The Causal Relationship Between Coronavirus (COVID-19) and Stock Indices: A Case Study for G20 Countries

Coronavirus (COVID-19 or SARS-CoV-2) spreads rapidly around the world. Coronavirus reached 8,000 cases on Jan 30, 2020. When the cases that lost their lives were examined, the majority of them were found to be elderly patients or patients diagnosed with chronic heart, lung and kidney, Parkinson's, and Diabetes. The number of cases worldwide was 509,164, the number of deaths worldwide was 23,335 while analyzing this study. COVID-19 also affected many economic variables in the world. Some of these variables are stock indices. In this study, it is aimed to discover the causal relationship between stock indices and coronavirus. Daily data were used in the research. Stock indices and total deaths and total cases of coronavirus are matched for each country. Unit root tests, Pairwise Granger Causality Tests, and regression analysis were made. The stationarity and level of significance were calculated. Causality Tests used to test hypotheses regarding the presence and the direction of causality. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Are Bitcoin and Gold a Safe Haven during COVID-19 and the 2022 Russia–Ukraine War?

Our investigation strives to unearth the best portfolio hedging strategy for the G7 stock indices through Bitcoin and gold using daily data relevant to the period 2 January 2016 to 5 January 2023. This study uses the DVECH-GARCH model to model dynamic correlation and then compute optimal hedge ratios and hedging effectiveness. The empirical findings show that Bitcoin and gold were rather effective hedge assets before COVID-19 and diversifiers during the pandemic and Russia–Ukraine war. From hedging effectiveness perspectives, gold and Bitcoin are safe-haven assets, and the investment risk of G7 stock indices could be hedged by taking a short position during thepandemic period and war except for the pair Nikkei/Gold. Additionally, gold beats Bitcoin in terms of hedging efficiency. We thus demonstrate the central role of Bitcoin and gold as financial market participants, particularly during market turmoil and downward movements. Our findings can be of interest to investors, regulators, and governments to take into consideration the role of Bitcoin in financial markets.

Empirical Analysis Of Covid-19 On Brics Nations And Their Impact Positive/Negative On Stock Market Indices

The coronavirus is a novel disease affected across the world. The symptoms are cold and fever. It was invented in December 2019 by China and is denoted as Covid-19 by World Health Organization (WHO). This disease affected not only humans but also influenced the stock market and an economic crisis across the world. About 3% effect on the global economy, it was comparatively higher than 2008-09. 1.2% and 2.8% were affected by middle Asia and the middle east during 2019-20. The oil export and import countries were affected by about 4.2% and 0.7% (IMF, 2020). The stock market is one of the barometers for Post Covid-19 status measurement. The study aims to analyze the infected cases, investigate the stock market indices in BRICS Nations, and find the relations between infected situations and the stock market of BRICS Nations. This study uses secondary data from the official websites of the World Health Organization, Dashboard from January 2021-June 2022. Using Multi Regression analysis in the five nations were infected and death cases and stock market indices. This study will be helpful in the present situation, and investors will decide to diversify funds in the profitable sector to accelerate their wealth. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Assessment of Volatility Contagion Effect Between Major Financial Markets During COVID-19 Pandemic Catastrophes

Across the globe, the havoc of the pandemic known to be a black swan event has brought miseries, deaths, uncertainty, loss of lives and jobs holding the humanity in a state of despair. The financial markets have been equally hit by the pandemic due to on-going uncertainty and hopelessness among the masses. The aim of this study is to examine the volatility contagion and dynamic conditional correlations between eight stock indices during the gloomy period to validate that there is a scope for revisiting the investment portfolio, create natural hedge in the investment portfolio by using exponential generalised autoregressive conditional heteroscedasticity (EGARCH) and dynamic conditional correlation generalised autoregressive conditional heteroscedasticity (DCC-GARCH) approach. We conducted an in-depth analysis of capturing volatility among stock indices ranging from tracking the volatility followed by estimating persistence and multivariate volatility contagion of major stock indices of developed and developing economies during turbulent times of the pandemic when the globe was reeling under the taxing consequences of the first and second wave of COVID-19. There are very few studies that have conducted an in-depth analysis of capturing volatility of stock indices ranging from tracking the asymmetric volatility followed by estimating persistence and multivariate volatility contagion of major stock indices of developed and developing economies during turbulent times of the pandemic when the globe was reeling under the taxing consequences of the first and second wave of COVID-19. © 2022 Management Development Institute.

Tail-risk spillovers from China to G7 stock market returns during the COVID-19 outbreak: A market and sectoral analysis.

