Purpose The purpose of this paper is to examine the influence of ownership structure and dividend payouts over firm’s profitability, valuation and idiosyncratic risk. The authors further investigate if corporate performance is sector dependent. Design/methodology/approach The study employs signaling and bankruptcy theories to evaluate the influence of ownership structure and dividend payout over a firm’s corporate performance. The authors use a panel regression approach to measure the performance of family owned firms against that of widely held firms. Findings The study confines to firms operating out of emerging markets. The results show that family owned firms are dominant with concentrated ownership. The management pays lower dividend leading to lower valuation and higher idiosyncratic risk. The study further illustrates that family ownership concentration and family control both influence firm performance and level of risk. The findings indicate that information asymmetry and under diversification lead to increased idiosyncratic risk, resulting in the erosion of firm’s value. Results also confirm that firms paying regular dividends are less risky and, hence, command a valuation premium. Originality/value The evidence supports the proposition that information asymmetry plays a significant role in explaining dividend payouts pattern and related impacts on corporate performance. The originality of the paper lies in factoring idiosyncratic risk while explaining profitability and related valuation among emerging market firms.

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This paper develops ensemble machine learning models (XGBoost, Gradient Boosting, and AdaBoost in addition to Random Forest) for predicting stock returns of Indian banks using technical indicators. These indicators are based on three broad categories of technical analysis: Price, Volume, and Turnover. Various error metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), Root-Mean-Squared-Error (RMSE) have been used to check the performance of the models. Results show that the XGBoost algorithm performs best among the four ensemble models. The mean of absolute error and the root-mean-square -error vary around 3–5%. The feature importance plots generated by the models depict the importance of the variables in predicting the output. The proposed machine learning models help traders, investors, as well as portfolio managers, better predict the stock market trends and, in turn, the returns, particularly in banking stocks minimizing their sole dependency on macroeconomic factors. The techniques further assist the market participants in pre-empting any price-volume action across stocks irrespective of their size, liquidity, or past turnover. Finally, the techniques are incredibly robust and display a strong capability in predicting trend forecasts, particularly with any large deviations.

Background: “Malaria and malnutrition are closely related in the months of hunger gap when malnutrition is at its peak often coincides with rainy season when the number of malaria cases shoot up. The disease combines in a vicious circle. Children sick with malaria are more likely to become dangerously malnourished”. Severely malnourished children with malaria infection may have no fever, or be hypothermic. Proactive screening for malaria in severely malnourished children is needed even if the child has no symptoms of malaria. The objectives of the study were to estimate prevalence of “malaria and malnutrition” co-existence in under 5 children of tribal dense regions and to determine if any significant difference between this co-existent condition against the disease alone.Methods: Eight villages were selected based on their inaccessibility and demography spread across Bamnipal and Sukinda region. Malaria testing using antigen based RDK and nutritional assessment using MUAC tapes were conducted in of 6 months to <5 yrs children.Results: A total of 224 children of under 5 yrs age group were screened. 50.4% of them were suffering from malaria, 38% of the children were at risk or suffering from severe acute malnutrition. Of the 113 children with malaria, 45% were having malnutrition. Out of 86 malnourished children 59% were diagnosed with malaria. 22.7% of children were found to have malaria and malnutrition together.Conclusions: Malaria and Malnutrition are co-existent and synergistic to each other.

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