2020
DOI: 10.1016/j.ememar.2019.03.007
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Arbitrage risk and the cross-section of stock returns: Evidence from China

Abstract: We demonstrate that arbitrage risk, constructed using three measuresnoise trader risk, trading cost and information uncertaintycan predict the return of stocks cross-sectionally in China. The findings are broadly consistent even when out-of-sample tests are conducted using the Fama-MacBeth cross-sectional regression approach. We also construct hypothetical portfolios using the information arising from arbitrage risk and find the existence of abnormal returns which is robust to the use of various portfolios con… Show more

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Cited by 3 publications
(2 citation statements)
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“…In each case, changes in adjusted CSR and CSiR measures are denoted as ∆CSR i,t and ∆CSiR i,t . Following past studies [36,64], this study accounts for observable heterogeneity with many control variables found to influence firm performance, including change in ∆LnSize i,t (the logarithm of total assets) [13,52], change in ∆Leverage i,t (total debt to common equity ratio; D/E ratio) [20,52], change in ∆Profit i,t [36] (operating income divided by total assets), change in ∆Liquidity i,t (current ratio, as calculated by dividing book value of current assets by debt in current liabilities) [13,14], changes in ∆Turnover i,t (dividing the total number of shares traded over month t by the number of shares outstanding for the period) [65], change in ∆Idiosy i,t (the standard deviation of the residuals from the fitted market models, estimated by the rolling regression with period t − 1 and including the past 36 months of returns) [30] and the lagged dependent variable (∆Performance i,t ) [66] to control for the potential lagged effect of stock performance changes. Moreover, we include the industry dummies (based on all four-digit Standard Industrial Classification (SIC) code industries) and monthly time dummies to account for unobserved heterogeneity across industries and time, respectively.…”
Section: Empirical Modelsmentioning
confidence: 99%
“…In each case, changes in adjusted CSR and CSiR measures are denoted as ∆CSR i,t and ∆CSiR i,t . Following past studies [36,64], this study accounts for observable heterogeneity with many control variables found to influence firm performance, including change in ∆LnSize i,t (the logarithm of total assets) [13,52], change in ∆Leverage i,t (total debt to common equity ratio; D/E ratio) [20,52], change in ∆Profit i,t [36] (operating income divided by total assets), change in ∆Liquidity i,t (current ratio, as calculated by dividing book value of current assets by debt in current liabilities) [13,14], changes in ∆Turnover i,t (dividing the total number of shares traded over month t by the number of shares outstanding for the period) [65], change in ∆Idiosy i,t (the standard deviation of the residuals from the fitted market models, estimated by the rolling regression with period t − 1 and including the past 36 months of returns) [30] and the lagged dependent variable (∆Performance i,t ) [66] to control for the potential lagged effect of stock performance changes. Moreover, we include the industry dummies (based on all four-digit Standard Industrial Classification (SIC) code industries) and monthly time dummies to account for unobserved heterogeneity across industries and time, respectively.…”
Section: Empirical Modelsmentioning
confidence: 99%
“…Our second motivation derives from the high volatility of the Chinese stock market. The rapid development of China's stock market has drawn increasing attention from international financial investors and academic researchers (see, e.g., Hammoudeh et al, 2014;Huang et al, 2017;Jayasuriya, 2011;Y. E. Lin et al, 2020;Narayan et al, 2014;Narayan & Sharma, 2016;Xu et al, 2019;Zhou et al, 2012).…”
mentioning
confidence: 99%