The AI-Powered Financial Insights Platform is designed to address the increasing complexity of decentralized applications and digital asset management systems. As blockchain ecosystems expand, users often struggle to interpret detailed transaction data, understand staking mechanisms, or navigate complex on-chain information. This platform leverages advancements in Artificial Intelligence, real-time blockchain indexing, and decentralized protocols to convert unintuitive data into easily interpretable financial insights while maintaining security and trust. By utilizing the Cardano network as its foundation, the platform provides a research-driven, layered architecture that ensures scalability and sustainability as separate principles. This platform represents a paradigm shift in wealth management and fiscal oversight by transitioning from reactive reporting to predictive intelligence. At its core, the system utilizes a sophisticated multi-agent AI architecture designed to ingest, normalize, and analyze massive volumes of heterogeneous financial data. By synthesizing information from global market indices, real-time news sentiment, and individual spending patterns, the platform constructs a 360-degree financial profile. It employs advanced Long Short-Term Memory (LSTM) networks and Transformer-based models to forecast cash flow trajectories and identify potential liquidity risks before they manifest. This proactive approach allows users-whether institutional investors or private individuals-to navigate volatile markets with a data-backed roadmap rather than relying on lagging indicators. Beyond mere data aggregation, the platform emphasizes contextual relevance. The "Insight Engine" utilizes Natural Language Generation (NLG) to translate complex algorithmic outputs into high-level executive summaries, effectively democratizing access to professional-grade financial analysis. Security is woven into the fabric of the application through a hybrid backend-combining the raw computational speed of C++ for high-frequency data processing with the flexibility of Python for AI model deployment. This ensures that the system remains scalable and responsive under heavy loads.
Generative and agentic artificial intelligence is entering financial markets faster than existing governance can adapt. Current modelrisk frameworks assume static, well-specified algorithms and onetime validations; large language models and multi-agent trading systems violate those assumptions by learning continuously, exchanging latent signals, and exhibiting emergent behavior. Drawing on complex adaptive systems theory, we model these technologies as decentralized ensembles whose risks propagate along multiple timescales. We then propose a modular governance architecture. The framework decomposes oversight into four layers of "regulatory blocks": (i) self-regulation modules embedded beside each model, (ii) firm-level governance blocks that aggregate local telemetry and enforce policy, (iii) regulator-hosted agents that monitor sector-wide indicators for collusive or destabilizing patterns, and (iv) independent audit blocks that supply third-party assurance. Eight design strategies enable the blocks to evolve as fast as the models they police. A case study on emergent spoofing in multiagent trading shows how the layered controls quarantine harmful behavior in real time while preserving innovation. The architecture remains compatible with today's model-risk rules yet closes critical observability and control gaps, providing a practical path toward resilient, adaptive AI governance in financial systems.
Spot Bitcoin ETFs, approved in January 2024, trade only during NYSE hours but track an asset that trades around the clock. We study whether this mismatch affects Bitcoin's intraday risk profile using a symmetric one-year difference-in-differences design on hourly Coinbase data. The aggregate US-hour effect is null, but hour-specific and sub-hourly decomposition reveals a volatility spike concentrated in the first 30 minutes of ETF trading (9:30-10:00 ET), the only window surviving multiple testing correction. The pre-open half-hour (9:00-9:30) is insignificant, a pattern more consistent with order flow at the open than with anticipatory positioning. Quantile analysis shows left-tail deepening at the 5th and 10th percentiles of US-hour returns while the median is unaffected, and both tails widen at the opening window. Trading volume surges at both NYSE open and close, but only the open generates a volatility spike, and an ETH/USD comparison on the same exchange, which lacked comparable ETF exposure, shows no similar pattern, together supporting a BTC-ETF-specific interpretation. The findings suggest that clock-bound financial instruments can reshape when risk concentrates in continuous markets.
