Marjan Sadat Fatemi Ghomi, Abbas Saghaei, Majid Mirzaee Ghazani
Accurately forecasting cryptocurrency price movements following market overreactions is crucial for traders, investors, and risk managers operating in highly volatile environments. This study presents a novel multi-source, multi-output deep learning framework designed to predict the direction of price changes in four major cryptocurrencies — Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), and Ripple (XRP) — immediately after overreaction events. By jointly modeling these assets, our approach captures their interconnected market dynamics, enhancing predictive accuracy. We compile an extensive dataset with over 656 features from diverse sources, including historical trading data, on-chain metrics, technical indicators, and social sentiment data from Google Trends, collected at both daily and intraday frequencies. To improve model interpretability and performance, we introduce two engineered features — price change magnitude and price variation speed — that effectively represent intraday volatility. Feature selection using a Random Forest approach reduces the feature set to 30 key variables, ensuring robustness and avoiding overfitting. Using three advanced deep learning architectures — LSTM, RNN, and CNN — we train models to classify the next-day price movement as upward or downward. Empirical results demonstrate that the multi-output LSTM achieves an F1-score of 73.42%, outperforming both single-asset models (62.95–68.25%) and alternative architectures. These findings highlight the benefits of joint modeling, leading to more reliable forecasts during turbulent market conditions. Our framework offers a practical tool for algorithmic trading, portfolio management, and risk mitigation in the dynamic cryptocurrency landscape.
The paper provides an integrated literature review of recent scientific publications on quantum computing in finance and identifies promising directions for future research on the subject. The review covers seven thematic areas: portfolio optimization, derivative pricing and stochastic volatility, quantum machine learning for fraud detection and credit risk, insurance and actuarial science, mixed-frequency econometrics, fuzzy-quantum approaches for financial explainability, and security of cryptocurrencies. The paper compiles the essential quantum computational methods proposed in the literature, outlines their economic significance and the existing constraints for empirical testing and implementation, and discusses cross-cutting issues of explainability, trustworthy AI, robustness, and governance that arise across these application domains. Drawing on this review, the paper identifies five macro-gaps in the existing literature and proposes seven concrete directions for future research, grounded in European financial data and currently available quantum computing infrastructure. A special focus throughout is the increasingly available quantum infrastructure in Europe and the regulatory emphasis on trustworthy artificial intelligence, both of which create timely opportunities for future applications in financial modelling, risk management, and explainable financial AI.
Abstract This study examines the dynamic volatility spillover between financial technologies and traditional and alternative financial markets. The analysis utilizes daily data covering the period from January 2018 to March 2026 for FinTech ETFs, the Nasdaq, Bitcoin, and gold markets. Interconnectivity among financial markets was analyzed using the time-varying parameter VAR (TVP-VAR) connectedness approach. The findings indicate the presence of a moderate and time-varying connectivity structure among the markets. In particular, a strong interaction was observed between the FinTech and Nasdaq markets, while Bitcoin was found to play a significant role as a volatility transmitter during certain periods. The gold market, on the other hand, generally exhibited more stable and limited interactions. Additionally, the study found that financial market linkages increase during periods of crisis and uncertainty. These results highlight that the transmission of volatility across financial markets has a dynamic structure and that portfolio diversification strategies should be evaluated accordingly. By examining FinTech markets alongside other major asset classes, the study provides a timely and comprehensive contribution to the literature.
Bitcoin has become an increasingly important asset for portfolio allocation, yet its diversification value and option-implied information remain difficult to evaluate. This paper examines Bitcoin risk from portfolio and option-implied perspectives. This study assesses whether Bitcoin improves the risk-return opportunity set with traditional assets and whether its diversification role remains stable during market stress. Option-implied measures, including the 25-delta Risk Reversal (RR25), smile curvature, and an at-the-money Implied-Volatility-minus-Realized-Volatility (IV-minus-RV) proxy, are then constructed to predict market conditions. Portfolio analysis shows that Bitcoin can improve risk-return tradeoffs but does not function as a stable minimum-variance asset or a reliable crisis hedge. Baseline regressions provide limited evidence that RR25 consistently predicts future realized volatility or returns. However, extreme negative short-dated RR25 is followed by higher future realized volatility, suggesting RR25 is more informative as a nonlinear stress-state indicator than as a continuous forecasting variable. Smile curvature captures the implied-volatility surface but provides weaker predictive information. Finally, the IV-minus-RV analysis shows that gradual RR25-based exposure scaling achieves a better risk-adjusted profile than a binary exposure rule. Overall, the findings indicate that Bitcoin's diversification benefits and option-implied information are state-dependent.
