Changing Income Structure, Ownership and Performance: An Empirical Analysis of Indian Banking Sector

This paper investigates the relationship between the changing patterns of bank’s source of income and risk adjusted performance. A database of 77 banks over the period of 1999 to 2004 is constructed for the 27 public sector banks, 22 private banks, 25 foreign banks and 3 cooperative banks to compare their change in income composition. Bank’s performance is measured by risk adjusted return on BIS risk allocated capital (RARORAC). To examine the relationship between ownership pattern and performance, we compare the difference between new generation private sector banks and foreign banks with their public sector and cooperative banks counterparts. We argue that in a competitive financial market in order to change the profitability drivers in banking, Indian banks need to improve their non-interest income and also augment risk adjusted interest income through better risk based pricing.Banking, Value creation and performance

Fuzzy clustering of univariate and multivariate time series by genetic multiobjective optimization

Given a set of time series, it is of interest to discover subsets that share similar properties. For instance, this may be useful for identifying and estimating a single model that may fit conveniently several time series, instead of performing the usual identification and estimation steps for each one. On the other hand time series in the same cluster are related with respect to the measures assumed for cluster analysis and are suitable for building multivariate time series models. Though many approaches to clustering time series exist, in this view the most effective method seems to have to rely on choosing some features relevant for the problem at hand and seeking for clusters according to their measurements, for instance the autoregressive coe±cients, spectral measures or the eigenvectors of the covariance matrix. Some new indexes based on goodnessof-fit criteria will be proposed in this paper for fuzzy clustering of multivariate time series. A general purpose fuzzy clustering algorithm may be used to estimate the proper cluster structure according to some internal criteria of cluster validity. Such indexes are known to measure actually definite often conflicting cluster properties, compactness or connectedness, for instance, or distribution, orientation, size and shape. It is argued that the multiobjective optimization supported by genetic algorithms is a most effective choice in such a di±cult context. In this paper we use the Xie-Beni index and the C-means functional as objective functions to evaluate the cluster validity in a multiobjective optimization framework. The concept of Pareto optimality in multiobjective genetic algorithms is used to evolve a set of potential solutions towards a set of optimal non-dominated solutions. Genetic algorithms are well suited for implementing di±cult optimization problems where objective functions do not usually have good mathematical properties such as continuity, differentiability or convexity. In addition the genetic algorithms, as population based methods, may yield a complete Pareto front at each step of the iterative evolutionary procedure. The method is illustrated by means of a set of real data and an artificial multivariate time series data set.Fuzzy clustering, Internal criteria of cluster validity, Genetic algorithms, Multiobjective optimization, Time series, Pareto optimality

Fuzzy clustering of univariate and multivariate time series by genetic multiobjective optimization

COMISEF Working Papers Series WPS-028 08/02/2010 URL: http://comisef.eu/files/wps028.pd

Quantum thermodynamics of a charged magneto-oscillator coupled to a heat bath

Explicit results for various quantum thermodynamic function (QTF) of a charged magneto-oscillator coupled to a heat bath at arbitrary temperature are demonstrated in this paper. Discernible expressions for different QTF in the two limits of very low and very high temperatures are presented for three popular heat bath models : Ohmic, single relaxation time and blackbody radiation. The central result is that the effect of magnetic field turns out to be important at low temperatures yet crucial at high temperatures. It is observed that the dissipation parameter, $\gamma$, and the cyclotron frequency, $\omega_c$, affect the decaying or rising behaviour of various QTF in just the opposite way to each other at low temperatures. In the high temperature regime, the effect of $\gamma$ is much pronounced than that of $\omega_c$.Comment: 26 Pages, 18 Figure

Effect of varietal performance on growth attributes and yields of lentil varieties under red and lateritic soil of West Bengal

A field experiment was conducted during rabi season of 2013-14 and 2014-15 at Agriculture Farm of Palli Siksha Bhavana (Institute of Agriculture), Visva-Bharati, Sriniketan, West Bengal to find out the varietal performance of different promising entries of lentil on growth attributes and yield. Tendifferent varieties viz. Subrata, Asha, Ranjan, HUL 57, BM 6, BM 7, PL 406, KLS 218, Moitree and PL 6 was studied in a randomized block design (RBD), replicated thrice. Different growth and yield attributes were measured in the experiment to find out the suitable variety of lentil for the red and lateritic soil zone of West Bengal. The lentil variety PL-406 showed maximum growth potentiality among the other varieties just followed by another long duration lentil variety KLS-218. The lentil variety PL406 showed maximum growth potentiality among the other varieties just followed by another long duration variety KLS 218. Highest grain yield was obtained from the variety Ranjan (789 kg ha-1 ) followed by the variety PL 406 (785 kg ha-1 ) and KLS 218 (783 kg ha-1 ) respectively. From the result of the present experiment, it can be concluded that the variety PL 406 gave maximum vegetative growth, whereas the variety Ranjan produce maximum yield and found most potential variety among other lentil varieties under red and lateritic soil of West Benga

Dissipative Tunneling in 2 DEG: Effect of Magnetic Field, Impurity and Temperature

We have studied the transport process in the two dimensional electron gas (2DEG) in presence of a magnetic field and a dissipative environment at temperature T. By means of imaginary time series functional integral method we calculate the decay rates at finite temperature and in the presence of dissipation. We have studied decay rates for wide range of temperatures -- from the thermally activated region to very low temperature region where the system decays by quantum tunneling. We have shown that dissipation and impurity helps the tunneling. We have also shown that tunneling is strongly affected by the magnetic field. We have demonstrated analytical results for all the cases mentioned above.Comment: 8 pages, 2 figure

Anisotropic Dark Energy and the Generalized Second Law of Thermodynamics

We consider a Bianchi type $I$ model in which anisotropic dark energy is interacting with dark matter and anisotropic radiation. With this scenario, we investigate the validity of the generalized second law of thermodynamics. It is concluded that the validity of this law depends on different parameters like shear, skewness and equation of state.Comment: 12 pages, accepted for publication in Phys. Scr. arXiv admin note: text overlap with arXiv:1008.0692 and arXiv:1106.241

Artificial Neural Network to predict mean monthly total ozone in Arosa, Switzerland

Present study deals with the mean monthly total ozone time series over Arosa, Switzerland. The study period is 1932-1971. First of all, the total ozone time series has been identified as a complex system and then Artificial Neural Networks models in the form of Multilayer Perceptron with back propagation learning have been developed. The models are Single-hidden-layer and Two-hidden-layer Perceptrons with sigmoid activation function. After sequential learning with learning rate 0.9 the peak total ozone period (February-May) concentrations of mean monthly total ozone have been predicted by the two neural net models. After training and validation, both of the models are found skillful. But, Two-hidden-layer Perceptron is found to be more adroit in predicting the mean monthly total ozone concentrations over the aforesaid period.Comment: 22 pages, 14 figure