Purpose This study aims to analyse the systemic risk emitted by all publicly listed commercial banks in a key emerging economy, India. Design/methodology/approach The study makes use of the Tobias and Brunnermeier (2016) estimator to quantify the systemic risk (ΔCoVaR) that banks contribute to the system. The methodology addresses a classification problem based on the probability that a particular bank will emit high systemic risk or moderate systemic risk. The study applies machine learning models such as logistic regression, random forest (RF), neural networks and gradient boosting machine (GBM) and addresses the issue of imbalanced data sets to investigate bank’s balance sheet features and bank’s stock features which may potentially determine the factors of systemic risk emission. Findings The study reports that across various performance matrices, the authors find that two specifications are preferred: RF and GBM. The study identifies lag of the estimator of systemic risk, stock beta, stock volatility and return on equity as important features to explain emission of systemic risk. Practical implications The findings will help banks and regulators with the key features that can be used to formulate the policy decisions. Originality/value This study contributes to the existing literature by suggesting classification algorithms that can be used to model the probability of systemic risk emission in a classification problem setting. Further, the study identifies the features responsible for the likelihood of systemic risk.

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The present investigation aimed to study the effect of biofortified wheat (WB 2) straw-based diet on lactating Murrah buffaloes. Twelve Murrah buffaloes were divided into two groups i.e., Control (T0) and Treatment (T1) based on body weight, parity, and previous milk record. Feeding was done as per ICAR (2013) standard for 90 days. Animals of the control group were fed conventional wheat straw, oats fodder, and concentrate mixture in the ratio 50:15:35 (on Dry Matter basis), respectively, whereas, animals of the treatment group were fed biofortified wheat straw, oats fodder and concentrate mixture in the same ratio. Nutrient analysis revealed minor differences between biofortified and conventional wheat straws. There was no significant difference (P > 0.05) among both the groups when means were compared for daily dry matter intake (T0: 15.70 ± 0.17 Kg/day/animal versus T1:15.75 ± 0.12 Kg/day/animal). Digestibility of Dry Matter (DM), Crude Protein (CP), Ether Extract (EE), Neutral Detergent Fiber (NDF), Acid-Detergent Fiber (ADF), and Organic Matter (OM) did not differ (P > 0.05) between groups. There was no significant difference (P > 0.05) in milk yield between the two groups (T0: 7.65 ± 0.1 Kg/day/animal vs T1: 7.75 ± 0.08 Kg/day/animal). Similarly, there was no significant difference (P > 0.05) found in Somatic Cell Count (SCC) and milk composition (Fat, SNF, Lactose, Protein) when analysed at weekly intervals. Mineral analysis of blood plasma and milk carried out at monthly intervals showed similar concentrations in both groups. Based on this study, it can be inferred that despite the biofortification of wheat variety WB 2, the zinc and iron concentrations in the straw were similar to conventional wheat straw. Hence, the straw from WB 2 variety had no significant impact on milk quality and production.

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