A Universal, Options-Free, One-Byte Market-State Primitive: Cross-Asset Transfer, Distributional-Novelty Detection, and Privacy-Preserving Federation Randolph James Ferlic, M.D. and Kimberly Kate Ferlic (Fieldstone Analytics, LLC, Austin, TX, USA) Preprint · Zenodo DOI: 10.5281/zenodo.22116173 · CC-BY 4.0 · Community: spiral-domain-encoder-campaign Abstract A companion study established that a fixed class-discriminant encoder — which reduces a window of a multivariate stream to a single 8-bit token — is a near-lossless detector of market stress across asset classes, but a taxed classifier and a null forecaster. This study asks, under strict pre-registration, whether that detector becomes a *universal, deployable primitive*, and finds that it does, along five axes, with an honest boundary preserved throughout. (1) The codebook is universal: fit on equities alone, it detects stress zero-shot across cryptocurrency, commodities, emerging-market funds, credit, and foreign exchange at a median AUC of 0.84, within 0.01 of a bespoke per-asset codebook, because the calm-to-stress discriminant direction is nearly identical across asset classes (median cross-asset cosine 0.94). (2) The token detects distributional regime-change that volatility structurally misses — the one place it beats volatility — and is an orthogonal channel that improves a volatility-only stack by +0.18 AUC on a mixed target. (3) It generalizes to credit and rates (pooled AUC 0.91) and to currency crosses (0.80), including markets with no options index. (4) It is cheap at scale and low-data: a genuine one-byte-on-the-wire firm-wide dashboard detects systemic stress at 0.92 at 200× compression — a 42-instrument universe fits in ~10 kilobytes per year — and a new deployment is usable after ~3 months of calm history. (5) The same one-byte token supports privacy-preserving federation: institutions each sharing one token per day match full-data systemic-detection utility while their individual positions are unrecoverable even to a non-linear, colluding, market-informed adversary (incremental reconstruction R² ≈ 0.01). All headline results survive a permutation-placebo leakage test, moving-block bootstrap confidence intervals, and robustness to split, seed, stress-label, and a strict recent holdout. Every negative is reported, and two honest scope refinements are disclosed: a formal differential-privacy guarantee is event-level, not stream-level, and portfolio membership is coarsely inferable while positions are not. Highlights · Universal codebook — one equity-fit codebook detects stress zero-shot across every asset class (median 0.84, transfer loss ~0.01, 22/23 targets); mechanism = a shared cross-asset stress direction (cosine 0.94). · A capability volatility lacks — the token detects distributional (shape) regime-change where trailing volatility is at or below chance; an orthogonal channel that complements any GARCH/VIX stack. · Coverage — credit/rates (pooled 0.91) and FX/carry white-space (0.80), options-free, at one byte per instrument. · Compression-at-scale — a 1-byte-on-the-wire firm-wide dashboard at AUC 0.92, 200× compression, ~10 KB/instrument-year; detection-sufficient encoder ≈ 590 multiply-accumulates and < 3 KB (microcontroller-class). · Low-data cold-start — usable after ~3 months of calm data; saturates within ~2 years; train on a trailing window. · Privacy-preserving federation — full-data-equivalent systemic detection with individual positions non-invertible even under a strong adversary; optional event-level differential privacy; manipulation-robust median aggregation; per-channel attribution; a yield-curve-shape token. · Information-theoretic characterization — detection is a ~1-bit decision while classification is high-bit; competitive with principled two-sample/OOD tests at O(1) online cost. · Honest negatives and hardening — network topology, temporal-token, cross-sectional ranking, and tail-edge results are all reported null; every headline result is leakage-tested (permutation placebo → chance) and confidence-bounded. What this record contains · Manuscript_Paper40.pdf — the manuscript (single-column preprint). · PAPER_40_ZENODO_ARCHIVE.zip — the reproducibility archive: seven pre-registration scopes (frozen outcome bands) covering