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3,636 results · page 50 of 152
Alexander I. Iliev, Malvika Panwar
No abstract is available for this record.
Yosef Bonaparte
This paper demonstrates that the Millennial generation exhibits unique personal traits that have implications for their portfolio choice and, hence, for the stock market. Specifically, Millennials display greater propensity to participate in the stock market, exhibit more confidence (as they trade more frequently), and more diversification (invest in greater number of stocks and foreign assets). At the macro level, we find that the Millennials influence the stock market to behave differently surrounding holidays, and the statistical significance of key financial anomalies is disrupted. Despite the Millennials’ proficiency in using internet, they utilize more social methods (friends/relatives) when they invest than previous generations. Collectively, we infer that the financial market is not only exposed to business cycles, but also to generation cycles.
Mateusz Skwarek
Abstract Despite recent studies focused on comparing the dynamics of market efficiency between Bitcoin and other traditional assets, there is a lack of knowledge about whether Bitcoin and emerging markets efficiency behave similarly. This paper aims to compare the market efficiency dynamics between Bitcoin and the emerging stock markets. In particular, this study indicates whether the dynamics of Bitcoin market efficiency mimic those of emerging stock markets. Thus, the paper's contribution emerges from the combination of Bitcoin and emerging markets in the field of dynamics of market efficiency. The dynamics of market efficiency are measured using the Hurst exponent in the rolling window. The study uses daily data for the MSCI Emerging Markets Index and the Bitcoin market over the period 2011–2022. Our results show that there is at most a moderate correlation between the dynamics of Bitcoin and emerging stock markets’ efficiency over the entire study period. The strongest correlations occur mainly in periods of high economic policy uncertainty in the largest Bitcoin mining countries. Therefore, the association between Bitcoin market efficiency and emerging stock markets’ efficiency may strengthen with an increase in economic policy uncertainty. These findings may be useful for investors and portfolio managers in constructing better investment strategies.
Yeguang Chi, Wenyan Hao, Qionghua Chu
No abstract is available for this record.
Chikashi Tsuji
This paper investigates the profitability of Bitcoin and US equity. More concretely, we inspect the performances of the S&P 500 index and Bitcoin by comparing their returns and volatilities. As a result, we obtain the following significant findings. First, our regression analysis clarifies that for the period after the sudden appearance of COVID-19, there was a weak nexus between the S&P 500 index and Bitcoin returns. In addition, our return and return spread analysis evidences that for this period, on average, Bitcoin returns were much higher than the S&P 500 index returns. Moreover, our volatility and volatility spread analysis reveals that for this period, on average, the volatilities of Bitcoin returns were much higher than those of the S&P 500 index returns.
Haoyuan Ma
Price forecasting is pretty crucial in the asset management and allocation and quantitative trading industries. With the development of the global economic situation, decentralized finance has gradually entered people's field of vision, and cryptocurrency and cryptocurrency finance have become the r
Aderonke Tosin-Amos
Virtual assets and currency sector are becoming increasingly intertwined.According to new IMF research, the correlation of crypto assets with traditional holdings like equities has increased dramatically as usage has grown, limiting their risk perception investment opportunities, and raising the danger of spillover across financial markets.Theoretical and empirical findings concerning cryptocurrencies and stock market behaviour have been misleading thereby putting policy makers at a crossroads.This paper therefore examines the response of stock market to investment in cryptocurrencies in the US stock market.Monthly data covering the period between February 2016 to February 2022 was used.The answer was achieved using novel dynamic autoregressive-distributed lag (ARDL) simulation techniques along with the Breitung and Candelon causality test.Findings revealed that cryptocurrencies impacted positively on the US stock market.Secondly, investment in Bitcoin and Ethereum is a good predictor of stock market while no evidence of causality between investment in ripple and stock market indices in the US stock market.Thirdly, a long-run relationship exists between investment in cryptocurrencies and behaviour of stock market indices in the United State, and that investment in cryptocurrencies has a significant long-run increasing effect on stock prices in United State.
Nicolae Florian Spătaru, Ștefan Ionescu
No abstract is available for this record.
