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2,329 results · page 13 of 98
Chao Feng, Shiqun Ma, Lijin Xiang, Zumian Xiao
No abstract is available for this record.
Faizah Alsulami, Ali Raza
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.
Élise Alfieri, Radu Burlacu, Geoffroy Enjolras
Purpose This paper examines the relationship between the degree of information asymmetry among investors and the occurrence of bubbles in cryptocurrency markets. Design/methodology/approach The study applies the Philipps, Shi and Yu (PSY) methodology to identify bubbles in 74 cryptocurrencies from July 2014 to April 2021. Findings The findings indicate that there is a negative relationship between the degree of information asymmetry among investors and the number and duration of bubbles across cryptocurrencies. Originality/value This finding supports the riding-bubble argument of Asako et al. (2020), which suggests that when the information asymmetry among investors is high, rational investors are less certain about what irrational, inexperienced investors might decide. This strategic uncertainty leads rational investors to close out their positions more quickly, resulting in a shorter duration of the bubble and a reduced propensity for new bubbles to emerge. The study’s findings hold regardless of the proxies used to measure information asymmetry and noise trading, cryptocurrency characteristics and regression model specifications.
Minhao Leong, Vitali Alexeev, Simon Kwok
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.
Sana Gaied Chortane, Kamel Naoui
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.
Dehua Shen, Yiheng Wu
No abstract is available for this record.
H.-Q. Bui, Chrıstophe Schınckus, Hamdan Amer Ali Al-Jaifi
No abstract is available for this record.
Ehsan Mohammadian Amiri, Akbar Esfahanipour
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.
Kübra Saka Ilgın
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.
Jia Qi, Yu Zhang, Congrong Ouyang
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.
Dailin Song
Accurately forecasting price swings is nowadays essential to investors looking to maximize their portfolios as the cryptocurrency markets continue to develop and fluctuate rapidly. The intricate, non-linear patterns in these markets are sometimes difficult for traditional financial models to depict. In response, this paper presents two machine learning techniques for predicting bitcoin prices: Extreme Gradient Boosting and Long Short-Term Memory. The study first evaluates how well these models forecast Bitcoin prices, assessing their accuracy with measures like Mean Absolute Error and Root Mean Squared Error. Four significant cryptocurrencies are then predicted by LSTM. In order to allocate assets in a way that optimizes returns while reducing risk, the forecasted prices are then incorporated into portfolio optimization algorithms utilizing Monte Carlo simulation and the efficient frontier. The results of the study show how machine learning approaches may be used to improve investing strategies through optimal portfolio allocation, in addition to projecting cryptocurrency values.
Faizah Alsulami, Ali Raza
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.
Fahad Ali, Muhammad Usman Khurram, Ahmet Şensoy
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.
Alparslan Sari, Mehmet A Gavcar, Safak Aplay, Adnan Özsoy
This study rigorously investigates the application and effectiveness of automated trading bots in cryptocurrency markets, with a particular focus on the deployment and performance of key strategies such as Mean Reversion, Arbitrage, and Grid Trading. Leveraging the CCXT library to access real-time market data from a variety of cryptocurrency exchanges, this research aims to analyze the operational dynamics and strategic efficacy of these bots under different market conditions. Through detailed simulations and comprehensive data analysis, the study evaluates the bots' ability to adapt to and capitalize on market anomalies and fluctuations. The findings are expected to provide valuable insights into the potential and limitations of each strategy, contributing to the advancement of trading bot technology and the optimization of trading strategies.
Jon A. Garfinkel, Lawrence Hsiao, Danqi Hu
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.
Botond Benedek, Bálint Zsolt Nagy
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.
Anna Ignatenko, Larysa Dokiienko
No abstract is available for this record.
Massimo Guidolin, Serena Ionta
No abstract is available for this record.
Nadiah Ruza, Saiful Izzuan Hussain, Nurulkamal Masseran
This study employed Extreme Value Theory (EVT) to identify high-risk investment opportunities in the volatile crptocuurency market. EVT provides a more accurate risk assessment than traditional methods as it focuses on the tail distribution. The daily outcomes of six major cryptocurrencies were used for the analysis (Bitcoin, Ethereum, Ethereum Classic, Litecoin, Monero and Ripple). The time frame extends from January 2017 to December 2019 and includes major changes. Returns are fitted to the generalized Pareto distribution (GPD) in conjunction with the extreme value distribution. The results show that Bitcoin has a relatively low downside risk compared to other cryptocurrencies. Ethereum and Litecoin have more stable return patterns, suggesting a safer profile, while Ripple and Monero have the highest tail risk. These findings are consistent with other studies looking at the diversification and safe-haven properties of certain cryptocurrencies and highlight the importance of Extreme Value Theory (EVT) in evaluating extreme negative risk. The study is highly relevant for investors, portfolio managers and regulators to minimize volatility and reduce systemic risk in digital asset markets.
Abdul Khaliq Aamir
No abstract is available for this record.
Wilder, Gregory J.
M.S.
Joon Chae, Kyounghun Bae, Hyoung‐Goo Kang, Bonha Koo
We examine how investor emotions and Bitcoin price influence each other using intraday data and textual analysis. We extract emotions from a popular online chatting window at one of the largest cryptocurrency exchanges in Korea. To control for global factors, we analyse relative Bitcoin prices and the differences between the Korean exchange and other global prices. The identified emotions predict the return and volatility of Bitcoin price one hour ahead. The results are economically significant: simple arbitrage trading strategies using the relationship between emotions and Bitcoin prices generate profits. Consequently, investor emotions drive Bitcoin prices, suggesting irrational crypto-markets that rational speculators may exploit.
Alistair Milne
No abstract is available for this record.