns. Using a quantitative time series approach, the study analyzed monthly data on bitcoin price and trading volume using Bitcoin prices in the period February 2015 to December 2025. We assess volatility using the GARCH-X model to introduce trading volume as an exogenous variable. The basic GARCH shows significant volatility persistence, indicating a clustering of high volatility in Bitcoin's returns. This finding results that trading volume is not just a static transaction number but reflects a very crucial information proxy. Every movement of trading activity generates new signals in which aggressive price react. Trading volume is also highly correlated with the volatility of returns, although the volatility of the model indicates the need for careful interpretation. Bitcoin's volatility is not solely due to historical volatility dynamics, but also the impetus from trading activity, highlighting the need to consider accurate volatility modeling in the digital asset market. This research adds value by embedding trading volumes into the GARCH model to evaluate its contribution in explaining Bitcoin's volatility through empirical insights for investment decisions and risk management in the cryptocurrency market
This study investigates the growing role of stablecoins within the global financial system and examines their potential integration into traditional foreign exchange markets. Despite the rapid expansion of stablecoins, empirical evidence comparing their market dynamics with those of non-stable cryptocurrencies remains limited. To address this gap, the study adopts a descriptive case study design based on documentary analysis and secondary quantitative market data. The documentary review establishes the theoretical foundations of stablecoins and their relevance to foreign exchange markets, while the quantitative analysis relies on market data collected from CCData, DefiLlama, and Statista. Weekly market observations covering the period from April 2019 to May 2024 were analyzed using descriptive statistics, comparative analysis, volatility measures, Pearson correlation analysis, and one-way ANOVA. The findings reveal that stablecoins exhibit significantly lower price volatility than Bitcoin while maintaining high levels of market liquidity and trading activity. Among the analyzed assets, Tether (USDT) remains the dominant stablecoin, followed by USD Coin (USDC) and Binance USD (BUSD). The statistical analysis confirms significant differences between stablecoins and Bitcoin, highlighting the distinct market behavior of reserve-backed digital assets. These findings suggest that stablecoins have evolved beyond their traditional role as cryptocurrency trading instruments and are increasingly functioning as efficient mechanisms for cross-border payments, liquidity management, and decentralized finance applications. This study contributes to the literature by providing an integrated empirical comparison of stablecoins and non-stable cryptocurrencies while demonstrating how the stability, liquidity, and operational characteristics of reserve-backed digital assets may facilitate their future integration into traditional foreign exchange markets. The findings also provide practical implications for policymakers, financial institutions, and regulators seeking to develop secure and efficient digital payment infrastructures supported by appropriate regulatory frameworks.
Bitcoin research increasingly relies on on-chain indicators to study network activity, monetary issuance, transaction demand, miner incentives, coin-age behavior, and long-run monetary dynamics. However, many commonly used Bitcoin metrics are dispersed across commercial platforms, subject to heterogeneous definitions, or not fully reproducible from primary blockchain data. This manuscript introduces Open Bitcoin Metrics (OBM), a reproducible, full-node-derived dataset and reference guide for Bitcoin on-chain time series designed for economic and econometric research. The dataset provides documented daily series covering block production, block-space usage, transaction counts, supply, issuance, fees, miner revenue, mining difficulty, estimated hashrate, Bitcoin Days Destroyed, dormancy, liveliness, UTXO counts, spent output value, and related UTXO-age indicators. Metrics are reconstructed from a locally maintained Bitcoin Core full node, a persistent spent-output indexer, or deterministic transformations of previously generated OBM series. Each series is accompanied by open-source Python code, stable identifiers, explicit definitions, metadata, validation procedures, interpretive caveats, and comparisons with the closest publicly available metrics. The dataset is intended to support transparent empirical research, replication, teaching, and comparative analysis across monetary economics, financial economics, and blockchain studies.
Liu Hong Yuan Tom, Ruilin Wang, Hairui Wang, Ziqi Cao · 5 authors
This study examines the impact of social media sentiment on Bit-coin market volatility. While existing literature often relies on single-source data or isolated factors, this research introduces a novel three-source pricing framework that integrates Twitter-derived social media sentiment, investor leverage ratios, and historical market data. Using a Weighted Least Squares (WLS) regression model to address heteroscedasticity in financial time series, we analyze daily Bitcoin returns from 2021 to the first half of 2022. Our results indicate that both social media sentiment has a statistically significant positive effect on Bitcoin returns. The model successfully identified high-risk market conditions, as validated by the May-June 2021 crash. These findings demonstrate that social media sentiment has a huge impact on cryptocurrency markets.
