Stock Market Growth Link in Asian Emerging Countries: Evidence from Granger Causality and Co-integration Tests

Pattnaik, Monalisha; Gahan, Padmabati

doi:10.1007/978-981-13-1843-6_2

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…(2020) and Sayed and Eledum (2021) applied first detection as event day to measure capital market reaction, however these studies focuses on single market, whereas this study analyzes 12 different markets with nine different event dates which might show different dimension. Moreover, prior studies like Choudhary and Singhal (2020), Pattnaik and Gahan (2018) and Stevanius and Sukamulja( 2020) examined Asian capital market co-integration found either mild or negative co-integration, therefore multiple capital market reaction with uniform event date will not reflect the actual aggregate market behavior.…”

Section: Data Set and Methodologymentioning

confidence: 95%

“…In this study, the COVID 19 relevant events are identified in Table 2 where the first local detection of COVID 19 infection as per the WHO situation reports in 12 Asian countries was considered as the event date like Alam et al (2021), Adnan et al (2020) and Sayed and Eledum (2021) applied first detection as event day to measure capital market reaction, however these studies focuses on single market, whereas this study analyzes 12 different markets with nine different event dates which might show different dimension. Moreover, prior studies like Choudhary and Singhal (2020), Pattnaik and Gahan (2018) and Stevanius and Sukamulja( 2020) examined Asian capital market co-integration found either mild or negative co-integration, therefore multiple capital market reaction with uniform event date will not reflect the actual aggregate market behavior. Peterson (1989), average estimation window durations range from 100 to 300 days.…”

Section: Sample Formation and Data Sourcementioning

confidence: 96%

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Asian perspective of capital market performance amid the COVID 19 pandemic

Adnan

2022

AJAR

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PurposeThe purpose of this research is to investigate the short-term capital markets' reactions to the public announcement first local detection of novel corona virus (COVID 19) cases in 12 major Asian capital markets.Design/methodology/approachUsing the constant mean return model and the market model, an event study methodology has been implied to determine the cumulative abnormal returns (CARs) of 10 pre and post-event trading days. The statistical significance of the data was assessed using both parametric and nonparametric test statistics.FindingsFirst discovery of local COVID 19 cases had a substantial impact on all 12 Asian markets on the event day, as shown by statistically significant negative average abnormal return (AAR) and cumulative average abnormal return (CAAR). The single factor ANOVA result has also demonstrated that there is no variability among 12 regional markets in terms of short-term market responses. Furthermore, there is little evidence that these major Asian stock market indices differ significantly from the FTSE All-World Index which might suggest possible spillover impact and co-integration among the major Asian capital markets. The study further discovers that market capitalization and liquidity did not have any significant impact on market reaction to announcement.Research limitations/implicationsThe study's contribution might have been compromised by the absence of socio-demographic, technical, financial and other significant policy factors from the analysis.Practical implicationsThese findings will be considerably helpful in tackling this unprecedented epidemic issue for personal and institutional investors, industrial and economic experts, government and policymakers in assessing the market in special circumstances, diversifying risk and developing financial and monetary policy proposals.Originality/valueThis paper is the first to examine the effects of local COVID 19 detection announcement on major Asian capital markets. This study will add to the literature by investigating unusual market returns generated by infectious illness outbreaks and the overall market efficiency and investors' behavioral pattern of major Asian capital markets.

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Section: Data Set and Methodologymentioning

confidence: 95%

Section: Sample Formation and Data Sourcementioning

confidence: 96%

Asian perspective of capital market performance amid the COVID 19 pandemic

Adnan

2022

AJAR

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“…To solve a variety of time series issues shown in economic parameters, weather indicators, economic growth, GDP and wind speed etc. (Tumer et al, 2018, Kordanuli et al 2017, Khashei et al, 2011, Milacic et al, 2017, Cadenas et al, 2010, Maleki et al 2018 and Chakraborty et al, 2019, James et al, 2013 and Pattnaik et al 2018) several hybrid approaches were developed in previous literature. To forecast dynamic time series accurately the hybrid ARIMA-ANN model (Zhang et al, 2003) has gained popularity due to its capacity.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms to Fight the COVID-19 Pandemic

Pattnaik

Pattnaik³

2023

Preprint

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The ongoing novel COVID-19 global pandemic is one of the health emergencies in 21st century after hundred years of Spanish flu that affected almost all the countries in the world. The objective of this study is to generate STM and LTM real-time out of sample forecasts of the future COVID-19 confirmed and death cases respectively for the top five mostly affected countries in the world namely USA, India, Brazil, Russia and UK. As of January 17, 2021, it has caused a pandemic outbreak with more than 94.5 million confirmed cases and more than 2 million reported deaths worldwide. Due to extreme robust behaviour in the univariate time series data, forecasting of both COVID-19 confirmed and death cases has become the exigent task for the government officials, healthcare workers, economists, corporate leaders, government, decision makers, public-policy makers, and scientific experts to allocate health resources. To solve this problem different hybrid approaches are applied which eliminate both linear and non-linear errors of the time series datasets and the predictions of for these countries will be practical to act as forewarning for all.

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“…Therefore, the combination of linear and nonlinear models can be well suited for accurately modeling such complex autocorrelation structures (Percival et al, 2020 and which reduce the bias and variances of the prediction residuals of the component models. Several hybrid methodologies were discussed in literature to solve a variety of time series problems arose in economic parameters, economic growth, GDP and wind speed (Tumer et al, 2018, Kordanuli et al 2017, Khashei et al, 2011, Milacic et al, 2017, Cadenas et al, 2010, Maleki et al 2018and Chakraborty et al, 2019, James et al, 2013and Pattnaik et al 2018). Zhang's hybrid ARIMA-ANN model (Zhang et al, 2003) has gained popularity due to its capacity to forecast complex time series accurately.…”

Section: Introductionmentioning

confidence: 99%

Prediction of COVID-19 Pandemic of Top Ten Countries in the World Establishing a Hybrid AARNN LTM Model

Gahan

Pattnaik

Nayak

et al. 2021

Preprint

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The novel COVID-19 global pandemic has become a public health emergency of international concern affecting 215 countries and territories around the globe. As of 28 November 2020, it has caused a pandemic outbreak with a total of more than 6,171,5119 confirmed infections and more than 1,44,4235 confirmed deaths reported worldwide. The main focus of this paper is to generate LTM real-time out of sample forecasts of the future COVID-19 confirmed and death cases respectively for the top ten profoundly affected countries including for the world. To solve this problem we introduced a novel hybrid approach AARNN model based on ARIMA and ARNN forecasting model that can generate LTM (fifty days ahead) out of sample forecasts of the number of daily confirmed and death COVID-19 cases for the ten countries namely USA, India, Brazil, Russia, France, Spain, UK, Italy, Argentina, Colombia and also for the world respectively. The predictions of the future outbreak for different countries will be useful for the effective allocation of health care resources and will act as early-warning system for health warriors, corporate leaders, economists, government/public-policy makers, and scientific experts.

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Stock Market Growth Link in Asian Emerging Countries: Evidence from Granger Causality and Co-integration Tests

Cited by 6 publications

References 6 publications

Asian perspective of capital market performance amid the COVID 19 pandemic

Asian perspective of capital market performance amid the COVID 19 pandemic

Machine Learning Algorithms to Fight the COVID-19 Pandemic

Prediction of COVID-19 Pandemic of Top Ten Countries in the World Establishing a Hybrid AARNN LTM Model

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