Iskandar Muda, Jaymin Shah, Jarudin Jarudin, Gioia Arnone · 6 authors
As the number of people infected with COVID-19 continued to rise, many nations placed their entire nations under a complete lockdown. As a direct consequence of this, the entire world is currently experiencing a catastrophic financial crisis. As a result of the pandemic, unemployment rates have increased across a number of different sectors, which is having a significant negative effect on international trade. During this challenging period, Artificial Intelligence (AI) is altering the way businesses examine the statistics pertaining to their cryptocurrency holdings. Utilizing artificial intelligence (AI) in the realm of business can result in a variety of positive outcomes. The technological effects of AI make our day-To-day lives simpler because they eliminate the need for human intervention in many situations. It would be helpful to have a better understanding of artificial intelligence and the methods it uses, such as the classifier model, in the event that there was a pandemic. If people have access to real-Time data analysis and predictions that have been generated by AI and big data, they will be able to make better decisions. In anticipation of the arrival of a new world, the company, along with SMEs and start-ups, is stepping up its efforts to enhance the management of virtual businesses by establishing a presence on multiple e-Trade systems. Artificial intelligence (AI) has emerged as a key player in the quest to find effective solutions to issues that arise in the workplace. AI is being applied to improve business operations in many different areas, including marketing, fraud detection, algorithmic trading, customer service, portfolio management, and product recommendations based on what customers want. These are just some of the many problems that are being solved by AI. In addition, technological advancements could be made in order to enhance the functionality of the suggested guidelines and achieve the most precise result possible in light of the current value of cryptocurrencies.
El-Sayed M. El-Kenawy M. El-Kenawy
The rapid evolution of cryptocurrencies has brought transformative changes to the financial landscape. Cryptocurrency prices, characterized by their inherent volatility, pose challenges for precise forecasting. This study introduces a novel approach to cryptocurrency price forecasting, leveraging Long Short-Term Memory (LSTM) networks, known for discerning temporal dependencies within time series data. Motivated to enhance prediction accuracy, this research investigates the effectiveness of LSTM networks in capturing complexities inherent in cryptocurrency price movements. The proposed methodology involves meticulous data collection and preprocessing, utilizing an extensive dataset from Kaggle. This dataset forms the foundation for predictive modeling and facilitates an in-depth analysis of cryptocurrency price dynamics. Exploratory data analysis, including visualization techniques, and a dedicated Time Series Analysis precede the implementation of predictive models, such as LSTM networks. Results and evaluation showcase promising outcomes, emphasizing the models' precision, accuracy, and explanatory power. The Mean Absolute Error (MAE) of 0.0177 underscores the precision achieved in predicting cryptocurrency prices, while the Mean Squared Error (MSE) of 0.00066 and the R² Score of 0.9486 attest to our models' overall accuracy and explanatory power. This research significantly contributes to understanding cryptocurrency forecasting by incorporating LSTM networks, paving the way for advancements in this evolving domain.
Jędrzej Rudkiewicz, Marcin Hernes
The purpose of the research is to study the cryptocurrency data listed on Binance, and design a profitable strategy based on the findings. The data covers over 150 selected cryptocurrencies. The study aims to detect anomalies in the volume and number of transactions and apply an investment strategy based on deviations and sudden price fluctuations. An autoencoder and LSTM-based neural network have been used. Based on the results of the present research, it can be concluded that the model successfully identified anomalies in the data regarding the volume and number of transactions carried out. I it was also observed that price volatility in the period close to the detected anomaly was significantly higher than average volatility for the sample.
Dima Alberg, Elroi Hadad
No abstract is available for this record.
Srđan Radulović
Bitcoin was presented at the end of 2008 but the question still remains whether it is a form of money or something entirely different. Bitcoin was not designed with the aim to create money in a strict sense but primarily with the intention to make the transfer of value as effective as possible. Yet, Bitcoin has a capacity to take on the role of money, and that capacity was recognized in court cases. In this regard, the paper presents the results of the primarily empirical but also theoretical research conducted previously on the volatile but still very deflationary nature of Bitcoin and its effect on monetary obligations. The idea that cryptocurrencies can be also used as a hedging instrument to prevent the negative effects of domestic currency depreciation might be controversial for a number of reasons, one of which is certainly the volatile nature of bitcoin "price". We stress that periodic depreciation of its value does not mean that bitcoin is inflationary. On the contrary, bitcoin is deflationary by nature, which is evident in different in-built mechanisms and new ways of application. In this paper, the author uses different analytical method techniques to single out and describe various deflatory mechanisms, both preprogramed and factual ones. The author also applies the synthetical method and its techniques, primarily abstraction and generalization, to sum up the data confirming the main hypothesis that bitcoin is by nature deflationary despite its volatility and, therefore, it can be used as a hedging mechanism.
