By combining behavioral finance theory and big data analysis technology, this study explores the mechanism of the impact of investor sentiment on cryptocurrency market price anomalies. Based on the fusion database of traditional exchange historical market data and social media sentiment data, the research team constructed multidimensional sentiment indicators to quantify the emotional fluctuations of market participants. The research design adopts the strict data cleaning process, feature engineering processing, and the hybrid modeling method combining the traditional statistical model and the machine learning algorithm. The empirical results show that extreme optimism or pessimism is significantly associated with abnormal price events, and the predictive ability of the composite sentiment index is better than that of the single volatility index. This research reveals the transmission pathways of cognitive biases such as overconfidence and the anchoring effect in the cryptocurrency market, confirming the significant influence of irrational psychological factors on the price formation of digital assets. The findings not only deepen our understanding of the interaction mechanism between investor psychology and market behavior, but also provide an innovative analytical framework for risk management and quantitative investment strategies in the cryptocurrency field.
This study provides empirical evidence that cryptocurrency market movements are influenced by sentiment extracted from social media. Using a high frequency dataset covering four major cryptocurrencies (Bitcoin, Ether, Litecoin, and Ripple) from October 2017 to September 2021, we apply state-of-the-art natural language processing techniques on tweets from influential Twitter accounts. We classify sentiment into positive, negative, and neutral categories and analyze its effects on log returns, liquidity, and price jumps by examining market reactions around tweet occurrences. Our findings show that tweets significantly impact trading volume and liquidity: neutral sentiment tweets enhance liquidity consistently, negative sentiments prompt immediate volatility spikes, and positive sentiments exert a delayed yet lasting influence on the market. This highlights the critical role of social media sentiment in influencing intraday market dynamics and extends the research on sentiment-driven market efficiency.
Cryptocurrencies have rapidly emerged as a significant financial asset class, influencing global monetary systems and financial markets. However, their extreme volatility, speculative nature, and evolving regulatory landscape pose challenges to investors, policymakers, and financial analysts. This study presents an in-depth quantitative analysis of cryptocurrency volatility and risk assessment, focusing on Bitcoin (BTC-USD) and its correlation with traditional financial assets, including the EUR/USD exchange rate and S&P 500 index. Our research employs Generalized Autoregressive Conditional Heteroskedasticity (GARCH) modeling to measure the dynamic volatility patterns of Bitcoin, revealing the asset’s substantial fluctuations over time and its sensitivity to market shocks. Additionally, we utilize Monte Carlo simulations to forecast potential future price movements of Bitcoin, highlighting risk scenarios and the probability distribution of price trajectories over a one-year period. The Value-at-Risk (VaR) model is implemented to estimate potential losses within a given confidence interval, providing a robust measure of downside risk. Furthermore, the study examines the integration of cryptocurrency markets with traditional financial instruments by analyzing cross-asset correlations and volatility spillover effects. The findings suggest that while Bitcoin remains a highly volatile asset, its correlation with the broader financial system is increasing, indicating a potential shift towards mainstream financial adoption. The results contribute to the ongoing debate on whether cryptocurrencies serve primarily as speculative instruments or as viable components of diversified investment portfolios. These insights are valuable for institutional investors, risk managers, and policymakers in designing more effective risk mitigation strategies for cryptocurrency investments.
Klaus Grobys, James W. Kolari, Davide Sandretto, Syed Jawad Hussain Shahzad · 5 authors
Abstract This paper explores the tail behavior of cryptocurrency momentum strategies and the profitability of volatility-managed momentum portfolios. Our main results derived from using a sample of large-cap cryptocurrencies and equal-weighted momentum portfolios indicate that cryptocurrency momentum is subject to severe crashes. Even a single cryptocurrency can cause insignificant momentum portfolio returns. In line with the literature on volatility-managing equity portfolios, our findings suggest that volatility management is a useful tool for mitigating cryptocurrency momentum crashes. Further corroborative evidence suggests that cryptocurrency momentum appears to be a phenomenon associated with large-cap cryptocurrencies.