In the digital age, Bitcoin remains the first and most notable cryptocurrency. Over the years, its value has increased, making it a desirable digital asset with millions of enthusiasts who trade and invest daily. Bitcoin is highly volatile in comparison with traditional assets and in absolute terms. Understanding its volatility history helps investors decide whether to buy, sell, or hold. A mathematical model that accounts for volatility is essential for these decisions. Unfortunately, Bitcoinâs vast profit potential for investors comes with the dilemma of its negative impact on global environmental health, which needs serious attention. This study aims to model Bitcoinâs return volatility that can support investment decisions and, on the other hand, the negative impact of Bitcoin mining and outline the actions necessary to mitigate it.
Cryptocurrency markets exhibit complex microstructural dynamics characterized by high-frequency volatility bursts, rapid regime switching, and long-range temporal dependencies, which expose several limitations of existing volatility forecasting approaches. In particular, attention-based models suffer from prohibitive quadratic computational cost on long high-frequency sequences, while many recurrent architectures struggle to adapt to regime transitions, asymmetric volatility responses, and risk-aware uncertainty estimation. To address these gaps, this paper proposesCryptoMamba-SSM, a novel volatility prediction framework built upon Mamba-based state space models with linear computational complexity. CryptoMamba-SSM integrates selective memory mechanisms with structured state space representations to effectively capture critical market microstructure signals arising from liquidity shocks and sentiment transitions, while dynamically adjusting memory retention across different volatility regimes. This design enables efficient modeling of long-sequence dependencies inherent in cryptocurrency price movements without incurring the computational bottlenecks of traditional attention-based architectures. Through comprehensive experiments on Bitcoin historical data spanning multiple market regimes, we demonstrate that CryptoMamba-SSM consistently outperforms conventional LSTM, GRU, and Transformer baselines, achieving up to a 23.7% reduction in Mean Absolute Error and a 31.2% improvement in directional accuracy. The selective memory mechanism effectively captures regime-switching behaviors and microstructural anomalies, leading to more reliable short-term volatility risk quantification. Moreover, the linear-time complexity of CryptoMamba-SSM enables real-time processing of high-frequency trading data while maintaining strong generalization across diverse market conditions.
This study assesses whether Bitcoinâs linkage with AI equities remains robust after accounting for equity risk sentiment. To this end, the study employs the multiscale quantileâonâquantile correlation (MSQQC) and multiscale quantileâonâquantile partial correlation (MSQQPC) approaches, using daily data covering 02/01/2019â16/06/2025. The results indicate that BTCâAI comovement is strongly stateâ and frequencyâdependent rather than stable across the joint distribution or across horizons. In the highâfrequency band, dependence is weak and only intermittently significant, with localised negative regions around BTC â 0.20 with AI â 0.30â0.50 and BTC â 0.30 with AI â 0.70. In the midâfrequency band, significance concentrates in the tails, showing negative dependence under downside stress conditions such as BTC â 0.10â0.30 with AI â 0.10, alongside sign changes when BTC is in upperâtail states. In the lowâfrequency band, dependence becomes broadly positive and significant across most quantile combinations, with limited decoupling when AI is highly elevated (â 0.80â0.90) and BTC is also in upper quantiles (â 0.70â0.90). Importantly, conditioning on VIX and VVIX does not materially alter these patterns, suggesting that sentiment influences segments of shortârun dependence but does not overturn the longerârun BTCâAI linkage. The study derives policy recommendations from these findings.
This paper examines whether social media sentiment derived from Twitter and Reddit improves the explanation and prediction of cryptocurrency volatility. Using Bitcoin and Ethereum as benchmark assets, we combine sentiment indicators with GARCH-type models and the HAR-RV framework. Results suggest that cryptocurrency volatility is primarily driven by internal market dynamics rather than social media sentiment.
