Abnormal Returns and Idiosyncratic Volatility Puzzle: Evidence from the Chinese Stock Market

Qu, Zhengyang; Liu, Xiaotian; He, Shi

doi:10.1080/1540496x.2018.1468249

Cited by 14 publications

(11 citation statements)

References 23 publications

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“…Stambaugh et al. , 2015; Qu et al. , 2019) also confirm the absence of a risk-return trade-off with respect to ivol using multiple data frequencies, covering a more extended sample period, using different portfolio methodologies, and controlling for different cross-sectional effects.…”

Section: Survey Of the Literaturesupporting

confidence: 53%

“…Other researchers (e.g. Stambaugh et al, 2015;Qu et al, 2019) also confirm the absence of a risk-return trade-off with respect to ivol using multiple data frequencies, covering a more extended sample period, using different portfolio methodologies, and controlling for different cross-sectional effects. The negative relationship is inconsistent with the risk-return trade-off and is called the idiosyncratic volatility puzzle.…”

Section: Survey Of the Literaturementioning

confidence: 75%

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Does the low-risk anomaly exist in the Indian equity market? A test using alternative risk measures

Ali

Badhani

Kumar

2021

JES

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PurposeThis study aims to investigate the risk-return trade-off in the Indian equity market at both the aggregate equity market level and in the cross-sections of stock return using alternative risk measures.Design/methodology/approachThe study uses weekly and monthly data of 3,085 Bombay Stock Exchange-listed stocks spanning over 20 years from January 2000 to December 2019. The study evaluates the risk-return trade-off at the aggregate equity market level using the value-weighted and the equal-weighted broader portfolios. Eight different risk proxies belonging to the conventional, downside and extreme risk categories are considered to analyse the cross-sectional risk-return relationship.FindingsThe results show a positive equity premium on the value-weighted portfolio; however, the equal-weighted portfolio of these stocks shows an average return lower than the return on the 91-day Treasury Bills. The inverted size premium mainly causes this anomaly in the Indian equity market as the small stocks have lower returns than big stocks. The study presents a strong negative risk-return relationship across different risk proxies. However, under the subsample of more liquid stocks, the low-risk anomaly regarding other risk proxies becomes moderate except the beta-anomaly. This anomalous relationship seems to be caused by small and less liquid stocks having low institutional ownership and higher short-selling constraints.Practical implicationsThe findings have important implications for investors, managers and practitioners. Investors can incorporate the effects of different highlighted anomalies in their investment strategies to fetch higher returns. Managers can also use these findings in their capital budgeting decisions, resource allocations and other diverse range of direct and indirect decisions, particularly in emerging markets such as India. The findings provide insights to practitioners while valuing the firms.Originality/valueThe study is among the earlier attempts to examine the risk-return trade-off in an emerging equity market at both the aggregate equity market level and in the cross-sections of stock returns using alternative measures of risk and expected returns.

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Section: Survey Of the Literaturesupporting

confidence: 53%

Section: Survey Of the Literaturementioning

confidence: 75%

Does the low-risk anomaly exist in the Indian equity market? A test using alternative risk measures

Ali

Badhani

Kumar

2021

JES

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“…Moreover, the prospect theory suggests that stock traders tend to be risk-averse when they incur losses and risk-seeking when they capture gains. Differentiating stocks according to their past performances in the Chinese stock market, Qu et al (2019) detect a positive volatility-return relation among firms with a positive abnormal return and a negative volatility-return relation among firms with a negative abnormal return. In our analysis, we use the standard deviation of the individual stock return in the estimation window “VO” to measure the volatility of different stocks as another control variable.…”

Section: Experimental Designmentioning

confidence: 99%

Heterogeneous firm-level responses to the US 2018 tariff announcement

Qin

Yang

Bai

2020

IJMF

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PurposeThis study examines the impact of the $60 billion tariff announcement of the US government on the Chinese exporting firms. In particular, it focuses on the firms whose revenues are highly dependent on the US economy.Design/methodology/approachThis study uses an experimental analysis and the event study methodology. The sample includes firms listed in mainland China and Hong Kong Stock Exchanges that have the highest revenues from exporting to the USA. The data are obtained from China Stock Market and Accounting Research (CSMAR) and DataStream.FindingsThe authors find that the tariff announcement has significantly negative impacts on stock performance both before and after the announcement, and the impacts are heterogeneous across all sample firms. For A shares listed in Mainland China, firms with more revenues from the US experience greater price drops on the announcement day, regardless of being in the targeted industry or not. But such finding is absent from H shares listed in Hong Kong. The authors also find that for all the firms, greater pricing power can alleviate the impacts of the tariff announcement.Research limitations/implicationsThe results provide implications to investors, policymakers and regulators on the further US-China cooperation in the future.Originality/valueThis is the first study documenting the heterogeneity of the impact of the tariff announcement and thus contributes to the prosperous studies on the varied firm-level responses in the Chinese stock market, and to the burgeoning literature by filling the gap of the financial market responses to the protectionist policy announcement.

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“…In the survey by Shleifer and Vishny (1997), it is found that investors may give up investing opportunities due to the limits of arbitrage, despite the presence of anomalies. The relationship between the effect of limits of arbitrage and anomalies has been found in some well‐known anomalies, including the book‐to‐market anomaly (Ali et al, 2003), asset growth anomaly (Lam & Wei, 2011), cash holdings effect (Li & Luo, 2016), and the idiosyncratic volatility puzzle (Duan et al, 2010; Han & Lesmond, 2011; Qu et al, 2018). Since anomaly‐driven trading involves holding and rebalancing portfolios, it is natural to test if the distress risk premium also carries costs that make arbitrage difficult.…”

Section: A Review Of the Literaturementioning

confidence: 99%

What drives the distress risk–return puzzle? A perspective on limits of arbitrage

Sha

Wang

2022

Int. J Fin Econ

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Empirical research has documented a negative relationship between distress risk and stock returns. This negative risk–return trade‐off, known as the distress puzzle, poses a challenge to asset pricing models. In this study, we provide a new explanation of the distress puzzle by considering the effect of arbitrage asymmetry. We find that the negative distress risk–return relation is stronger in stocks that have higher limits of arbitrage. The investors are virtually unable to short sell mispriced high distress risk stocks due to the low supply of lendable stocks from institutions and that arbitrage is costly. In addition, we show that the limits of arbitrage effect is distinct from liquidity effect in explaining the distress puzzle.

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Abnormal Returns and Idiosyncratic Volatility Puzzle: Evidence from the Chinese Stock Market

Cited by 14 publications

References 23 publications

Does the low-risk anomaly exist in the Indian equity market? A test using alternative risk measures

Does the low-risk anomaly exist in the Indian equity market? A test using alternative risk measures

Heterogeneous firm-level responses to the US 2018 tariff announcement

What drives the distress risk–return puzzle? A perspective on limits of arbitrage

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