This study uses a combination of copulas and CoVaR to investigate risk spillovers from China to G7 countries before and during the COVID-19 pandemic. Using daily data on stock and equity sectors for the period from January 1, 2013 to June 9, 2021, the main empirical results show that, before the COVID-19 pandemic, stock markets were positively related and systemic risk was comparable for all countries. However, during the COVID-19 outbreak, the level of dependence increased for all G7 countries and the upside-downside risk spillovers become on average higher for all stock markets, with the exception of Japan. Our results also provide evidence of higher market risk exposure to information from China for the technology and energy sectors. Moreover, we find an asymmetric risk spillover from China to the G7 stock markets, with higher intensity in downside risk spillovers before and during COVID-19 spread.

Is Ethereum's Price Return Determined by COVID-19, Macro-financial, and Crypto Market Factors?

We investigated COVID-19 cases per country, macro-financial, and crypto market factors that might have affected Ethereum's price return in the top three countries of users, which were also affected by COVID-19 (United States, China, and Germany). Feasible Generalized Least Square (FGLS) was used as the methodology and the generalized method of moments (GMM) was tested for a robustness check. The findings revealed that Ethereum price returns were greatly affected by COVID-19 factors. Meanwhile, macro-financial factors (stock indices and gold) had stronger effects on the return of Ethereum price rather than the crypto market. © 2022 ACM.

Factors Impacting on Bitcoin Returns in the Top Three COVID-19 Infected Countries

Our research investigated the effect of COVID-19 cases (cumulative positive and death cases) on Bitcoin price in the top three infected countries based on WHO (United States, Brazil, and India). Macro-financial and internal factors are employed as the other independent determinants of Bitcoin prices. We utilized feasible generalized least squares (FGLS) alongside generalized method of moments (GMM) for robustness check. The output revealed robustness across different econometric models. The findings unraveled that COVID-19 cumulative positive cases brought positive but insignificant impacts on Bitcoin returns, while its death cases stated the opposite. Macro-financial factors represented by stock indices and gold price imposed that they could be alternative investments to Bitcoin under the uncertain times of COVID-19. Liquidity and volume in respect to return discovery of Bitcoin are imperative instruments, as these internal factors move in the same direction with Bitcoin's demand and return movement. Efficiency in internal factors drives investors' demand, hence pushing the increase in Bitcoin's return. © 2022 ACM.

Deep Learning-Based Trend Analysis on Indian Stock Market in COVID-19 Pandemic Scenario and Forecasting Future Financial Drift

This present study has used the long-short-term memory (LSTM) network-based deep learning architecture to analyze the influence of the current widespread COVID-19 on the Indian stock market. The major contribution of this work is as follows: (1) Designing LSTM-based deep neural network is used to study the effect of the COVID-19 outbreak and Lockdown on the Indian stock exchange (Nifty 50), and (2) designing a prediction model to capture the effect of various COVID-19 waves in India on Indian Stock exchange. The outcomes of the analysis show that the increase in daily new confirmed cases, recovered cases, and death cases have a significant adverse impact on the trend of the stock market. Moreover, the results of the work have also analyzed the impact of government policy such as ‘lockdown city’ with a reaction to increased Pandemic cases. This work is briefly summarized as follow: (1) LSTM-based deep neural network is used for this study to analyze the effect of the COVID-19 outbreak on the Indian stock exchange. (2) The Indian Stock exchange affected by the COVID-19 pandemic has been studied. Here, the analysis is based on the impact of COVID-19 including the effect of lockdown. (3) A prediction model has been proposed for the study of the behavior of the Indian stock index (Nifty 50) during the COVID-19 pandemic. (4) Comparison of the efficacy of the suggested approach with other existing baseline regression models. © 2022, The Institution of Engineers (India).

Fuzzy Time-Series Models Based on Intuitionistic Fuzzy, Rough Set Fuzzy, and Differential Evolution

Time-series forecasting is a vital concern for any data having temporal variations. Comparing with the other conventional time-series methodologies, the fuzzy time-series (FTS) proved its superiority. Substantial research using time-series forecasting to predict the stock index data has been found in the earlier works. The fuzzy sets approach alone cannot explain the data thoroughly. In this article, we have proposed three different methods of time-series forecasting. The first method is based on a rough set of FTS, a rule induction-based method;the second method is based on intuitionistic FTS. The last method is the extension of the second method using differential evolution. In the first model, a fuzzy algorithm based on rules is used to derive prediction rules from the time-series data and adopt an adaptive expectation model that replaces the fuzzy logical relationships or groups. In the second method, to split the universe of discourse into a non-uniform interval, a clustering algorithm-based intuitionistic fuzzy approach is used, taking care of the membership and non-membership function. Finally, the last method has been tuned for a better outcome using differential evolution. To examine the results, contrast analyses on the Taiwan stock exchange data and daily cases of COVID-19 pandemic prediction have been carried out. The outcome of the proposed approaches validates that the first and second techniques, showing promising results. However, the third method outperforms the other methods and the present techniques concerning the root-mean-square error metric. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Is Bitcoin a safe haven? Application of FinTech to safeguard Australian stock markets