studies FIN-15 … FIN-45; the deterministic per-study runners and the shared frozen encoder/loader and federation modules; the per-study JSON result summaries behind every figure and table value; the figure-rebuild script; and the eight figures. All paths and identifiers are scrubbed and leak-scanned per the campaign deposit discipline; no raw market data is redistributed. Cite as R. J. Ferlic and K. K. Ferlic, "A universal, options-free, one-byte market-state primitive: cross-asset transfer, distributional-novelty detection, and privacy-preserving federation," Zenodo, 2026, doi: 10.5281/zenodo.22116173. License and patent notice Released under the Creative Commons Attribution 4.0 International License (CC-BY 4.0). Consistent with that license, no patent, patent-application, or other intellectual-property right of the authors is licensed, waived, granted, or otherwise conveyed by this deposit; the methods described — including the single-token class-discriminant encoder, its multi-token product-quantization variant, its unsupervised nearest-centroid-distance monitoring mode, and the privacy-preserving multi-party aggregation of its tokens — are the subject of filed and pending U.S. patent applications. Licensing inquiries: randolphf@fieldstoneanalyticsllc.com. Companion deposits (spiral-domain-encoder-campaign) · Single-token financial market-state monitor (companion study): doi:10.5281/zenodo.22101085 · Single-token industrial sensor substrate: doi:10.5281/zenodo.20854722 · Hardening and generality characterization: doi:10.5281/zenodo.20802759 · Deterministic multi-token token ladder: doi:10.5281/zenodo.22003179 Keywords universal codebook; decision-preserving compression; distributional novelty; federated privacy; differential privacy; systemic risk; market-stress detection; class-discriminant codebook; cross-asset transfer; edge computing; anomaly detection; pre-registration; honest negatives; financial time series; regime detection; credit risk; yield curve; volatility regime; VIX; market surveillance
Serhat Yüksel, Gabriela Oana Olaru, Serkan Eti, Hasan DİNÇER
It is frequently emphasized in the behavioral finance literature that investment decisions cannot be explained solely by economic indicators and rational expectations and that psychological factors also play a significant role in this process. However, the lack of a comparative analysis of the importance of psychological factors influencing investor behavior in the literature and the lack of consensus on which factors are more dominant constitute a fundamental problem. This deficiency leads to significant uncertainties in both theoretical modeling and practical investment strategies, increasing market risks such as irrational price movements, speculative bubbles, and panic selling. In this context, the aim of this study is to determine the relative importance of the fundamental psychological factors influencing investor decisions and, considering these factors, to identify the most appropriate investment alternatives for individuals. This study develops a new integrated decision-making model to answer these research questions. Considering the demographic characteristics of the experts, importance coefficients are calculated using the Euclidean distance-based weighting approach. Criterion weights are then determined using the Entropy method, and the MABAC and MAIRCA methods are applied to rank investment alternatives. Additionally, fractal fuzzy sets based on the Sierpinski triangle are integrated into the proposed model to model uncertainty more effectively. The study's contributions to the literature are highlighted in three dimensions: (1) psychological factors, often overlooked in the literature, are included in the criteria set; (2) expert weights are differentiated based on demographic characteristics rather than assumed to be equal; and (3) expert opinions are modeled more flexibly and precisely using new fractal number-based fuzzy sets. The findings indicate that trust is the most critical psychological factor, followed by loss aversion. In terms of investment alternatives, stocks stand out as the most suitable option, while bonds/deposits and gold are other important alternatives, with cryptocurrencies and real estate ranking next.