Abstract This paper examines the dependence, systemic risk spillover, return and volatility spillover, and portfolio implications across various timescales between the Green Bond (GB) and U.S. S&P 500 Stock (SP), Vanguard Total World Stock Index Fund (VT), Bitcoin (BTC), Ethereum (ETH), Ripple, OIL, and GOLD markets. The sample period is August 07, 2015–October 6, 2023, covering periods of instability during the COVID-19 pandemic and the Russia–Ukraine conflict. Using the wavelet–copula–conditional value-at-risk and wavelet-multivariate asymmetric-GARCH framework, our main results show that the systemic risk and return, volatility spillovers, and diversification opportunities are portfolio-specific and timescale-dependent. Specifically, there is a negative long-term correlation for the pairs GB-SP and GB-OIL, whereas the pair GB–GOLD pair is positively correlated in the short term. GB can mitigate the risk of other markets. In terms of the portfolio implications, GB weakly hedges BTC and ETH during normal and turbulent periods but has a strong ability to hedge VT in the short term and SP in the mid and long term. Regarding hedging effectiveness, the role of GB for GOLD and VT is noted.
This study presents an advanced adaptive trading framework that integrates Deep Reinforcement Learning (DRL) with the Iterative Model Combining Algorithm (IMCA) to overcome the critical limitations of static ensemble methods in global portfolio optimization. Using a diverse cross-market dataset of 39 stocks from the US, Australia, Europe, Thailand, and one cryptocurrency (BTC-USD), the research rigorously evaluates models’ adaptability under volatile market conditions. Volatile market conditions—such as COVID-19, SVB crisis, and the 2022 crypto crash—are captured via volatility metrics (e.g., drawdown), with DRL models like PPO/TD3 adapting through dynamic reward signals. This cross-asset integration is particularly critical, as it captures the complex dynamics and correlations between traditional financial markets and emerging digital assets. Although DRL models like PPO and TD3 outperform traditional strategies, they remain vulnerable to market drawdowns and high volatility. IMCA significantly surpasses these models, achieving the highest cumulative return of 29.52% and a superior Sharpe ratio of 0.829 by dynamically recalibrating model weights in response to real-time market dynamics. This study addresses a substantial research gap, highlighting the failure of traditional ensemble models—reliant on static weightings—to adapt to evolving financial conditions, resulting in suboptimal risk-adjusted returns. IMCA offers a dynamic, data-driven approach that continuously optimizes portfolio strategies across fluctuating market regimes, demonstrating its scalability and robustness across diverse asset classes and regional markets, and providing an empirical framework for adaptive portfolio management. Policy recommendations underscore the need for financial institutions to adopt AI-driven adaptive models like IMCA to enhance portfolio resilience, profitability, and responsiveness in uncertain markets.
Suleiman Dahir Mohamed, Mohd Tahir Ismail, Majid Khan Majahar Ali
Despite the introduction of several adjustments, mitigating data anomalies in financial datasets has proven challenging, particularly in the context of cryptocurrencies with extreme values and increased volatility. The progress in properly addressing these anomalies prior to testing remains restricted, highlighting the unique and complex nature of financial data in this domain. Thus, in this paper we propose a hybrid approach called the Win-IS strategy. It is meant to address the influence of extreme outliers in the tail and subsequently identify breaks, trend breaks and outliers in cryptocurrencies. This methodology uses the winsorization (Win) process to enhance the effectiveness of the indicator saturation (IS) approach. The study uses cryptocurrencies like Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Tether (USDT), and Ripple (XRP). The results of the research indicate that the winsorization strategy improved the detectability of the IS approach, with Win-IS outperforming the IS method in terms of the Bayesian Information Criterion. Furthermore, the Win-IS technique uncovered additional breaks, trend breaks and outliers that were previously unknown and repeated in some cases as detected by the IS strategy. The effect of winsorization is dependent on the chosen percentile and dataset attributes. Through detailed examination and comparison, the findings of this research contribute to the improvement of other detection approaches, providing a valuable perspective for researchers and practitioners in the field. Additionally, this hybrid approach can improve decision-making, risk management and model creation, benefiting investors, legislators and scholars.
