|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Discovering interlinkages between major cryptocurrencies using high‑frequency data: new evidence from COVID‑19 pandemic显示文摘Through the application of the VAR-AGARCH model to intra-day data for three cryptocurrencies(Bitcoin,Ethereum,and Litecoin),this study examines the return and volatility spillover between these cryptocurrencies during the pre-COVID-19 period and the COVID-19 period.We also estimate the optimal weights,hedge ratios,and hedging effectiveness during both sample periods.We find that the return spillovers vary across the two periods for the Bitcoin–Ethereum,Bitcoin–Litecoin,and Ethereum–Litecoin pairs.However,the volatility transmissions are found to be different during the two sample periods for the Bitcoin–Ethereum and Bitcoin–Litecoin pairs.The constant conditional correlations between all pairs of cryptocurrencies are observed to be higher during the COVID-19 period compared to the pre-COVID-19 period.Based on optimal weights,investors are advised to decrease their investments(a)in Bitcoin for the portfolios of Bitcoin/Ethereum and Bitcoin/Litecoin and(b)in Ethereum for the portfolios of Ethereum/Litecoin during the COVID-19 period.All hedge ratios are found to be higher during the COVID-19 period,implying a higher hedging cost compared to the pre-COVID-19 period.Last,the hedging effectiveness is higher during the COVID-19 period compared to the pre-COVID-19 period.Overall,these findings provide useful information to portfolio managers and policymakers regarding portfolio diversification,hedging,forecasting,and risk management. | Imran Yousaf Shoaib Ali | 2020 | Financial Innovation2020，6，1： | 4 |
| 2 | Effect of family control on corporate dividend policy of firms in Pakistan显示文摘This study examines the impact of family control on the dividend policy of firms in Pakistan,covering the period from 2009 to 2016.It also investigates whether family control moderates the impact of firm-specific factors on the dividend policy.The GMM model for panel data estimation is used.The mean difference univariate analysis shows that family firms differ from nonfamily firms based on financial characteristics.The multivariate analysis shows that family firms pay lower dividends than nonfamily firms.Besides,firm size inversely affects the dividend policy,whereas tangibility positively affects it.Moreover,family control does not moderate the impact of all firm-specific factors on the dividend policy.Overall,family control,size,and tangibility are found to be the main determinants of the dividend policy in Pakistan. | Imran Yousaf Shoaib Ali Arshad Hassan | 2019 | Financial Innovation2019，5，1： | 2 |
| 3 | Herding behavior in Ramadan and financial crises: the case of the Pakistani stock market显示文摘This study examines herding behavior in the Pakistani Stock Market under different market conditions,focusing on the Ramadan effect and Crisis period by using data from 2004 to 2014.Two regression models of Christie and Huang(Financ Analysts J 51:31-37,1995)and Chang et al.,(J Bank Finance 24:1651-1679,2000)are used for herding estimations.Results based on daily stock data reveal that there is an absence of herding behavior during rising(up)and falling(down)market as well as during high and low volatility in market.While herding behavior is detected during low trading volume days.Yearly analysis shows that herding existed during 2005,2006 and 2007,while it is not evident during rest of the period.However,herding behavior is not detected during Ramadan.Furthermore,during financial crisis of 2007-08,Pakistani Stock Market exhibits herding behavior due to higher uncertainty and information asymmetry. | Imran Yousaf Shoaib Ali Syed Zulfiqar Ali Shah | 2018 | Financial Innovation2018，4，1： | 2 |
| 4 | Novel LaFe_(2)O_(4)spinel structure with a large oxygen reduction response towards protonic ceramic fuel cell cathode显示文摘Highly active and stable electrocatalysts are mandatory for developing high-performance and longlasting fuel cells.The current study demonstrates a high oxygen reduction reaction(ORR)electrocatalytic activity of a novel spinel-structured LaFe_(2)O_(4)via a self-doping strategy.The LaFe_(2)O_(4)demonstrates excellent ORR activity in a protonic ceramic fuel cell(PCFC)at temperature range of 350-500℃.The high ORR activity of LaFe_(2)O_(4)is mainly attributed to the facile release of oxide and proton ions,and improved synergistic incorporation abilities associated with interplay of multivalent Fe^(3+)/Fe^(2+)and La^(3+)ions.Using LaFe_(2)O_(4)as cathode over proton conducting BaZr_(0.4)Ce_(0.4)Y_(0.2)O_(3)(BZCY)electrolyte,the fuel cell has delivered a high-power density of 806 mW/cm^(2)operating at 500℃.Different spectroscopic and calculations methods such as UV-visible,Raman,X-ray photoelectron spectroscopy and density functional theory(DFT)calculations were performed to screen the potential application of LaFe_(2)O_(4)as cathode.This study would help in developing functional cobalt-free ORR electrocatalysts for low temperature-PCFCs(LT-PCFCs)and solid oxide fuel cells(SOFCs)applications. | Jinping Wang Yuzheng Lu Naveed Mushtaq M.A.K Yousaf Shah Sajid Rauf Peter D.Lund Muhammad Imran Asghar | 2023 | Journal of Rare Earths2023，41，3： | 1 |
| 5 | 二元金属硫族化合物在能源储存体系中的作用:挑战和可能的解决策略显示文摘二元金属硫族化合物由于其丰富的相界面、高活性位点、优异的导电性以及快速的电化学动力学,与单金属硫族化合物相比具有更加优异的电化学性能.然而,材料的本征离子电导率低,在充放电过程中结构发生反复的团聚与断裂,体积膨胀大等因素导致其电化学性能衰减严重.为了解决上述问题,诸多策略被提出并用于调控二元金属硫族化合物的纳米结构以获得优化的电极材料.但是目前依旧缺乏对二元金属硫族化合物电化学过程的系统认识,也因此限制了其大规模的商业化应用.在这篇综述中,我们不仅重点介绍了基于二元金属硫族化合物电极材料制备的最新研究进展,还通过解释二元金属硫族化合物的电化学动力学,进一步构建和理解了这类材料的构效关系.此外,我们讨论了通过纳米结构化和使用碳材料与三维模板与之形成复合材料的策略来调控和修饰二元金属硫族化合物,并将详细讨论二元金属硫族化合物在超级电容器、金属离子电池、金属空气电池以及碱金属电池等电化学能源储存体系中的工作机理.最后,我们总结了二元金属硫族化合物在发展实际能源器件的过程中面临的主要挑战及可能的解决策略.我们相信,这篇综述将对如何调控二元金属硫族化合物的理化性质以获得优异的电化学器件提供指导性建议. | Yousaf Muhammad Naseer Ufra Ali Imran 李一举 Aftab Waseem Mahmood Asif Mahmood Nasir 高鹏 姜银珠 郭少军 | 2022 | Science China Materials2022，65，3： | 0 |