Liu Jin, Yahya M.H., Saidatunur Fauzi Saidin, Li Lu
Abstract Multivariate cryptocurrency forecasting is challenging because market series exhibit non-stationarity, cross-variable dependence, heterogeneous temporal scales, and abrupt short-term fluctuations. Although Transformer-based forecasting models can capture long-range temporal relationships, directly modeling raw high-frequency sequences may obscure dominant periodic structures and increase computational cost. This study proposes a frequency-guided multi-scale decomposition and patch Transformer, termed FMDP-Transformer, for multivariate cryptocurrency time-series forecasting. First, a frequency-guided multi-scale representation module estimates dominant temporal periods from the Fourier amplitude spectrum and constructs scale-specific representations through period-dependent average pooling. This module is designed to extract multi-scale periodic information and attenuate short-term disturbances rather than to perform explicit anomaly detection. Second, the resulting representation is decomposed into trend and residual components. A lightweight linear projection is used for parsimonious trend extrapolation, while the residual component is divided into overlapping patches and processed by a Transformer encoder to model local and long-range temporal dependencies. The forecasts produced by the two branches are subsequently combined. Experiments on Bitcoin, Dogecoin, and Binance Coin data derived from the G-Research Crypto Forecasting dataset evaluate the model under multiple forecasting horizons. Comparisons with recurrent, decomposition-based, patch-based, inverted-Transformer, and multi-scale forecasting models, together with component ablations and computational-complexity analysis, are used to assess its effectiveness. The results indicate that frequency-guided multi-scale representation, decomposition, and patch tokenization provide complementary benefits for multivariate cryptocurrency forecasting. Nevertheless, the proposed frequency-guided smoothing operation does not explicitly identify statistical anomalies, and abrupt market movements may contain predictive information rather than noise.
This study proposes a Bitcoin price prediction model utilizing Long Short-Term Memory (LSTM) networks, integrating technical indicators, Reddit sentiment indicators, and on-chain data. The cryptocurrency market, particularly Bitcoin, exhibits extreme price volatility, despite its high profit potential. This volatility stems from a combination of macroeconomic factors, market participant sentiment, and fluctuations in supply and demand within the blockchain ecosystem. Existing literature typically examines only one or two types of data—whether technical, sentiment, or on-chain—without systematically verifying the complementary effects of integrating these heterogeneous data sources on predictive performance. To address this gap, this study quantitatively analyzes the contribution of each data type by constructing four experimental settings combining technical indicators, Reddit sentiment, and on-chain data based on Bitcoin price movements. Moreover, the proposed LSTM model is benchmarked against Linear Regression (LR), Random Forest (RF), and XGBoost (XGB) under identical experimental conditions. Evaluation metrics, including RMSE, MAE, and MAPE, indicate that while the LSTM model demonstrates superior predictive performance using only technical indicators, the inclusion of both Reddit sentiment and on-chain indicators results in a slight increase in error metrics. Nevertheless, this study emphasizes the potential of capturing the multifaceted characteristics of the market, which are often overlooked with single price-based indicators. It provides an empirical foundation supporting the effectiveness of heterogeneous data integration in future cryptocurrency price prediction research.
This study proposes a framework combining Variational Mode Decomposition (VMD) with a relevance-driven selection process to reduce noise and redundancy in financial time-series forecasting. The original time series is decomposed by VMD into intrinsic mode functions (IMFs), which are then evaluated using three relevance metrics: relative energy contribution, mutual information, and Spearman's rank correlation coefficient. These metrics identify the IMFs most strongly associated with future price movements. As opposed to conventional VMD-based approaches that treat all IMFs equally, the proposed relevance-driven selection process adapts IMF selection to the statistical properties of the analyzed market, thereby improving model generalization across different volatility conditions and forecasting horizons. This study makes three main contributions: (i) developing a relevance-driven IMF selection strategy to overcome limitations of traditional VMD methods, (ii) designing a hybrid framework that integrates multiscale decomposition with nonlinear information filtering, and (iii) conducting a comprehensive empirical evaluation of the proposed models. Experiments on hourly Bitcoin (BTC)/USD data from 2018 to 2025 show that the VMD-RDIC-deep learning models achieves strong forecasting performance. The results show that the proposed relevance-driven decomposition framework improves prediction accuracy and robustness compared with traditional statistical models, including Autoregressive Integrated Moving Average (ARIMA), as well as machine learning and deep learning approaches, highlighting its suitability for complex and volatile financial markets. Received: 18 January 2026 | Revised: 13 April 2026 | Accepted: 23 July 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available at https://www.kaggle.com/datasets/novandraanugrah/bitcoin-historical-datasets-2018-2024. Author Contribution Statement Maryam Maatallah: Conceptualization, Methodology, Software, Data curation, Writing – original draft, Visualization. Mourad Fariss: Software, Formal analysis, Writing – original draft. Hakima Asaidi: Validation, Investigation, Writing – review & editing. Mohamed Bellouki: Resources, Writing – review & editing, Supervision, Project administration.