Thomas Conlon, Diego Víctor de Mingo‐López, Andrew Urquhart
ABSTRACT Growth in cryptocurrency funds has followed the wider expansion of the cryptocurrency sector. In this paper, we study the performance persistence and market timing ability of cryptocurrency fund managers. We show that cryptocurrency funds produce remarkable levels of abnormal returns. Moreover, sorting by previous alpha provides compelling evidence of persistence in abnormal returns. Funds with previous excess abnormal returns have high ex post abnormal returns, while cryptocurrency factors explain only a small proportion of the variation in these returns. An ex post outperformance among funds displaying ex ante market timing skills is found, while these ex post abnormal returns can, in turn, be attributed to managerial timing abilities.
The paper demonstrates the nonsense of using Bitcoin in financial investments. By using mean-variance financial analysis, stochastic dominance, CVaR, and the Shapley value theory as analytical statistical models, I show how Bitcoin performs poorly by comparing it against other traded assets. The conclusion is reached by analyzing daily freely available market data for the period 2018–2023.
The research paper will focus on the influence of major cryptocurrencies, especially Bitcoin and Ethereum, on world financial markets and traditional financial systems. It looks at how, because of their decentralized nature, these digital assets have brought new dynamics to financial markets in the price of other assets, their volatility, and their means of investment. The research design is of a mixed-methods nature, combining quantitative data from financial market indices with qualitative insights from expert interviews. Some of the main lessons learnt are declining value with other financial assets, the interdependency between movements in crypto assets and other linked assets and disruptions in banking, payments and investment. Besides, there are regulation decisions that should consider the fluctuations of the market and security requirements, as well as the analysis of many initiatives in order to provide sufficient regulation frameworks on the international level. The concluding advice proposed how not only to accommodate the disturbance of current financial stability through innovations but also to integrate the utilization of cryptocurrencies.
Steve Springer Laryea, Kofi Agyarko Ababio, Jules Clément, Marno Booyens
This paper aims to investigate investors’ prospects in adding value to their portfolios by considering investors’ behavioural score (Cumulative Prospect Theory (CPT) score) and a clustering technique in the selection of assets. The universe of assets constitutes 63 cryptocurrencies sourced from Bloomberg from Jan 01, 2020, to July 31, 2022. The study period was segmented into two distinct and mutually exclusive periods, namely COVID-19, and post-COVID-19. Nine portfolios were constructed of which six were based on the CPT and the remaining on the K means Clustering technique. Using the copula-based Differential Evolution (DE) algorithm for the optimisation, the results show that portfolios consisting of assets with extremely high CPT scores were preferred during the post-COVID-19 and full sample periods, except for portfolios comprising assets with extremely low CPT scores during the COVID-19 period. The most optimised portfolio was composed of classified assets with extremely high CPT scores in the post-COVID-19 period. These findings provide intuitive and coherent investment strategies to guide investors in the cryptocurrency market.
Within the framework of high-frequency volatility modeling, this study investigates the realized volatility spillover dynamics across major cryptocurrencies over an extended period of time. Using a Time-Varying Parameter Vector Autoregression (TVP-VAR) model of the realized volatility (RV), this work constructs the Total Connectedness Index (TCI) and Pairwise Connectedness Index (PCI) to measure the intensity and direction of realized volatility transmission within this digital asset network. Our findings reveal a consistently high level of spillovers among these leading cryptocurrencies, with notable peaks during periods of global market turbulence. Notably, Ethereum emerges as the most influential volatility transmitter, challenging the traditional view of Bitcoin as a primary driver of volatility spillovers. This reflects Ethereum’s pivotal role in decentralized finance (DeFi), decentralized applications (dApps), and its growing trading activity, suggesting a shifting influence in the increasingly diversified cryptocurrency ecosystem.