This paper investigates the extent to which cognitive heuristics, social influence, and digitally-mediated sentiment drive the extreme volatility of cryptocurrency, Decentralised Finance (DeFi), and Non-Fungible Token (NFT) markets, and the degree to which these dynamics deviate from the Efficient Market Hypothesis. Using an integrative narrative review and a synthesis of empirical evidence from 2014â2025, the paper develops the Integrated Digital Asset Behavioural Model (IDABM), a four-variable framework relating market stability to social velocity (Sv ), heuristic load (Hl ), platform gamma (PÎł ), and liquidity leverage (Ll ). The analysis draws on demographic and sentiment data, case evidence from the 2022 Terra/ Luna and FTX collapses, and a comparative cross-asset bias taxonomy. The findings indicate that digital asset markets constitute a pure sentiment environment in which the absence of conventional valuation anchors produces heuristic dominance and structurally amplified herding behaviour. The paper concludes that effective regulation must shift from informational disclosure toward behavioural guardrails â including algorithmic accountability, regulation of gamified trading interfaces, and behavioural literacy requirements.
Digitalisation of finance led to the creation of a digital financial economy, where digital assets such as cryptocurrencies, decentralized financial assets, non-fungible tokens, stablecoins, etc. were traded. In this study, machine learning and deep learning techniques, including ARIMA, FB Prophet, LSTM, and BiLSTM, have been used to forecast the prices of digital assets. In this study, Bitcoin, Ethereum, Uniswap, Aave, ApeCoin, and Decentraland tokens have been categorized into three groups, and the prediction models have been trained using the tokens' closing prices. The authors find that NFTs have been underestimated and that DeFi assets have greater growth potential. Whereas cryptocurrencies have been traded more and shown greater volatility than other asset classes. BiLSTM achieves the best results, with higher accuracy in price prediction. Here, it has been seen that ApeCoin, Decentraland, and Bitcoin are more stable than other assets. Thus, for an optimised portfolio and additional savings, it is necessary to provide a proper asset mix.
We document stable cross-asset patterns in cryptocurrency limit-order-book microstructure: the same engineered order book and trade features exhibit remarkably similar predictive importance and SHAP dependence shapes across assets spanning an order of magnitude in market capitalization (BTC, LTC, ETC, ENJ, ROSE). The data covers Binance Futures perpetual contract order books and trades on 1-second frequency starting from January 1st, 2022 up to October 12th, 2025. Using a unified CatBoost modeling pipeline with a direction-aware GMADL objective and time-series cross validation, we show that feature rankings and partial effects are stable across assets despite heterogeneous liquidity and volatility. We connect these SHAP structures to microstructure theory (order flow imbalance, spread, and adverse selection) and validate tradability via a conservative top-of-book taker backtest as well as fixed depth maker backtest. Our primary novelty is a robustness analysis of a major flash crash, where the divergent performance of our taker and maker strategies empirically validates classic microstructure theories of adverse selection and highlights the systemic risks of algorithmic trading. Our results suggest a portable microstructure representation of short-horizon returns and motivate universal feature libraries for crypto markets.
This study introduces a novel approach to walk-forward optimization by parameterizing the lengths of training and testing windows. We demonstrate that the performance of a trading strategy using the Exponential Moving Average (EMA) evaluated within a walk-forward procedure based on the Robust Sharpe Ratio is highly dependent on the chosen window size. We investigated the strategy on intraday Bitcoin data at six frequencies (1 minute to 60 minutes) using 81 combinations of walk-forward window lengths (1 day to 28 days) over a 19-month training period. The two best-performing parameter sets from the training data were applied to a 21-month out-of-sample testing period to ensure data independence. The strategy was only executed once during the testing period. To further validate the framework, strategy parameters estimated on Bitcoin were applied to Binance Coin and Ethereum. Our results suggest the robustness of our custom approach. In the training period for Bitcoin, all combinations of walk-forward windows outperformed a Buy-and-Hold strategy. During the testing period, the strategy performed similarly to Buy-and-Hold but with lower drawdown and a higher Information Ratio. Similar results were observed for Binance Coin and Ethereum. The real strength was demonstrated when a portfolio combining Buy-and-Hold with our strategies outperformed all individual strategies and Buy-and-Hold alone, achieving the highest overall performance and a 50 percent reduction in drawdown. A conservative fee of 0.1 percent per transaction was included in all calculations. A cost sensitivity analysis was performed as a sanity check, revealing that the strategy's break-even point was around 0.4 percent per transaction. This research highlights the importance of optimizing walk-forward window lengths and emphasizing the value of single-time out-of-sample testing for reliable strategy evaluation.
