In search of distress risk in China's stock market

Gao, Liquan; Wang, Qian

doi:10.1016/j.gfj.2018.08.003

Cited by 10 publications

(6 citation statements)

References 37 publications

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“…There are researchers, like Andreou et al (2021), Anginer and Yildizhan (2018), Filipe et al (2016), Vassalou and Xing (2004), who revealed a positive association between distress risk and stock return. Conversely, researchers like Bauer and Agarwal (2014), Gao et al (2019), and Shen (2021) have found a negative relationship. This negative relationship between distress risk and stock return is said to be a distress anomaly, in other words, distress risk is not compensated by the higher returns.…”

Section: Review Of Literaturementioning

confidence: 97%

“…This negative relationship between distress risk and stock return is said to be a distress anomaly, in other words, distress risk is not compensated by the higher returns. Gao et al (2019) found mixed results on distress anomaly in emerging countries. Panel data has also been applied to examine the relationship between distress prediction and stock return.…”

Section: Review Of Literaturementioning

confidence: 99%

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How Indian Pharma Industry Performed in the Last Decade? Impact of a Non-macroeconomic Variable and Financial Distress

Singh

Chakraborty

2023

Jindal Journal of Business Research

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The pharmaceutical industry in India is a significant part of the country’s international commerce, providing appealing channels and prospects for investment. The non-macroeconomic variable, like the COVID-19 pandemic, had a significant impact on the pharmaceutical industry, as well as other industries. This study examines the financial situation of pharma firms and the overall pharma industry using the Altman Z-score before and during the COVID period. Hence, the study is undertaken in three window periods. The study further describes the pharma industry’s stock return to market return and finally, the impact of overall Z-score and its ratios impact on stock return by panel data modeling. The result depicted an improved financial situation in the pharma sector due to improved investors’ confidence and higher beta in pharma stocks compared to market movement during the COVID period. Lastly, it has been analysed that Working Capital to Total Assets (WC/TA) is the significant ratio in predicting the stock return.

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Section: Review Of Literaturementioning

confidence: 97%

Section: Review Of Literaturementioning

confidence: 99%

How Indian Pharma Industry Performed in the Last Decade? Impact of a Non-macroeconomic Variable and Financial Distress

Singh

Chakraborty

2023

Jindal Journal of Business Research

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“…The euphoria of the good news will not last long; over time this euphoria will cease, causing a decrease in stock demand which has an impact on falling prices and stock returns. This is called a market correction, leading to a return reversal [17,18,19]. Stocks with a high return and in the winner category will decrease to a negative value, therefore becoming loser stocks.…”

Section: Hypotheses Developmentmentioning

confidence: 99%

Anomaly Analysis of Winner-Looser Stocks IDX30 and LQ45 Overreaction Amid the Covid-19: What Can Learn?

Susilowati¹,

Purnama²,

Sudaryana³

et al. 2022

Proceedings of the 1st UPY International Conference on Education and Social Science (UPINCESS 2022)

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The purpose of this research was to investigate the overreaction anomaly in stocks listed on the IDX30 and LQ45 amid the COVID-19 Pandemic in 2020. An overreaction anomaly or price reversal occurs when looser stocks outperform winner stocks, which can be seen from the ACAR value of each stock. The samples used in this study are companies listed on the IDX30 and LQ45 period February-July 2020 and the period August-January 2021. This is a comparative study, hence using a qualitative approach, while the method study is the event study. The measurement used to estimate the market reaction to the published information is the abnormal return of each stock. The results showed that there was an overreaction anomaly in some test periods which was indicated by the ACAR value of looser stock that could outperform the ACAR value of winner stock. The results of the independent sample t-test statistical test showed that the ACAR value of winner stock had a significant difference from the ACAR value of loser stock. The implications of the research are investors should be aware toward risk potentially to macro economy shock and investors to assess rationally and wisely in making decisions even to blue chips stocks.

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“…In recent years, the distress risk anomaly in emerging markets has been investigated in many studies. For example, Gao et al [36] investigated the significance of book-tomarket, size, and momentum factors in capturing the financial distress risks of the stock market in China. Lai et al [37] explained stock returns in the stock markets of Australia, ailand, Singapore, Malaysia, Korea, Indonesia, and Hong Kong, finding that the four-factor financial crisis risk asset pricing model has received extensive empirical support in these markets.…”

Section: Literature Reviewmentioning

confidence: 99%

Stock Market Temporal Complex Networks Construction, Robustness Analysis, and Systematic Risk Identification: A Case of CSI 300 Index

Wan

Zhang

et al. 2020

Complexity

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The Chinese stock 300 index (CSI 300) is widely accepted as an overall reflection of the general movements and trends of the Chinese A-share markets. Among the methodologies used in stock market research, the complex network as the extension of graph theory presents an edged tool for analyzing internal structure and dynamic involutions. So, the stock data of the CSI 300 were chosen and divided into two time series, prepared for analysis via network theory. After stationary test and coefficients calculated for daily amplitudes of stock, two “year-round” complex networks were constructed, respectively. Furthermore, the network indexes, including out degree centrality, in degree centrality, and betweenness centrality, were analyzed by taking negative correlations among stocks into account. The first 20 stocks in the market networks, termed “major players,” “gatekeeper,” and “vulnerable players,” were explored. On this basis, temporal networks were constructed and the algorithm to test robustness was designed. In addition, quantitative indexes of robustness and evaluation standards of network robustness were introduced and the systematic risks of the stock market were analyzed. This paper enriches the theory on temporal network robustness and provides an effective tool to prevent systematic stock market risks.

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In search of distress risk in China's stock market

Cited by 10 publications

References 37 publications

How Indian Pharma Industry Performed in the Last Decade? Impact of a Non-macroeconomic Variable and Financial Distress

How Indian Pharma Industry Performed in the Last Decade? Impact of a Non-macroeconomic Variable and Financial Distress

Anomaly Analysis of Winner-Looser Stocks IDX30 and LQ45 Overreaction Amid the Covid-19: What Can Learn?

Stock Market Temporal Complex Networks Construction, Robustness Analysis, and Systematic Risk Identification: A Case of CSI 300 Index

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