Purpose>This study aims to address the timely question of whether Bitcoin exhibited a safe haven property against the major Australian stock indices during the first and second waves of the COVID-19 pandemic in Australia and whether such property is similar or different in one year time from the first wave of the COVID-19.Design/methodology/approach>The authors used the bivariate Dynamic Conditional Correlation, Generalized Autoregressive Conditional Heteroskedasticity model, on the five-day returns of Bitcoin and Australian stock indices for the sample period between 23 April, 2011 and 19 April, 2021.Findings>The results show that Bitcoin offered weak safe haven and hedging benefits when combined in a portfolio with S&P/ASX 200 Financials index, S&P/ASX 200 Banks index or S&P/ASX 300 Banks index. In regard to the S&P/ASX All Ordinaries Gold index, the authors found Bitcoin a risky candidate with inconsistent safe haven and hedging benefits. Against S&P/ASX 50 index, S&P/ASX 200 index and S&P/ASX 300 index, Bitcoin was nothing more than a diversifier. The outset of the second COVID-19 wave, which was comparatively more severe than the first, is also reflected in the results with considerably higher correlations.Originality/value>There is a lack of in-depth empirical evidence on the safe haven capabilities of Bitcoins for various Australian stock indices during the first and second waves of the COVID-19 pandemic. The study bridges this void in research.

Calendar anomalies in the stock markets: conventional vs Islamic stock indices

PurposeIn this study, the authors evaluate seven calendar anomalies’–the day of the week, weekend, the month of the year, January, the turn of the month (TOM), Ramadan and Eid festivals–effects in both the conventional and Islamic stock indices of Bangladesh. Also, the authors examine whether these anomalies differ between the two indices.Design/methodology/approachThe authors select the Dhaka Stock Exchange (DSE) Broad Index (DSEX) and the DSEX Shariah Index (DSES) of the DSE as representatives of the conventional and Islamic stock indices respectively. To carry out the investigation, the authors employ the generalized autoregressive conditional heteroskedasticity (GARCH) typed models from January 25, 2011, to March 25, 2020.FindingsThe study’s results indicate the presence of all these calendar anomalies in either conventional or Islamic indices or both, except for the Ramadan effect. Some significant differences in the anomalies between the two indices (excluding the Ramadan effect) are detected in both return and volatility, with the differences being somewhat more pronounced in volatility. The existence of these calendar anomalies argues against the efficient market hypothesis of the stock markets of Bangladesh.Practical implicationsThe study’s results can benefit investors and portfolio managers to comprehend different market anomalies and make investment strategies to beat the market for abnormal gains. Foreign investors can also be benefited from cross-border diversifications with DSE.Originality/valueTo the authors’ knowledge, first the calendar anomalies in the context of both conventional and Islamic stock indices for comparison purposes are evaluated, which is the novel contribution of this study. Unlike previous studies, the authors have explored seven calendar anomalies in the Bangladesh stock market's context with different indices and data sets. Importantly, no study in Bangladesh has analyzed calendar anomalies as comprehensively as the authors’.

Effective forecasting of stock market price by using extreme learning machine optimized by PSO-based group oriented crow search algorithm.

Stock index price forecasting is the influential indicator for investors and financial investigators by which decision making capability to achieve maximum benefit with minimum risk can be improved. So, a robust engine with capability to administer useful information is desired to achieve the success. The forecasting effectiveness of stock market is improved in this paper by integrating a modified crow search algorithm (CSA) and extreme learning machine (ELM). The effectiveness of proposed modified CSA entitled as Particle Swarm Optimization (PSO)-based Group oriented CSA (PGCSA) to outperform other existing algorithms is observed by solving 12 benchmark problems. PGCSA algorithm is used to achieve relevant weights and biases of ELM to improve the effectiveness of conventional ELM. The impact of hybrid PGCSA ELM model to predict next day closing price of seven different stock indices is observed by using performance measures, technical indicators and hypothesis test (paired t-test). The seven stock indices are considered by incorporating data during COVID-19 outbreak. This model is tested by comparing with existing techniques proposed in published works. The simulation results provide that PGCSA ELM model can be considered as a suitable tool to predict next day closing price.

