Firms' exposures on COVID-19 and stock price crash risk: Evidence from China

Kong, Xiaowei; Jin, Y.; Liu, Lihua; Xu, Jun

doi:10.1016/j.frl.2022.103562

Cited by 11 publications

(6 citation statements)

References 28 publications

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“…Figure 3 depicts the quantification of institutional investor association as an independent variable. Drawing on previous studies (Xu et al, 2023;Huacheng Wang et al, 2015;Liu & Huang, 2019), this paper includes the following control variables in the regression analysis: the average excess turnover rate (OTurnoveri,t); the negative return skewness coefficient (NCSKEW i,t ); firm size (SIZE i,t ); the standard deviation of the firm's annual weekly return (Sigma i,t ); stock net asset book-to-market ratio ( BM i,t ); stock annual average weekly return ( Ret i,t ); information asymmetry ( AbsACC i,t ); debt ratio ( Lev i,t ); and return on assets (ROA i,t ).…”

Section: Definition Of Variablesmentioning

confidence: 99%

Institutional investor association and stock price crash risk: Evidence from China

Zhao,

Naktnasukanjn,

Dawod

et al. 2024

JEECAR

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This study investigates the relationship between institutional investor association and stock price crash risk, using data from all listed non-financial sector companies in the Chinese capital market. The findings indicate a significant positive correlation between institutional investor association and stock price crash risk. Moreover, property rights and agency costs play significant moderating roles in this relationship. Specifically, the impact of institutional investors on stock price crash risk is more pronounced in non-state-owned enterprises (non-SOEs) than in state-owned enterprises (SOEs). Furthermore, this impact is more pronounced in firms with high agency costs and prominent agency problems compared to firms with low agency costs. This research contributes to financial regulators being able to identify better and prevent stock price crashes, ensuring the stability of investors' returns from their invested enterprises.

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Section: Definition Of Variablesmentioning

confidence: 99%

Institutional investor association and stock price crash risk: Evidence from China

Zhao,

Naktnasukanjn,

Dawod

et al. 2024

JEECAR

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show abstract

“…The empirical findings conceptualize their research by testing whether the markets are less prone to PCR, as prices are more likely to reflect the underlying value of assets and be less influenced by speculative or irrational behavior. The empirical studies took several economic events to test that tenet, for example, whether the PCR incurred due to the events of accounting fraud (Richardson et al ., 2022), COVID-19 (Kong et al ., 2022) or social media coverage (Wu et al ., 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

“…PCR also forms significant losses for investors' wealth. Additionally, stock price crashes can have broader economic consequences, as they can affect the stock market's overall performance and the economy (Habib et al ., 2018; Kong et al ., 2022). For example, a stock market crash can lead to a decline in consumer confidence, reducing spending and investment (Ferrer et al ., 2016).…”

Section: Introductionmentioning

confidence: 99%

Does sharia-compliant debt financing reduce stock price crash risk?

Kontesa¹

2023

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PurposeThis paper examines the impact of sharia-compliant debt financing on stock price crash risk. Unlike those previous studies that took Sukuk or sharia-compliant firms, this study tests the impact of the proportion reported sharia-compliant debt financing in the balance sheet on the risk of price crash of a firm.Design/methodology/approachUsing the data from 2,752 firm-year observations of 344 Malaysian non-financial listed companies from 2012 to 2019, this article used a robust panel data estimation technique for statistical inferences. This study also employs panel GMM and quantile least squares as the robustness check.FindingsThis study established a negative relationship between sharia-compliant debt financing and stock price crash risk. The robustness checks with different estimation techniques confirm the results. It implies that firms with a more significant proportion of Sharia-compliant financing tend to have lower future stock price crash risk.Practical implicationsConsistent with the Islamic finance literature, the present study contributes to the existing literature on Islamic capital markets from the perspective of stock price crash risk because it is vital for risk management and investment decision-making as a measure of tail risk for stocks. The findings of this research will assist investors in developing portfolio strategies that incorporate firms with higher levels of sharia-compliant debt financing in their balance sheets. Additionally, the results of this study suggest that policymakers and regulatory bodies should consider revising their monitoring approaches for publicly listed firms.Originality/valueThis study is interesting and unique, as it is a pioneer in testing the impact of sharia-compliant debt financing on reducing stock price crash risk.

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“…Their study addresses the burgeoning field of cryptocurrencies and offers a practical approach to forecasting the volatility in this rapidly evolving market. Jie Song, Shangkun Liang, and Yuhan Zhen [16] investigated the impact of CEO-auditor dialect sharing on stock price crash risk in China. This research sheds light on the intricate relationship between corporate governance factors and stock market dynamics, offering implications for corporate risk management.…”

Section: International Journal On Recent and Innovationmentioning

confidence: 99%

Analysis of Stock Portfolio with Global Economic Factors using Dynamic Data Modelling

B Varija

2023

IJRITCC

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This article analyzes stock portfolios and worldwide economic challenges. This study uses APIs to retrieve data and analyze correlations. To determine and measure how economic indicators affect stock performance. This work introduces an autoencoder-based model to better comprehend the complex relationship between economic conditions and stock portfolio dynamics. The analysis begins with an API retrieval of a wide range of macroeconomic information. These indicators include global economic metrics like GDP, unemployment, CPI, federal funds rates, and treasury bill rates. After collection, data is carefully curated and prepared for analysis. This study uses correlation analysis to understand economic variables and stock portfolio performance. This study explores how economic conditions affect stock prices and portfolio returns. This study seeks to discover trends, dependencies, and future issues that may affect investment decisions. This study also introduces an autoencoder-based neural network model to capture complex nonlinear relationships between economic variables and stock portfolio behavior. Deep learning improves interpretability and prediction, allowing a better understanding of the complex financial ecosystem dynamics. The inquiry provides valuable insights for investors, financial experts, and regulators. This study advances data-driven investment and risk management solutions. The autoencoder-based approach also reveals latent structures and hidden factors that affect stock portfolios. This novel approach opens new study options. In conclusion, this study provides a thorough stock portfolio analysis approach for global economic challenges. API data retrieval, correlation analysis, and a novel autoencoder model are used in this work to better understand the complicated relationships between economic indicators and financial markets. These insights can improve investment and policy decisions in a more integrated and dynamic global economy.

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Firms' exposures on COVID-19 and stock price crash risk: Evidence from China

Cited by 11 publications

References 28 publications

Institutional investor association and stock price crash risk: Evidence from China

Institutional investor association and stock price crash risk: Evidence from China

Does sharia-compliant debt financing reduce stock price crash risk?

Analysis of Stock Portfolio with Global Economic Factors using Dynamic Data Modelling

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