| 6 | Store of value or speculative investment?Market reaction to corporate announcements of cryptocurrency acquisition显示文摘In this study,we analyze the stock market reaction to 35 events associated with 32 publicly traded companies from six countries that have announced cryptocurrency acquisitions,selling,or acceptance as a means of payment.Our analysis focuses on traditional firms whose core business is unrelated to blockchain or cryptocurrency.We find that the aggregate market reaction around these events is slightly positive but statistically insignificant for most event windows.However,when we perform heterogeneity analyses,we observe significant differences in market reaction between events with high(larger CARs)and low cryptocurrency exposure(lower CARs).Multivariate regressions show that the level of exposure to cryptocurrency('skin in the game')is a critical factor underlying abnormal returns around the event.Further analyses reveal that economically meaningful acquisitions of BTC or ETH(relative to firm’s total assets)drive the observed effect.Our findings have important implications for managers,investors,and analysts as they shed light on the relationship between cryptocurrency adoption and firm value. | AndréD.Gimenes Jéfferson A.Colombo Imran Yousaf | 2023 | Financial Innovation2023，9，1： | 0 |
| 7 | Prediction Models for COVID-19 Integrating Age Groups, Gender, and Underlying Conditions显示文摘The COVID-19 pandemic has caused hundreds of thousands of deaths,millions of infections worldwide,and the loss of trillions of dollars for many large economies.It poses a grave threat to the human population with an excessive number of patients constituting an unprecedented challenge with which health systems have to cope.Researchers from many domains have devised diverse approaches for the timely diagnosis of COVID-19 to facilitate medical responses.In the same vein,a wide variety of research studies have investigated underlying medical conditions for indicators suggesting the severity and mortality of,and role of age groups and gender on,the probability of COVID-19 infection.This study aimed to review,analyze,and critically appraise published works that report on various factors to explain their relationship with COVID-19.Such studies span a wide range,including descriptive analyses,ratio analyses,cohort,prospective and retrospective studies.Various studies that describe indicators to determine the probability of infection among the general population,as well as the risk factors associated with severe illness and mortality,are critically analyzed and these ndings are discussed in detail.A comprehensive analysis was conducted on research studies that investigated the perceived differences in vulnerability of different age groups and genders to severe outcomes of COVID-19.Studies incorporating important demographic,health,and socioeconomic characteristics are highlighted to emphasize their importance.Predominantly,the lack of an appropriated dataset that contains demographic,personal health,and socioeconomic information implicates the efcacy and efciency of the discussed methods.Results are overstated on the part of both exclusion of quarantined and patients with mild symptoms and inclusion of the data from hospitals where the majority of the cases are potentially ill. | Imran Ashraf Waleed SAlnumay Rashid Ali Soojung Hur Ali Kashif Bashir Yousaf Bin Zikria | 2021 | Computers, Materials & Continua2021，，6： | 0 |
| 8 | Ensembling Neural Networks for User’s Indoor Localization Using Magnetic Field Data from Smartphones显示文摘Predominantly the localization accuracy of the magnetic field-based localization approaches is severed by two limiting factors:Smartphone heterogeneity and smaller data lengths.The use of multifarioussmartphones cripples the performance of such approaches owing to the variability of the magnetic field data.In the same vein,smaller lengths of magnetic field data decrease the localization accuracy substantially.The current study proposes the use of multiple neural networks like deep neural network(DNN),long short term memory network(LSTM),and gated recurrent unit network(GRN)to perform indoor localization based on the embedded magnetic sensor of the smartphone.A voting scheme is introduced that takes predictions from neural networks into consideration to estimate the current location of the user.Contrary to conventional magnetic field-based localization approaches that rely on the magnetic field data intensity,this study utilizes the normalized magnetic field data for this purpose.Training of neural networks is carried out using Galaxy S8 data while the testing is performed with three devices,i.e.,LG G7,Galaxy S8,and LG Q6.Experiments are performed during different times of the day to analyze the impact of time variability.Results indicate that the proposed approach minimizes the impact of smartphone variability and elevates the localization accuracy.Performance comparison with three approaches reveals that the proposed approach outperforms them in mean,50%,and 75%error even using a lesser amount of magnetic field data than those of other approaches. | Imran Ashraf Soojung Hur Yousaf Bin Zikria Yongwan Park | 2021 | Computers, Materials & Continua2021，，8： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员