This study compares the forecasting performance of four deep learning architecturesâGRU, LSTM, RNN, and CNNâfor one-step-ahead Bitcoin price prediction. A grid search determined the optimal configuration, which was applied uniformly across models to ensure fair evaluation. Using daily BTC closing prices from January 2018 to July 2025, it is found that the GRU model achieved the lowest forecasting errors (MSE, RMSE, MAE, MAPE) and the highest RÂē, with LSTM performing closely behind. Visual analyses confirmed that GRU and LSTM maintained stronger alignment with actual prices during volatile periods. To assess economic value, model forecasts were integrated into a rule-based trading strategy under realistic market frictions, including a 0.10% transaction cost and a 0.10% trading threshold, with both short-selling-enabled and long-only variants tested. The GRU strategy with short-selling generated the highest terminal wealth (approximately 24% higher than the Buy-and-Hold benchmark) and superior risk-adjusted returns, measured by CAGR, Maximum Drawdown, and Sharpe Ratio. The findings demonstrate that careful hyperparameter optimization, coupled with an architecture capable of capturing complex temporal dependencies, can significantly improve both predictive accuracy and trading profitability in cryptocurrency markets. These results provide practical implications for designing AI-driven trading systems.
Antonio Pellicani, Gianvito Pio, SaÅĄo DÅūeroski, Michelangelo Ceci
Abstract The cryptocurrency market represents a significant innovation in the financial ecosystem, built upon cryptographic principles to ensure secure and transparent transactions. Cryptocurrencies experienced a global adoption, driven by their decentralized nature that enables borderless transactions without third-party intermediaries. The price of cryptocurrencies is characterized by a significant volatility, that introduces both opportunities and challenges. In this context, the development of accurate methods for the forecasting of price variation, able to work in real-time on data streams, has become vital for various stakeholders. In this paper, we propose a novel approach, called LEMON, for the online prediction of the price variation of cryptocurrencies, that leverages possible temporal correlations among them. Our approach stems from the empirical evidence that cryptocurrencies tend to form groups characterized by similar trends, a behavior often attributed to shared market dynamics and common external factors. Through the analysis of temporal correlations, LEMON dynamically identifies these groups, that are then exploited to learn multiple multi-target tree-based models, specifically designed for processing continuous data streams. LEMON also introduces a novel adaptive non-parametric weighting scheme, that automatically adjusts the importance of each instance based on the observed data distribution in real-time, improving the forecasting of the price variation. Our experiments, performed on 16 datasets related to 16 cryptocurrencies, demonstrate that LEMON outperforms state-of-the-art approaches in two distinct prediction tasks: forecasting the closing price variation (regression) and predicting the market trend direction (classification), making it an effective tool to support stakeholders requiring accurate real-time predictions.
Deniz Erer, Tuna Can GÞleç, Ãzge Korkmaz, Elif Erer
Rapid developments in blockchain, decentralized finance, and tokenization have raised the question of whether Sukuk can complement technology-based financial assets. This study compares the time-varying efficiency and multifractal dynamics of Sukuk indices, DeFi tokens, lending and borrowing tokens, and a FinTech index from May 25, 2020, to November 29, 2023. Using TGARCH, nonlinearity and long-memory tests, MF-DFA, and MF-DCCA, the study examines shock persistence, asymmetric volatility, market efficiency, and cross-market dependence. The findings show that negative shocks increase volatility more strongly than positive shocks and that all markets display nonlinear and multifractal behavior. Sukuk indices, particularly RMENA and RDJSUKUK, show lower market deficiency values than most technology-based assets. However, persistent cross-correlations indicate that Sukuk is not a direct substitute for these assets. Rather, Sukuk may serve as a relatively stable and efficient complementary asset in technology-exposed portfolios.Key Words: Sukuk, DeFi assets, Tokenization, Financial Economics, MF-DFA, MF-DCCAJEL Classification: F65, E44, G15, C58
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 2015â2023. Employing FinBERT for sentiment classification, we develop novel compound sentiment scores that integrate text-based sentiment with Facebookâs 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 Facebookâs 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. Keywordsâ 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 KwiatkowskiâPhillipsâSchmidtâShin (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 Spearmanâs 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.