A One-Byte, Options-Free Market-State Monitor: Detection-Preserving Compression of Financial Data Streams with a Class-Discriminant Token Randolph James Ferlic, M.D., and Kimberly Kate Ferlic — Fieldstone Analytics, LLC Correspondence: randolphf@fieldstoneanalyticsllc.com Preprint · Zenodo DOI: 10.5281/zenodo.22101085 · CC-BY 4.0 Abstract A previously described class-discriminant encoder reduces a window of a multivariate stream to a single 8-bit token (statistical features projected onto a shrinkage-regularized linear-discriminant and principal-component subspace, quantized to a k-means centroid, read by a lightweight head), trading thousands-fold compression for a preserved decision. We ask, under strict pre-registration, how much of that property survives on financial data — the hardest domain for naive machine learning. Across five asset classes (equities, foreign exchange, rates, cryptocurrency, commodities) and thousands of trading days of real daily price/volume data, we find a sharp, consistent boundary. As a detector, the token is near-lossless: an unsupervised codebook fit on calm windows only flags market-stress windows by nearest-centroid distance at a mean AUC of about 0.90, within 0.01–0.04 of a full 50-feature detector, at roughly 500× compression and with no labels. Pooled across a thirteen-name basket, a one-byte-per-instrument, options-free Token Market-State Index tracks the VIX volatility index (Spearman 0.62) and detects market stress that neither a full-feature detector nor VIX statistically outperforms at this sample size — a result that survives purged, embargoed walk-forward validation and block-bootstrap confidence intervals across 2010–2026, and generalizes across all five asset classes, to intraday (hourly) frequency, and to a distinct cross-asset macro risk-off state. A second, complementary detector built from the same tokens — the cross-sectional co-movement of the per-instrument token distances — flags correlation and contagion regimes, adding information beyond volatility (a +0.11 AUC point-estimate increment) and rivaling the absorption-ratio systemic-risk measure computed from the full return covariance. As a classifier or forecaster, the same token is honestly limited: it pays a real 0.07–0.16 AUC tax on supervised volatility-regime and market-state classification (only partly recovered by multi-token product quantization), it is coincident rather than leading, and it shows no directional-return skill at any horizon. The unifying regularity is that the encoder retains what a detector needs and discards what a classifier or forecaster needs — the same compression boundary observed for physiological and industrial signals, now mapped in finance. We report all negatives, including two pre-registered red flags that caught bugs in our own code before they became false results. Highlights · The single 8-bit token is a strong coincident detector of market stress: an unsupervised calm-fit codebook flags stress windows at a mean AUC of ~0.90, within 0.01–0.04 of a full 50-feature detector, at ~500× — indeed as few as two bits — compression, with no labels. · A pooled, options-free, one-byte-per-instrument Token Market-State Index tracks VIX (Spearman 0.62) and detects market stress that neither a full-feature detector nor VIX statistically outperforms at this sample size — surviving purged/embargoed walk-forward and block-bootstrap intervals across 2010–2026, and generalizing across equities, FX, rates, cryptocurrency and commodities, to intraday frequency, and to a distinct macro risk-off state. · A second detector from the same tokens — cross-sectional co-movement — flags contagion / correlation regimes, adding information beyond volatility (a +0.11 AUC point-estimate increment) and rivaling the absorption-ratio systemic-risk measure, from compressed tokens rather than the full covariance. · Honest limits, fully reported: the token pays a real 0.07–0.16 AUC classification tax, is coincident, not leading, and shows no directional-return skill at any horizon; a coincident de-risking rule reduces drawdown but is presented explicitly not as a trading strategy. Two self-caught bugs (a lookahead label and a zeroed factor) are disclosed. · No new method is claimed: the contribution is a pre-registered map of where an extreme-compression class-discriminant token is a detector and where it is not, on real public market data, with all outcomes — including those that missed their bands — reported verbatim. Statistical candor is explicit: only the market-state result carries bootstrap confidence