Kirill Shilov, Andrey Zubarev
No abstract is available for this record.
Ta-Cheng Chang, Wei-Ying Nie, Hsuan-Ling Chang, Kuang‐Chieh Yen
We examine how economic policy uncertainty (EPU) influences realized variance dependency and tail-risk synchronization across major cryptocurrencies. Using 5-min high-frequency returns to construct realized variance and signed jump variance measures, we document that global and Western EPU (the US, UK, France) significantly strengthen both variance dependency (VD) and signed jump variance dependency (SJVD) among the top 15 cryptocurrencies, whereas Asian EPUs exhibit weaker and less consistent effects. The sensitivity of SJVD is particularly pronounced, reflecting the asymmetric transmission of tail risk during uncertainty shocks. These findings remain robust after controlling for Bitcoin’s realized volatility and hold in post-COVID subsample analysis. Our results suggest that cryptocurrency markets exhibit greater systemic interconnectedness and heightened tail-risk co-movements during periods of elevated policy uncertainty, with important implications for risk management and financial stability monitoring.
Hao Fan, Yahua Xu, Elie Bouri, Pingping Zeng
No abstract is available for this record.
Jingli Ren, Haiyan Wang
No abstract is available for this record.
Jan Šíla, Evžen Kočenda, Ladislav Krištoufek, Jiří Kukačka
No abstract is available for this record.
Jan Prüser
Abstract The high returns of cryptocurrencies have attracted many investors in recent years. At the same time the evolution of cryptocurrencies is characterized by extreme volatility. For investors, it is therefore key to gauge the risks related to an investment in cryptocurrencies. We provide a comparison of several GARCH and stochastic volatility models for forecasting the risk of cryptocurrencies over the out-of-sample period from 28.09.2018 to 28.02.2023. It turns out that the widely used GARCH(1,1) does not provide accurate risk predictions. In contrast, adding t -distributed innovations or allowing for regime changes improves the accuracy in both model classes. Finally, we consider a Bayesian decision-guided approach with discount learning to combine the different models and provide robust evidence that combining the model predictions leads to accurate combined risk predictions.
Riadh Benammar, Adel Boubaker, Anas Elmelki
This paper uses the wavelet coherence approach and the wavelet-based Granger causality test, to investigate the effect of the five waves of the COVID-19 pandemic on Bitcoin, Ethereum, BNB, Cardano, Ripple, Dogecoin, TRON, Litecoin, Stellar, and Bitcoin Cash in a time-frequency framework from 22 January 2020 to 22 February 2022. The results show the presence of correlation between the COVID-19 pandemic and cryptocurrencies in the short-medium term, and a positive impact on Bitcoin only during the first wave of the pandemic in the medium term. However, Cardano failed to act as a risk diversifier. In the long-term, our analysis shows that Ethereum, BNB, Ripple, Dogecoin, TRON, Litecoin, Stellar, and Bitcoin Cash proved their ability as strong safe haven assets, even during different periods of the COVID-19 crisis. Our results can provide helpful information for policymakers, and cryptocurrency market main and hedge funds managers during periods of uncertainty.
Leyi Zhang
Quantitative trading plays a pivotal role in financial markets. Over the past decade, quantitative trading has made remarkable improvements. Due to instability and nonlinearity in financial markets, it is still challenging to formulate high-return trading strategies to address the problem of long-t
Ke Xu, Yu‐Lun Chen, Bo Liu, Jian Chen
Abstract Price discovery studies of a single asset traded in multiple markets have traditionally focused on assessing the relative price discovery contribution of each market. However, in this paper, we demonstrate that the overall price discovery across all markets can undergo changes even when the relative price discovery of each market remains constant. We propose that this overall change in price discovery can be effectively captured by the fractional parameter in the fractionally cointegrated vector autoregressive (FCVAR) model. In contrast, the widely used cointegrated vector autoregressive (CVAR) model fails to account for this dynamic in overall price discovery. Through a combination of simulation exercises and empirical applications, we show that the FCVAR approach outperforms the CVAR model not only in evaluating the relative price discovery contributions but also, more importantly, in providing a comprehensive measurement of overall price discovery.
Juan Ignacio Ibañez
No abstract is available for this record.
Jasmeen Kaur Chahal, N. K. Bhatia, Gurpreet Singh, Vidhyotma Gandhi · 5 authors
No abstract is available for this record.