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Purpose -The purpose of this paper is to provide an empirical evaluation of the impact of infrastructure development on industry-level productivity, output and efficiency in India over the period 1994-2006. Design/methodology/approach -The first stage, estimated total factor productivity (TFP) and technical efficiency of eight important industries. In the next stage, the effects of infrastructure were estimated on TFP, output, labor productivity and technical efficiency. Fully modified ordinary least squares procedure was utilized to generate consistent estimates of the relevant panel variables in the cointegrated frameworks. Findings -The results of this study are mixed. On the one hand, TFP, output and technical efficiency appear to be positively and largely affected by infrastructure. On the other hand, the effect of infrastructure on the labor productivity is somewhat negligible. In addition, the effects of information and communication technology on the industrial performance are found to be very weak. Originality/value -This is the first study of its kind in the related literature which attempts to investigate the role of infrastructure in industrial performance, using alternative frameworks, namely, growth accounting and production function approach. The paper uses appropriate techniques to account for the potential endogeneity of regressors as well as for multicollinearity among infrastructure variables.

PurposeThe main aim of the study is to identify some critical microeconomic determinants of financial distress and to design a parsimonious distress prediction model for an emerging economy like India. In doing so, the authors also attempt to compare the forecasting accuracy of alternative distress prediction techniques.Design/methodology/approachIn this study, the authors use two alternatives accounting information-based definitions of financial distress to construct a measure of financial distress. The authors then use the binomial logit model and two other popular machine learning–based models, namely artificial neural network and support vector machine, to compare the distress prediction accuracy rate of these alternative techniques for the Indian corporate sector.FindingsThe study’s empirical results suggest that five financial ratios, namely return on capital employed, cash flows to total liability, asset turnover ratio, fixed assets to total assets, debt to equity ratio and a measure of firm size (log total assets), play a highly significant role in distress prediction. The study’s findings suggest that machine learning-based models, namely support vector machine (SVM) and artificial neural network (ANN), are superior in terms of their prediction accuracy compared to the simple binomial logit model. Results also suggest that one-year-ahead forecasts are relatively better than the two-year-ahead forecasts.Practical implicationsThe findings of the study have some important practical implications for creditors, policymakers, regulators and other stakeholders. First, rather than monitoring and collecting information on a list of predictor variables, only six most important accounting ratios may be monitored to track the transition of a healthy firm into financial distress. Second, our six-factor model can be used to devise a sound early warning system for corporate financial distress. Three, machine learning–based distress prediction models have prediction accuracy superiority over the commonly used time series model in the available literature for distress prediction involving a binary dependent variable.Originality/valueThis study is one of the first comprehensive attempts to investigate and design a parsimonious distress prediction model for the emerging Indian economy which is currently facing high levels of corporate financial distress. Unlike the previous studies, the authors use two different accounting information-based measures of financial distress in order to identify an effective way of measuring financial distress. Some of the determinants of financial distress identified in this study are different from the popular distress prediction models used in the literature. Our distress prediction model can be useful for the other emerging markets for distress prediction.

The study attempts to evaluate if there are any systematic patterns in stock returns for the Indian market. The empirical findings reveal that there is a reversal in long-term returns, once the short-term momentum effect has been controlled by maintaining a one year gap between portfolio formation period and the portfolio holding period. A contrarian strategy based on long-term past returns provides moderately positive returns. Further, there is a continuation in short-term returns and a momentum strategy based on it provides significantly positive payoffs. The results in general are in conformity with those for developed capital markets such as the US.

In this paper we investigate the presence of the following asset pricing anomalies viz. size, value, momentum, liquidity, accruals, profitability and net stock issues in India. Size effect is the strongest with a difference of 4.4 % per month between small and big stock returns. A positive relationship is reported between accruals, stock issues and returns and a negative relation between profitability and returns which is in contrast to prior research. CAPM is unable to explain these anomalies with the exception of net stock issues. The Fama French (FF) model is able to capture value, profitability and accruals. While liquidity anomaly is explained by a liquid augmented FF model, the sector and earnings momentum factors do not contribute significantly towards explaining returns. Size and short term momentum are persistent and hence continue to pose challenge to rational asset pricing in India. Our findings shall be highly useful for investment analysts and portfolio managers. The research contributes to asset pricing literature especially for emerging markets.