Non-fungible tokens (NFTs) have gained mainstream attention in the fintech community, but there is little research on their statistical properties. This study investigates the long-memory characteristics of NFT returns and volatility, focusing on their potential for predicting price movements. As NFTs do not conform to traditional models, understanding their unique features is crucial for comprehending complex market dynamics. This study aims to reveal the impact of macroeconomic factors on NFT prices, understand their correlation and develop predictive models using autoregression and artificial intelligence (AI) technology. This research utilized datasets from the Centers for Disease Control and Prevention (CDC), U.S. Bureau of Labor Statistics, Bureau of Economic Analysis, Christie’s, Dune, and Google Trends. Correlation and p value tests revealed strong relationships between NFT prices and variables such as weekly volume, pandemics, inflation and security. The Baseline Model using autoregression with NFT volume, security and technology factors outperformed all other models demonstrating the speculative volatility of NFTs. The Transformer Model using transformers, an architecture used by ChatGPT, Gemini and Stable Diffusion, showed high accuracy with less feature selection and preprocessing efforts. This study provides a novelty using a systematic approach for researchers to perform financial forecasting and contributes to the scarce literature on NFTs. This research offers valuable insights to investors and private agents regarding the right economic conditions for NFT investments by reducing portfolio risks and making informed decisions. To the authors’ best knowledge, this is the first study to utilize time-series transformers for forecasting NFTs based on macroeconomic factors.
This study is the first to scientifically investigate stock indices and currency exchanges that affect crypto price volatility pre and post the FTX (Future Exchanges) collapse event. Weekly series from 1 January 2020 to 31 December 2024 were utilized for the analysis. The ARDL model suggests positive symmetric short- and long-term effects of USA stock indices on Bitcoin and Ethereum prices (p < 0.10), while Japanese stock indices and currency exchanges have negative symmetric short- and long-term effects on Bitcoin and Ethereum price volatility (p < 0.10). The global index MSCI has no symmetric effect. The asymmetric approach NARDL suggests positive and negative asymmetric short- and long-term effects of USA and Japanese stock indices and currency exchanges on Bitcoin and Ethereum price volatility (p < 0.05). This research helps exchange brokers and crypto traders diversify their holdings, reduce stock index and currency exchange risk, and accurately predict Bitcoin and Ethereum price variations.
We investigate the evolving relationships between cryptocurrencies and equity portfolios and find that Bitcoin’s contributions to the active risks of equity portfolios have grown over time, exceeding 10% in defensive strategies. This underscores the increasing importance of investment professionals quantifying and managing crypto-related risk exposures in their portfolios, a task for which we provide guidance. For risk measurement, we use intraday returns to significantly improve the forecast accuracy of equity portfolio sensitivities to cryptocurrency risks. For risk management, we advocate direct hedging for optimal risk reduction and suggest using stock selection constraints as an alternative approach to limit the influence of cryptocurrencies on portfolio risk exposures.
Has the mean-variance framework become obsolete? In this paper, we replace traditional variance–covariance methods of portfolio optimisation with relative Tsallis entropy and mutual information measures. Its goal is to enhance risk management and diversification in complicated finance ecosystems. We utilize the S&P 500 and Bitwise 10 cryptocurrency indices’ daily returns (2019–2024 data) and conduct our analysis to the year 2020 under extreme shocks. Many models were trained with different configurations, like mean-variance (MV), mean-entropy (ME), and mean-mutual information (MI) traders and their corresponding variants, using Sharpe’s ratio, Jensen’s alpha, and entropy value of risk (EVAR). The findings indicate that entropic models outperform conventional models in terms of diversification and, especially, extreme risk management. Because the appropriate normalization conditions often fail to be satisfied, we can informally see that after a recalibration of the effective frontier, we obtain from EVAR an accumulated resilience aspect to these rare events while also observing the great potential of entropy-based models to replicate non-linear dependencies between assets. The results show that models combining entropy and mutual information optimise the gain–loss ratio (GLR), providing stable diversification and improved risk management, while maximising returns in complex and volatile market environments.
