Using industry portfolios as test assets and a battery of statistical tests, we study if the informational efficiency of stock prices has declined after the COVID-19 crisis began. The results suggest that the predictability of stock returns in some industries has increased during the COVID-19 period. Markets appear to have become less informationally efficient during the COVID-19 crisis. JEL Classification Code: C58, G01, G10, G14.

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Purpose The purpose of this paper is to compare the performance of various multifactor asset pricing models across ten emerging and developed markets. Design/methodology/approach The general methodology to test asset pricing models involves regressing test asset returns (left-hand side assets) on pricing factors (right-hand side assets). Then the performance of different models is evaluated based on how well they price multiple test assets together. The parameters used to compare relative performance of different models are their pricing errors (GRS statistic and average absolute intercepts) and explained variation (average adjusted R2). Findings The Fama-French five-factor model improves the pricing performance for stocks in Australia, Canada, China and the USA. The pricing in these countries appears to be more integrated. However, the superior performance in these four countries is not consistent across a variety of test assets and the magnitude of reduction in pricing errors vis-à-vis three- or four-factor models is often economically insignificant. For other markets, the parsimonious three-factor model or its four-factor variants appear to be more suitable. Originality/value Unlike most asset pricing studies that use test assets based on variables that are already used to construct RHS factors, this study uses test assets that are generally different from RHS sorts. This makes the tests more robust and less biased to be in favour of any multifactor model. Also, most international studies of asset pricing tests use data for different markets and combine them into regions. This study provides the evidence from ten countries separately because prior research has shown that locally constructed factors are more suitable to explain asset prices. Further, this study also tests for the usefulness of adding a quality factor in the existing asset pricing models.

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Purpose The purpose of this paper is to explore whether stock selection strategies based on four fundamental quality indicators can generate superior returns compared to overall market. Design/methodology/approach The sample of stocks comprises the constituents of BSE-500 index, which is a broad based index consisting of highly liquid stocks from all 20 major industries of the Indian economy. Portfolios are constructed on the basis of quality indicator rankings of companies and the returns of these portfolios are compared with the overall market. Excess returns on quality based portfolios are also determined using OLS regressions of quality portfolio returns on market, size, value and momentum factor returns. Findings The results suggest that two of the four quality strategies, namely Grantham Quality indicator and Gross Profitability have generated superior returns after controlling for market returns as well as common anomalies such as size, value and momentum. Combining value strategies with quality strategies do not yield any significant gains relative to quality only strategies. Practical implications For investors looking to invest in the Indian stock market for a long term, this study provides evidence on the performance of some fundamental indicators that can help predict long run stock performance. The findings suggest that investors can distinguish between high-performing and low-performing stocks based on stock quality indicators. Originality/value This is the first such study to look into the performance of quality investing in the Indian stock market. As most quality investing studies have been focussed on developed economies, this paper provides out-of-sample evidence for quality investing in the context of an emerging market.

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Do factor investment strategies that have generated superior returns in the past continue to do so out-of-sample? To test this hypothesis, I check the performance of nine factor-based indices of the National Stock Exchange (NSE) of India. My results show that the performance of most indices falls considerably in the out-of-sample period, i.e. the period after the launch of an index. The results hold for absolute as well as excess and risk-adjusted returns. In additional tests, I find that none of the factor strategies generates significant alpha after controlling for standard factors such as size, value, and momentum.