intervals; the other comparative figures are single-split point estimates, pre-registered but multiplicity-uncorrected. What this record contains · The manuscript (PDF), with a fourteen-study summary table and eight figures. · A reproducibility archive (`PAPER_39_ZENODO_ARCHIVE.zip`): the eight pre-registration scopes with frozen outcome bands, the deterministic per-study runners (FIN-1 through FIN-14), the per-study JSON result summaries behind every figure and table value, and the figures. All data are public daily and hourly OHLCV series and the CBOE Volatility Index (VIX); no raw data is redistributed — the loaders fetch the public source at run time. Cite as R. J. Ferlic and K. K. Ferlic, "A one-byte, options-free market-state monitor: detection-preserving compression of financial data streams with a class-discriminant token," Zenodo, 2026, doi: 10.5281/zenodo.22101085. License and patent notice Released under CC-BY 4.0. Consistent with Section 2(b) of that license, no patent, patent-application, or other intellectual-property right of the authors is licensed, waived, granted, or otherwise conveyed by this publication or by any reuse of it; the methods described herein — including the single-token class-discriminant encoder, its multi-token product-quantization variant, and its unsupervised nearest-centroid-distance monitoring mode — are the subject of filed and pending U.S. patent applications, and attribution under CC-BY does not extend to those rights. Inquiries regarding licensing of the encoding methods may be directed to randolphf@fieldstoneanalyticsllc.com. Companion deposits Part of the single-token class-discriminant codebook family on Zenodo (community: spiral-domain-encoder-campaign), which includes the single-token industrial sensor substrate (doi: 10.5281/zenodo.20854722), the hardening-and-generality characterization (doi: 10.5281/zenodo.20802759), and the deterministic multi-token token-ladder (doi: 10.5281/zenodo.22003179). This deposit applies the same previously described encoding method to a new input domain — financial data streams. Keywords decision-preserving compression, class-discriminant codebook, market-stress detection, contagion and correlation regime, systemic risk, volatility regime, VIX, anomaly detection, edge computing, pre-registration, honest negatives.
Tokenized real-world assets trade continuously on public blockchains, but thevenues that price their underlyings do not. For roughly two-thirds of wall-clocktime, an on-chain protocol must value collateral against a market that is shut. This record accompanies "Coverage Inversion: Calibration-Transparent Fair-ValueOracles for Closed-Market Hours". The paper inverts the conventionalpoint-plus-confidence oracle interface: the target coverage level tau becomes apublished product input, and every served price band carries a calibrationreceipt that a third party can reconstruct from public data. CONTENTS The paper (67 pages) and the LaTeX source arXiv compiles. A reference implementation in three languages — the Python serving path, its Rust port (pinned to the Python by a 329-case parity harness), and the Anchor programs for the on-chain publish path. The calibration artefacts: the 20 deployment scalars that define the served bands, including the SHA-256-stamped frozen artefact used for out-of-sample validation. The public band archive: an append-only record of bands actually served, with Saturday width commitments published before Monday's open, so the claims can be audited after the fact rather than taken on trust. EVIDENCE Two closed-market panels over the same ten US-listed tickers, 2014-2026: 5,996 weekend windows (Friday close to Monday open) and 22,624 overnight windows (close to next open). The headline weekend result is held out by leave-one-symbol-out cross-validation at tau = 0.95: realised coverage 0.9497 +/- 0.0128, every fold passing Kupiec. On a 40-cell symbol-by-tau grid the architecture passes 40 of 40 Kupiec tests, against 31 of 40 for the strongest practitioner baseline (GARCH-t). The same architecture, with only its gap selector changed, carries from weekends to overnight gaps — calibration transparency is a property of closed market hours generally, not of weekends specifically. Because an earnings release has a publicly known date and session, the band widens deterministically ahead of it. No incumbent oracle exposes calendar-conditioned coverage. LICENSING This record is CC BY 4.0. The source code in the reference-implementation archive is Apache-2.0 and ships its own LICENSE file, which governs that archive.