The book Solutions and Technologies for Modern Business stands as a relevant contribution to understanding the technological and strategic transformations impacting the contemporary business environment.With a broad approach, the work offers reflections on innovative solutions, technological tools, and management practices aimed at strengthening and adapting organizations in the face of constant market changes.By emphasizing the integration of theory and practice, the book contributes to the development of critical analyses regarding the use of technology in organizational processes, highlighting its importance for competitiveness, innovation, and decision-making.The work brings together diverse perspectives that enrich academic debate and encourage the development of more efficient and sustainable strategies in the business context.This book is recommended for professors, students, and professionals in the fields of administration, management, technology, and business, as well as for anyone interested in expanding their knowledge of solutions and technologies applied to the corporate environment.It is a work that fosters learning, reflection, and the improvement of organizational practices in the digital era.
Bitcoinâs price dynamics are influenced by both internal factors (e.g., supply shocks, investor sentiment) and external drivers, among which the stability of stablecoins has attracted increasing academic and regulatory attention. This paper investigates the effect of stablecoin peg deviations (USDT and USDC) on Bitcoin returns using daily data from January 2020 to August 2025. Based on a vector autoregression (VAR) framework, we conduct unit root tests, lag order selection, model estimation, Granger causality tests, and impulse response analysis. Results show that both Bitcoin returns and stablecoin deviations exhibit strong short-term inertia. USDT and USDC deviations significantly Granger-cause Bitcoin returns, whereas the reverse causality is weaker. Impulse responses indicate that stablecoin deviations first produce positive shocks to Bitcoin returns, followed by negative corrections that gradually stabilize. The effect of USDT is more pronounced and persistent, underscoring its central role in cryptocurrency markets. These findings highlight the importance of monitoring stablecoin market stability, especially USDT, for investors and regulators seeking to manage systemic risks in crypto markets.
Cryptocurrencies have upended the financial industry since they provide decentralized and peer-to-peer transactions. However, due to market volatility and the numerous non-linear relationships between price dynamics and human mood, forecasting Bitcoin values is a difficult task. The deep learning architecture shown in this work combines sentiment confidence scores derived from cryptocurrency-related tweets utilizing Transformer-based natural language processing with historical price indicators. The model incorporates Convolutional Neural Networks (CNN) to detect local time-series patterns and Long Short-Term Memory (LSTM) networks to produce long-term dependencies. We apply this architecture, involving sequence-based preprocessing and normalization, to Bitcoin and Ethereum to ensure robustness. Evaluations in comparison to baseline models Sentiment fusion dramatically increases predicting accuracy, especially during times of market turbulence, according to CNN-LSTM without sentiment, vanilla LSTM, and ARIMA. Our research helps develop scalable, sentiment-aware financial forecasting algorithms that better reflect the behavior of real markets.
This study systematically reviews scientific research on predicting cryptocurrency markets. A total of 790 articles obtained from the Web of Science database were included in the analysis, and the structure of the literature was evaluated using bibliometric methods. The preliminary investigation indicated that studies examining the prediction of cryptocurrencies have undergone a substantial increase since 2016. While a significant proportion of the extant literature pertains to Bitcoin, the first cryptocurrency, it is evident that other cryptocurrencies, such as Ethereum, have also attracted the attention of researchers over the years. The analysis yielded four primary categories: machine learning-based prediction methods, financial risk and volatility analyses, behavioral and technical determinants, and finally, advanced deep learning methods. In the context of cryptocurrency prediction, studies have underscored the significance of attributes, emphasizing their role in enhancing the efficacy of prediction models. These studies have also highlighted the impact of integrating machine learning and deep learning-based models with conventional methods in enhancing the performance of established models. The study emphasizes the necessity to direct future research towards the integration of behavioral indicators and the examination of multiple market relationships.