This study aims to develop a dynamic portfolio trading system for high-risk profiles of cryptocurrencies in two phases: 1) portfolio selection and 2) portfolio construction. In the first phase, we propose a novel algorithmic trading model applying a Convolutional Neural Network (CNN) using a 2-D convolution layer with eight kernels of 3×3 sizes based on the prediction of selected technical indicators to predict buy/sell trading signals. To effectively increase the accuracy of the CNN model, first, the H-step ahead predictions of the selected technical indicators based on Long-short-term-memory (LSTM) along with the indicators themselves have been used to construct input matrices of the CNN model. A new price labeling approach was proposed to determine buying or selling points using the zigzag indicator (ZZ) in our CNN model. Assets with buy signals have been selected to construct the proposed portfolio. In the second phase, we propose a novel robust approach based on Holt-Winters-Multiplicative (HWM) to determine the realized crypto portfolio weights robustly by considering the seasonal effects. The experimental results show that our developed system outperforms the competing models for 30 cryptocurrencies with a high-risk profile in the two phases.
Bu çalışma Bitcoin getirileri ile kripto para piyasalarındaki yatırımcı duyarlılığını temsil eden Kripto Korku ve Açgözlülük Endeksi arasındaki kısa ve uzun dönemli ilişkiyi ve bu ilişkinin yönünü ve şiddetini araştırmaktadır. Çalışmada 01.02.2018-07.09.2022 dönemine ait günlük veri setleri A-ARDL (Augmented Autoregressive Distributed Lag) yöntemi ile analize tabi tutulmuştur. Finansal stres ve VIX Korku endekslerinin de kontrol değişkenler olarak kullanıldığı çalışmada yatırımcı duyarlılığının Bitcoin getirilerini kısa ve uzun dönemde pozitif ve önemli seviyede etkilediği bulgusu elde edilmiştir. Buna göre açgözlülük (korku) duygusundaki artışın Bitcoin getirilerini pozitif (negatif) etkilediği belirlenmiştir. Elde edilen bu bulgunun davranışsal finans ve yatırımcı duyarlılığı teorileriyle uyumlu olduğu ifade edilebilmektedir.
The rapid growth and increasing adoption of cryptocurrencies have reshaped the investment landscape, presenting unique opportunities and challenges for investors. This study examines how advisory information sources influence cryptocurrency investment behaviors and intentions among U.S. investors. Using data from the 2021 National Financial Capability Study, it explores how reliance on financial professionals, media, and social networks shapes investment decisions. The motivation for this research lies in the need to understand the divergent roles of these sources in an era where traditional and emerging financial advice coexist. Findings reveal that reliance on financial advisors correlates with reduced cryptocurrency investment and future investment intentions, reflecting advisors’ cautious stance toward volatile assets. Conversely, reliance on media and social networks significantly increases both current investments and future intentions. The findings also highlight that investor confidence is positively associated with the likelihood and intentions to invest in cryptocurrency. Conversely, heightened risk perceptions associated with cryptocurrency reduce both the likelihood and intentions to invest. The study calls for financial professionals to enhance client education on cryptocurrency risks and for policymakers to strengthen regulations, ensuring accurate information dissemination through media and social networks. By providing a nuanced understanding of advisory influence and investors’ characteristics, this research offers valuable insights for financial professionals, policymakers, and investors navigating the complexities of cryptocurrency investments.
Mohammad Abdullah, Mohammad Ashraful Ferdous Chowdhury, G. M. Wali Ullah
This study inspects the asymmetric tail risk dynamics, efficiency, and interconnectedness among FinTech stocks, cryptocurrencies, and traditional assets. Firstly, we employ the Multifractal-Asymmetric Detrended Cross-Correlation Analysis to examine the cross-correlation patterns and efficiency dynamics of the analyzed assets. The findings reveal asymmetries in cross-correlations and the presence of multifractality, highlighting the nonlinear relationships among these assets and find FinTech assets are the most efficient. Secondly, we utilize the time domain quantile connectedness method to investigate tail risk connectedness, offering insights into the network's shock transmission and spillover effects. Our analysis identifies the major risk transmitters (FinTech stocks) and receivers (bond), emphasizing the interconnectedness of the assets. Additionally, the study conducts bivariate portfolio analysis, considering short and long investment horizons, to guide asset allocation and hedging strategies. Our findings have significant implications for facilitating informed investment strategies and improving the stability and resilience of financial markets.
