ABSTRACT
This study examines the performance of Islamic gold-backed cryptocurrencies during the bear market of 2020. Price data is collected for three Islamic gold-backed cryptocurrencies, OneGram, HelloGold and X8X, as well as the conventional gold-backed cryptocurrency, PaxGold, and the conventional fiat-backed cryptocurrency, Bitcoin, from December 2019 to November 2020. Analysis via ARMA-EGARCH models show that returns on all cryptocurrencies are lower during the bear market but the decrease is only significant for the Islamic gold-backed cryptocurrencies. Volatility is found to be higher for all five cryptocurrencies but the effect of the bear market on the volatility is only significant for the conventional cryptocurrencies.
ABSTRACT
The study examines the effects of market conditions, volatility and liquidity shocks on the arbitrage profits during pre-COVID and COVID periods. The study uses a conditional quantile regression and finds no significant difference in the impact of market conditions on the arbitrage profits during pre-COVID and COVID crisis periods. The increase in volatility combined with low liquidity during the COVID period makes arbitrage non-viable. However, the decline in volatility during the COVID period encourages investors to initiate arbitrage. The results are useful to fund managers and market analysts to develop suitable trading strategies and stock market regulators to take necessary steps to improve price discovery mechanisms and market efficiency. © 2023 John Wiley & Sons Ltd.
ABSTRACT
Using a large sample of stocks from 48 developed and emerging markets over 1995 to 2021, we find evidence that suggests that international diversification is the best risk-reduction tool when all markets are considered. However, after the turn of the millennium, industrial diversification is the best alternative for funds limited to developed markets, especially when they are restricted to a region. Importantly, the benefits of diversification persist through hard times, such as the Asian financial crisis, the IT bubble burst, the global financial crisis, and the COVID-19 pandemic, demonstrating their countercyclicality and proving their value when investors need them the most. © 2023 CFA Institute. All rights reserved.
ABSTRACT
This paper study the nowcasting and forecasting for the healthcare stock price in the united states during the Covid-19 period including the google trend data information. The data is collected in monthly data from 2015 to 2020 which are five interested stock price indexes in the healthcare sector. Empirically, the finding reveals that the Bayesian structural time series analysis can be used to investigate the stock price indexes with the google trend data is becoming useful for the prediction in term of current movement. In term of the machine learning algorithms, the unsupervised learning k-Mean algorithm is employed to cluster the cycle regimes of the stock market which provided three regimes such as Bull market, Sideways and Bear market. There are twenty-nine months stand for bull market, thirty-seven months are predictively provided sideways market and five months are referred as the bear market. Additionally, the supervised learning algorithms by using the Linear Discriminant Analysis (LDA), k-Nearest Neighbors (kNN) and Support vector machine (SVM) are used to investigate the cycle regimes of healthcare stock in next five year. The results indicated that LDA is chosen by the highest coefficient validation which represented the the regimes of stock in the healcare sector of the unites states of America will stay on the sideways periods in the next five years. Thus, the finding in this paper can be the useful information for investor to manage their portfolio especially, in healthcare sector during the Covid-19 period. © Published under licence by IOP Publishing Ltd.