This study aims to analyze the volatility spillovers between Bitcoin and Ethereum, the two main actors in the cryptocurrency market, and altcoins across sectoral and financial groups. Using data from January 1, 2021, to March 6, 2023, the study applied the VAR-based method developed by Diebold and Yılmaz (2012) and measured both directional and total volatility spillovers. The findings show that Bitcoin's volatility largely stems from internal dynamics and spreads to other cryptocurrencies to a limited extent. In contrast, Ethereum is more affected by external shocks and exhibits a stronger volatility spillover across the market. Among altcoin categories, Gaming, Analytics, and DeFi groups were found to be the most influential in volatility transmission, while thematic tokens such as NFT, Web3, and Metaverse were more sensitive to external volatility. In contrast, stablecoins and tokens in the identity and healthcare sectors were found to have relatively low volatility and a more stable structure. These results offer important insights for investors and regulators regarding risk management strategies and portfolio diversification. The study provides a valuable framework for understanding the systematic volatility dynamics within the cryptocurrency ecosystem
This research investigates the predictive power of news sentiment from Google News on Bitcoin price movements, leveraging a five-year dataset of news headlines (2019 to 2024). By correlating sentiment scores with historical Bitcoin prices, the study employs various machine learning algorithms to forecast price trends. The results indicate that while Decision Tree and Random Forest models offer balanced predictions, Logistic Regression and Support Vector Machines achieve high AUC scores but suffer from class imbalance. In contrast, NaĂŻve Bayes and KNN models prove less effective. The findings suggest that sentiment analysis of news headlines can provide moderate short-term predictions for Bitcoin price fluctuations. This study introduces an innovative tool for investors and market analysts, offering insights into the influence of news sentiment on cryptocurrency prices.
Deep learning has emerged as a widely applied approach across various fields, with finance and forecasting being among its most prominent areas of use. Within this domain, different deep learning architectures have been developed to address specific prediction problems. This study compares the performance of ARIMAX and several deep learning modelsâincluding LSTM, BILSTM, CNN-LSTM, GRU, and TFTâin forecasting Bitcoin prices. The dataset consists of daily values from January 2014 to January 2025. The dependent variable is the daily Bitcoin closing price ($), while the independent variables include oil price (USD/barrel), gold price (USD/ounce), platinum price ($/XPT), and the USD/TRY exchange rate. All analyses were conducted in Python using Google Colab, with the Keras library employed for model implementation. Root Mean Square Error (RMSE) was selected as the evaluation metric for predictive accuracy. The results indicate that the TFT model achieved the highest predictive performance, followed closely by the GRU model. LSTM, BILSTM, and ARIMAX models showed similar yet weaker performance, while the CNN-LSTM model produced the least accurate forecasts, with significantly higher RMSE values compared to the other models.
Virtual currency has become one of the most sought-after alternative assets in the past decade with bitcoin being a leading example. value leapt from its starting price of $0.0025 to increase by more than 40 million times that amount, creating one of the greatest rises in value in the entire history of finance. In the past few years, many academic studies show that even though Bitcoin runs independently from traditional finance, but still there is a high correlation between Bitcoin and stock market. In particular, following the introduction of Bitcoin options back in 2017, Bitcoin now appears more predictive of stock return movements than before. Research by Afees A. Salisu and his coworkers display that a solitary Bitcoin price prediction model using an optimized predictive regression framework notably surpasses older ones. but donât say how long this goes on Therefore this research will go to try and determine the time frame when Bitcoin is better at predicting the future of the stock market as opposed to stock options. Also, weâll use machine learning techniques to train machine learning models to predict the movements of the stock market and see if they work.