This study is the first to scientifically investigate stock indexes and currency exchanges that affect crypto prices. The purpose is to distinguish between the USA-Japan stock markets and the currency market&#039;s short- and long-term effects on bitcoin and ethereum. Auto Regressive Distributed Lag (ARDL) is used to analyze weekly series from 1-1-2016 to 20-10-2024. An asymmetric error-checking framework employing non-linear ARDL statistical approach to study variables affecting bitcoin and ethereum prices. Bitcoin appear to have short- and long-term linear effects on the US-Japan stock markets. Euro, GBP, and USA-Japan stock markets exhibit short-term linear effects with ethereum. Ethereum linearly affects GBP. This research helps exchange brokers and crypto traders diversify their holdings, reduce stock index and currency exchange risk, and accurately predict bitcoin and ethereum price variations.
Abstract Investing in cryptocurrencies is progressively becoming a norm; however, these assets are excessively volatile and often decrease or increase in value instantly. Thus, rational investors holding cryptocurrencies for extended periods firmly search for assets that can diversify their risk, preferably with assets other than cryptocurrencies. In this study, we consider the two most studied cryptocurrencies with the highest capitalization and trading volume/value, namely Bitcoin and Ethereum. Specifically, we examine whether high-performing leading US tech stocks (Facebook, Amazon, Apple, Netflix, Google [FAANG]) can provide any diversification benefits to cryptocurrency investors. To do so, we employ dynamic conditional correlation (DCC), asymmetric DCC, time-varying parameter vector autoregression-based connectedness measures, dynamic correlation-based hedge and safe-haven regression analyses, portfolio optimization and hedging strategies, time- and frequency-based wavelet coherence, and high-frequency 10-min intraday data from January 1, 2018 to January 31, 2023. We find that FAANG stocks can be considered (at least weak) safe havens for Bitcoin and Ethereum during the sample period. Our subperiod analyses reveal that the safe-haven role of FAANG stocks, specifically for Bitcoin, has noticeably increased. While the safe-haven property of Facebook is the most promising, for Netflix it is blurred between a weak–safe-haven and a hedge. Our findings may help investors, policymakers, and academicians to invest in cryptocurrencies, formulate relevant investment guidelines, and extend the literature on cryptocurrencies, respectively.
Abstract We present the first evidence of investor‐trading‐based disagreement's influence on cross‐sectional cryptocurrency daily returns. We interpret abnormal trading volume as investor disagreement and find evidence in support of Miller's disagreement model: when short‐sale constraints are binding, high abnormal volume (high disagreement) assets experience lower future returns. Further supporting Miller, these same conditions associate with higher contemporaneous order imbalance, and ex post decreases in both buying and selling activities, with the former exceeding the latter in magnitude. By contrast, the effect of high disagreement disappears after a coin's margin trading is activated. We conclude that price‐optimism models explain the disagreement‐returns relationship when opinion divergence is likely the dominant determinant of returns.
Abstract The asymmetries of factors influencing the return of cryptocurrencies have already been well documented; however, in the case of NFTs, only information asymmetries and hedging properties related to asymmetries were studied. Therefore, the present study examines factors affecting NFT returns, from market-related factors (crypto-market index return and stock market index return) to the Amihud illiquidity ratio and Google search trends during different market conditions. The wavelet coherences-based methodology was applied separately during the boom, bust, normal, and turbulent periods identified by structural breakpoints. Based on 14 NFT projects between April 2019 and July 2022, results show two fundamental asymmetries influencing these NFT returns. First, there is an asymmetry in the behavior of the factors in different periods; second, there is an asymmetry in how illiquidity manifests itself over NFTs that do or do not possess cash flow-generating potential.