The Effect of Quantitative Easing through Google Metrics on US Stock Indices

The purpose of this study is to investigate the fluctuations that occur in stock returns of US stock indices when there is an increase in the volume of Google internet searches for the phrase “quantitative easing” in the US. The exponential generalized autoregressive conditional heteroscedasticity model (EGARCH) was applied based on weekly data of stock indices using the three-factor model of Fama and French for the period of 1 January 2006 to 30 October 2020. The existence of a statistically significant relationship between searches and financial variables, especially in the stock market, is evident. The result is strong in three of the four stock indices studied. Specifically, the SVI index was statistically significant, with a positive trend for the S&P 500 and Dow Jones indices and a negative trend for the VIX index. Investor focus on quantitative easing (QE), as determined by Google metrics, seems to calm stock market volatility and increase stock returns. Although there is a large body of research using Google Trends as a crowdsourcing method of forecasting stock returns, this paper is the first to examine the relationship between the increase in internet searches of “quantitative easing” and stock market returns.

THE IMPACT OF THE GLOBAL COVID-19 PANDEMIC ON THE STOCK MARKET INDICES OF SELECTED COUNTRIES

The worldwide spread of coronavirus has shaken stock markets and significantly increased risk. The most-watched US stock index S&P 500 fell by 35% from 19th February to 23rd March. Indices of other countries registered a similar development. Although the spread of the virus has been brought under control in many countries currently, the worldwide number of infections is still growing. Unprecedented monetary and fiscal stimuli, on the other hand, have reversed sentiment in the markets. From 23rd March 2020, stock markets gradually had been growing until the S&P 500 index reached only 5% below historical highs on 8th June 2020. The paper deals with the development of volatility of selected stock indices, their mutual correlations, and the relationship with the number of infected in a given country.

Trend of Oil Prices, Gold, GCC Stocks Market during Covid-19 Pandemic: A Wavelet Approach

This paper analyzed the co-movement among the stock indices of GCC members like Abudhabi, Bahrain, Oman, Saudi Arabia, Qatar and global oil prices as indicated by Brent, WTI. Gold, S&P 500 index and Dow Jones index has also been taken into account. Daily prices from January 2, 2020 up to September 30, 2020 were used for the analysis. In order to analyze the co-movement among the above mentioned indices in time frequency space wavelet transform approach has been used. The techniques employed in the study include wavelet correlation and wavelet coherence approach. The findings of this empirical study suggests that though there was no much interconnectedness among the above mentioned factors in the short run, the impact of the global pandemic crisis that got added to the oil price shock could be seen in the medium and long run. The study suggests that investors need to be cautious of their investment decisions with respect to the time horizon as these markets show significant co-movement in the long run when hit by global crisis.

Is Bitcoin a safe haven? Application of FinTech to safeguard Australian stock markets

Purpose This study aims to address the timely question of whether Bitcoin exhibited a safe haven property against the major Australian stock indices during the first and second waves of the COVID-19 pandemic in Australia and whether such property is similar or different in one year time from the first wave of the COVID-19. Design/methodology/approach The authors used the bivariate Dynamic Conditional Correlation, Generalized Autoregressive Conditional Heteroskedasticity model, on the five-day returns of Bitcoin and Australian stock indices for the sample period between 23 April, 2011 and 19 April, 2021. Findings The results show that Bitcoin offered weak safe haven and hedging benefits when combined in a portfolio with S&P/ASX 200 Financials index, S&P/ASX 200 Banks index or S&P/ASX 300 Banks index. In regard to the S&P/ASX All Ordinaries Gold index, the authors found Bitcoin a risky candidate with inconsistent safe haven and hedging benefits. Against S&P/ASX 50 index, S&P/ASX 200 index and S&P/ASX 300 index, Bitcoin was nothing more than a diversifier. The outset of the second COVID-19 wave, which was comparatively more severe than the first, is also reflected in the results with considerably higher correlations. Originality/value There is a lack of in-depth empirical evidence on the safe haven capabilities of Bitcoins for various Australian stock indices during the first and second waves of the COVID-19 pandemic. The study bridges this void in research.

COVID-19 outbreak and sectoral performance of the Australian stock market: An event study analysis.

The outbreak of COVID-19 has weakened the economy of Australia and its capital market since early 2020. The overall stock market has declined. However, some sectors become highly vulnerable while others continue to perform well even in the crisis period. Given this new reality, we seek to investigate the initial volatility and the sectoral return. In this study, we analyse data for eight sectors such as, transportation, pharmaceuticals, healthcare, energy, food, real estate, telecommunications and technology of the Australian stock market. In doing so, we obtain data from Australian Securities Exchange (ASX) and analysed them based on 'Event Study' method. Here, we use the 10-days window for the event of official announcement of the COVID-19 outbreak in Australia on 27 February 2020. The findings of the study show that on the day of announcement, the indices for food, pharmaceuticals and healthcare exhibit impressive positive returns. Following the announcement, the telecommunications, pharmaceuticals and healthcare sectors exhibit good performance, while poor performance is demonstrated by the transportation industry. The findings are vital for investors, market participants, companies, private and public policymakers and governments to develop recovery action plans for vulnerable sectors and enable investors to regain their confidence to make better investment decisions.