2015),"Perishable food supply chain quality (PFSCQ): A structured review and implications for future research"If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to evaluate the profitability of investment strategies based on past price changes and trading volumes. Design/methodology/approach -Data are employed from January 1998 to December 2011 for select emerging markets. Portfolios are formed on the basis of past information on prices and/or volumes. Unrestricted and risk adjusted returns for sample portfolios are analyzed. The risk models employed in study are Capital Asset Pricing Model (CAPM), Fama-French (F-F) Model and Fama-French augmented models. Findings -Price momentum patterns are observed for Brazil, India, South Africa and South Korea, while there are reversals in Indonesia and China. Low-volume stocks outperform high-volume stocks for all sample countries except China. Further, volume and price based bivariate strategies do a better job than univariate strategies in case of India, South Africa and South Korea. The past price and volume patterns in stock returns are not fully explained by CAPM as well as the F-F Model. Price and volume momentum factors do play a role in explaining some of these return patterns. Finally, the unexplained returns seem to be an outcome of investor under or overreaction to past information. The sources of price and volume momentum seem to be partly risk based and partly behavioral. Originality/value -The study analyzes combined role of price and volume in portfolio formation with post holding analysis. The work is useful for global portfolio managers, policy makers, market regulators and the academic community. The study contributes to asset pricing and behavioral finance literature for emerging markets.

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We combine corporate attributes and fundamental factors for evolving different investment strategies using data from 200 companies listed in the National Stock Exchange (NSE) from 2005 to 2018. The results indicate the existence of equity market anomalies based on size, volume, earnings, cash flow variability, asset growth, price momentum, price-to-book ratio and profitability. The performance of trading strategies is sensitive to portfolio construction procedure, that is, forming 5/10/20 portfolios. Bivariate strategies generally perform better than univariate strategies in the Indian context. On an overall basis, the size-based strategy performs best with a mean excess return of 3.63 per cent per month. We further find that corporate fundamentals such as profitability, operating efficiency, liquidity, solvency, innovation and entry barriers help in filtering poor future performers that may have been recommended by attributes-based strategy. Our filtered portfolios based on firm attributes and corporate fundamentals outperform unfiltered portfolios, and their returns are not explained by multi-factor performance benchmarks.

PurposeThis study attempts to identify fundamental determinants of bond ratings for non-financial and financial firms. Further the study aims to develop a parsimonious bond rating model and compare its efficacy across statistical and range of machine learning methods in the Indian context. The study is motivated by the insufficiency of prior work in the Indian context.Design/methodology/approachThe authors identify the critical determinants of non-financial and financial firms using multinomial logistic regression. Various machine learning and statistical methods are employed to identify the optimal bond rating prediction model. The data cover 8,346 bond issues from 2009 to 2019.FindingsThe authors find that industry concentration, sales, operating leverage, operating efficiency, profitability, solvency, strategic ownership, age, firm size and firm value play an important role in rating non-financial firms. Operating efficiency, profitability, strategic ownership and size are also relevant for financial firms besides additional determinants related to the capital adequacy, asset quality, management efficiency, earnings quality and liquidity (CAMEL) approach. The authors find that random forest outperforms logit and other machine learning methods with an accuracy rate of 92 and 91% for non-financial and financial firms.Practical implicationsThe study identifies important determinants of bond ratings for both non-financial and financial firms. The study interalia finds that the random forest technique is the most appropriate method for bond ratings predictions in India.Social implicationsBetter bond ratings may mitigate corporate defaults.Originality/valueUnlike prior literature, the study identifies determinants of bond ratings for both non-financial and financial firms. The study also experiments with modern machine learning techniques besides the traditional statistical approach for model building in case of relatively under researched market.

Prior research describes value and growth characteristics on the same continuum. We attempt to test this for the Indian market. Similar to past studies, our results confirm the presence of value premium. But unlike previous literature, superior profits in growth strategy are also observed. This leads us to believe that value and growth are different dimensions. Value premium may be explained by investor overreaction. Whereas, growth premium arises due to future growth potential of high growth stocks.Negative correlation is observed between value and growth premiums which can be used by investors for achieving time diversification. We find that higher profits can be achieved by combining value and growth. The returns on univariate value or growth strategies are explained by either Capital Asset Pricing Model or Fama-French three factor model. The returns on bivariate strategies based on both value and growth remain unexplained even by Fama-French five factor model level.