The problem of automatic and accurate forecasting of time‐series data has always been an interesting challenge for the machine learning and forecasting community. A majority of the real‐world time‐series problems have non‐stationary characteristics that make the understanding of trend and seasonality difficult. The applicability of the popular deep neural networks (DNNs) as function approximators for non‐stationary TSF is studied. The following DNN models are evaluated: Multi‐layer Perceptron (MLP), Convolutional Neural Network (CNN), and RNN with Long Short‐Term Memory (LSTM‐RNN) and RNN with Gated‐Recurrent Unit (GRU‐RNN). These DNN methods have been evaluated over 10 popular Indian financial stocks data. Further, the performance evaluation of these DNNs has been carried out in multiple independent runs for two settings of forecasting: (1) single‐step forecasting, and (2) multi‐step forecasting. These DNN methods show convincing performance for single‐step forecasting (one‐day ahead forecast). For the multi‐step forecasting (multiple days ahead forecast), the methods for different forecast periods are evaluated. The performance of these methods demonstrates that long forecast periods have an adverse effect on performance.
Over the last few years, the Indian banking sector has been ceaselessly adding increasingly huge piles of non‐performing assets (NPAs), also known as bad loans, to its balance sheet. As of March 2018, gross NPAs for all scheduled commercial banks (SCBs) stood at over ₹10 trillion, compared to merely 4 years back, that is, March 2014, when they were almost one‐fourth of this. Aimed at tackling the NPA problem, the Insolvency and Bankruptcy Code (IBC) was enacted in 2016 by the Parliament of India, which provides for initiation and quick resolution of bankruptcy proceedings against defaulters. In 2017, the RBI identified 12 Mega‐defaulters which accounted for nearly INR 1.75 trillion of the total NPAs of Indian banks. Targeting the NPA problem at the policy level requires extensive research and analysis in order to come up with effective action plans. This study tries to analyse NPAs of Indian banks using panel data regression models and identify their key determinants. It also examines the relative severity of the NPA problem in case of public banks as compared to private banks.
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