This study introduces a unified and methodologically symmetric comparative framework for multivariate cryptocurrency forecasting, addressing long-standing inconsistencies in prior research where model families, feature sets, and preprocessing pipelines differ across studies. Under an identical and rigorously controlled experimental setup, we benchmark six deep learning architecturesâLSTM, GPT-2, Informer, Autoformer, Temporal Fusion Transformer (TFT), and a Vanilla Transformerâtogether with four widely used econometric models (ARIMA, VAR, GARCH, and a Random Walk baseline). All models are evaluated using a shared multivariate feature space composed of more than forty technical indicators, identical normalization procedures, harmonized sliding-window formations, and aligned temporal splits across five high-liquidity assets (BTC, ETH, XRP, XLM, and SOL). The experimental results show that transformer-based architectures consistently outperform both the recurrent baseline and classical econometric models across all assets. This superiority arises from the ability of attention mechanisms to capture long-range temporal dependencies and adaptively weight informative time steps, whereas recurrent models suffer from vanishing-gradient limitations and restricted effective memory. The best-performing deep learning models achieve MAPE values of 0.0289 (BTC, GPT-2), 0.0198 (ETH, Autoformer), 0.0418 (XRP, Informer), 0.0469 (XLM, Informer), and 0.0578 (SOL, TFT), substantially improving upon the performance of both LSTM and all econometric baselines. These findings highlight the effectiveness of attention-based architectures in modeling volatility-driven nonlinear dynamics and establish a reproducible, symmetry-preserving benchmark for future research in deep-learning-based financial forecasting.
ABSTRACT Based on the rationale that returns and volatility are interrelated, we apply a multilayer network framework involving the return layer and volatility layer of cryptocurrencies, NFTs, and DeFi assets over the period January 1, 2018âJanuary 23, 2024. The results show significant connectedness in each of the return and volatility layers, with major cryptocurrencies such as Bitcoin and Ethereum playing a central role. Large spikes in the level of connectedness are noticed around COVIDâ19 pandemic and RussiaâUkraine conflict, and Bitcoin and Ethereum emerge as net transmitters of returns and volatility shocks, emphasizing their significant role around these crisis periods. Notably, a strong positive rank correlation exists between the return and volatility layers, highlighting the significant riskâreturn relationship in the digital asset class. The findings suggest that economic actors should not ignore the interconnectedness between the return and volatility layers in the system of cryptocurrencies, NFTs, and DeFi assets for the sake of a comprehensive analysis of information flow. Otherwise, a share of the information flow concerning the returnâvolatility nexus across these digital assets would be missed, possibly leading to inferences regarding asset pricing, portfolio allocation, and risk management.
BPN), MATLAB, mean absolute percentage error (MAPE) In this study, we investigate Bitcoin price volatility from December 15, 2014 to January 29, 2024 using an integrated, multisource feature set and an optimization-learning pipeline that couples Taguchi orthogonal arrays with a backpropagation network (BPN) implemented in MATLAB.Publicly available market variables were prioritized and nonquantifiable exogenous shocks were not modeled; Taguchi screening identified critical predictors and simultaneously tuned control factors (network specification, hidden-neuron count, and currency inclusion), after which the BPN was trained on aligned weekly (n = 573) and monthly (n = 108) datasets to ensure cross-market comparability.Model accuracy, assessed by mean absolute percentage error (MAPE), improved substantially after Taguchi-guided selection and configuration-weekly MAPE decreased from 3.23% to 0.36% and monthly MAPE from 6.32% to 0.07%demonstrating the efficacy of the proposed optimization framework.Out-of-sample forecasts for February-April 2025 achieved predominantly sub-10% MAPE, while high-error instances were analyzed and attributed to contributing factors, yielding decision-relevant insights for practitioners and researchers.Collectively, the results show that systematic variable selection and orthogonal-array-based model design materially enhance neural forecasts of cryptocurrency prices and provide a reproducible pathway to accurate, time-efficient prediction.