Deep Learning for Multi-factor Models in Regional and Global Stock Markets

2020 ◽  
pp. 87-102
Author(s):  
Masaya Abe ◽  
Kei Nakagawa
Keyword(s):  
Deep Learning ◽  
Stock Markets ◽  
Factor Models ◽  
Global Stock Markets ◽  
Global Stock

Winter Is Coming: Repercussions of a New COVID-19 Wave in Global Stock Markets

2020 ◽  
Author(s):  
Panagiotis Lazaris ◽  
Anastasios Petropoulos ◽  
Vasileios Siakoulis ◽  
Evangelos Stavroulakis ◽  
Nikos Vlachogiannakis ◽  
...  
Keyword(s):  
Stock Markets ◽  
Global Stock Markets ◽  
Global Stock

scholarly journals Risk contagion of global stock markets under COVID‐19:A network connectedness method

2021 ◽  
Author(s):  
Honghai Yu ◽  
Wangyu Chu ◽  
Yu’ang Ding ◽  
Xuezhou Zhao
Keyword(s):  
Stock Markets ◽  
Network Connectedness ◽  
Global Stock Markets ◽  
Global Stock

COVID-19 and overconfidence bias: the case of developed, emerging and frontier markets

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vijay Kumar Shrotryia ◽  
Himanshi Kalra
Keyword(s):  
Stock Markets ◽  
Impulse Response Functions ◽  
Economic Activities ◽  
Content Type ◽  
Long Run ◽  
Governance Practices ◽  
Global Stock Markets ◽  
Developed Stock Markets ◽  
Overconfidence Bias ◽  
Global Stock

PurposeThe main purpose of the present study is to delve into the overconfidence bias in global stock markets during both pre COVID-19 and COVID-19 phases.Design/methodology/approachThe present study makes use of daily adjusted closing prices and volume of the broad market indices of 46 global stock markets over a period ranging from July 2015 till June 2020. The sample period is split into pre COVID-19 and COVID-19 phases. In order to test the overconfidence fallacy in the chosen stock markets, bivariate market-wide vector auto regression (VAR) models and impulse response functions (IRFs) have been employed in both phases.FindingsA highly significant contemporaneous relationship between market return and volume appears to be more pronounced in the Japanese, US, Chinese and Vietnamese stock markets in the pre COVID-19 era for the relevant coefficients are positive and highly significant for most lags. Coming to the period of turbulence, the present study discovers strong overconfident behavior in the Chinese, Taiwanese, Turkish, Jordanian and Vietnamese stock markets during COVID-19 phase.Practical implicationsA stark finding is that none of the developed stock markets reveal strong overconfidence bias during pandemic, suggesting a loss or decline in the investors' confidence. Therefore, the regulators should try to regain the investors' trust and confidence in the markets by ensuring honest, fair and transparent practices. The money managers should reduce the transaction cost to encourage trading and educate investors to hold a well-diversified portfolio to mitigate risk in the long run. The governments may launch recovery packages focusing on sustaining and improving economic activities. Finally, a better investment culture may be built by the corporate houses through good corporate governance practices to regain lost trust.Originality/valueThe present study appears to be the very first attempt to gauge overconfidence bias in the wake of a recent COVID-19 pandemic.

COVID-19 Vaccines: Saving Lives and the Global Stock Markets

2021 ◽  
Author(s):  
Kam Fong Chan ◽  
Zhuo Chen ◽  
Yuanji Wen ◽  
Tong Xu
Keyword(s):  
Stock Markets ◽  
Global Stock Markets ◽  
Global Stock

scholarly journals The recovery of global stock markets indices after impacts due to pandemics

2021 ◽  
Vol 55 ◽  
pp. 101335 ◽  
Author(s):  
S.A. David ◽  
C.M.C. Inácio Jr. ◽  
José A. Tenreiro Machado
Keyword(s):  
Stock Markets ◽  
Global Stock Markets ◽  
Global Stock

scholarly journals Risk measurement of global stock markets: a factor copula-based GJR-GARCH approach

2019 ◽  
Vol 1324 ◽  
pp. 012098
Author(s):  
Quanrui Song ◽  
Jianxu Liu ◽  
Songsak Sriboonchitta
Keyword(s):  
Stock Markets ◽  
Risk Measurement ◽  
Global Stock Markets ◽  
Global Stock
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