This study evaluates the risk-adjusted consequences of large-weight equity–cryptocurrency substitution and examines whether these effects differ systematically across equity styles (Growth vs. Value) and regions (Asia, Europe, and the Americas). Using daily data from January 1, 2021 to December 31, 2023, the analysis constructs style-segmented MSCI country equity indices and compares annual Sharpe ratios under four constant-mix, daily rebalanced strategies: (S1) 100% equity; (S2) 50% equity / 50% cryptocurrency; (S3) 50% equity / 50% global bonds; and (S4) 33.33% equity / 33.33% global bonds / 33.33% cryptocurrency. Five major non-stablecoin cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Solana (SOL), and Binance Coin (BNB)—are evaluated individually to isolate coin-specific substitution effects. Performance is assessed annually and compared across Growth and Value portfolios within identical country–year–cryptocurrency environments to identify style-dependent outcomes. The results show that cryptocurrency substitution generally improves Sharpe ratios, but the effects are benchmark-, coin-, style-, and region-dependent. Improvements are more heterogeneous under direct 50% equity–crypto substitution, especially for Value portfolios, but become uniformly positive when crypto is introduced within an equity–bond benchmark. SOL provides the largest and most consistent improvements, while ETH and BNB are frequently strong and BTC is the least consistent. Growth portfolios benefit more than Value portfolios in Asia and the Americas, whereas Europe shows more style-neutral effects. Because cryptocurrency shocks are common within a given coin-year, statistical inference is interpreted as cross-market evidence rather than fully independent observations.
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.
Abstract The emergence of cryptocurrencies has presented investors with novel portfolio diversification opportunities. This study investigates the interplay between precious metals and cryptocurrencies, examining their potential for enhancing portfolio returns and mitigating risk. Using daily closing prices from August 2017 to November 2022, we employ an autoregressive distributed lag (ARDL) approach to analyze comovement and causality between these asset classes. Our findings reveal a short-run linkage between Bitcoin, precious metals, and other cryptocurrencies but no long-run cointegration. Notably, gold prices unidirectionally influence cryptocurrency prices, a relationship not observed with other assets. This absence of long-term comovement suggests that precious metal investors can leverage modern portfolio theory to diversify cryptocurrency volatility risk. These results offer valuable insights for investors seeking to optimize portfolios by strategically incorporating cryptocurrencies for improved risk-adjusted returns.
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.