2,822 research outputs found
Determinants of net interest margin under regulatory requirements: an econometric study
Using data for the period 1995-96 to 1999-2000, this paper seeks to identify the factors influencing spreads of Scheduled Commercial Banks in India. Among the explanatory variables, we incorporate, in addition to the standard set of variables, regulatory requirement variables. Our analysis reveals that (i) size does not necessarily correlate with higher spread, and (ii) higher fee income enables banks to tolerate lower spreads. With regard to regulatory requirement variables, it is found that (i) capital plays an important role in affecting spreads of public sector banks, and (ii) non-performing assets is uniformly important across all bank groups in influencing spreads.Net interest margin; regulatory requirements; banking; India
Learning to detect an oddball target with observations from an exponential family
The problem of detecting an odd arm from a set of K arms of a multi-armed
bandit, with fixed confidence, is studied in a sequential decision-making
scenario. Each arm's signal follows a distribution from a vector exponential
family. All arms have the same parameters except the odd arm. The actual
parameters of the odd and non-odd arms are unknown to the decision maker.
Further, the decision maker incurs a cost for switching from one arm to
another. This is a sequential decision making problem where the decision maker
gets only a limited view of the true state of nature at each stage, but can
control his view by choosing the arm to observe at each stage. Of interest are
policies that satisfy a given constraint on the probability of false detection.
An information-theoretic lower bound on the total cost (expected time for a
reliable decision plus total switching cost) is first identified, and a
variation on a sequential policy based on the generalised likelihood ratio
statistic is then studied. Thanks to the vector exponential family assumption,
the signal processing in this policy at each stage turns out to be very simple,
in that the associated conjugate prior enables easy updates of the posterior
distribution of the model parameters. The policy, with a suitable threshold, is
shown to satisfy the given constraint on the probability of false detection.
Further, the proposed policy is asymptotically optimal in terms of the total
cost among all policies that satisfy the constraint on the probability of false
detection
Learning Opposites Using Neural Networks
Many research works have successfully extended algorithms such as
evolutionary algorithms, reinforcement agents and neural networks using
"opposition-based learning" (OBL). Two types of the "opposites" have been
defined in the literature, namely \textit{type-I} and \textit{type-II}. The
former are linear in nature and applicable to the variable space, hence easy to
calculate. On the other hand, type-II opposites capture the "oppositeness" in
the output space. In fact, type-I opposites are considered a special case of
type-II opposites where inputs and outputs have a linear relationship. However,
in many real-world problems, inputs and outputs do in fact exhibit a nonlinear
relationship. Therefore, type-II opposites are expected to be better in
capturing the sense of "opposition" in terms of the input-output relation. In
the absence of any knowledge about the problem at hand, there seems to be no
intuitive way to calculate the type-II opposites. In this paper, we introduce
an approach to learn type-II opposites from the given inputs and their outputs
using the artificial neural networks (ANNs). We first perform \emph{opposition
mining} on the sample data, and then use the mined data to learn the
relationship between input and its opposite . We have validated
our algorithm using various benchmark functions to compare it against an
evolving fuzzy inference approach that has been recently introduced. The
results show the better performance of a neural approach to learn the
opposites. This will create new possibilities for integrating oppositional
schemes within existing algorithms promising a potential increase in
convergence speed and/or accuracy.Comment: To appear in proceedings of the 23rd International Conference on
Pattern Recognition (ICPR 2016), Cancun, Mexico, December 201
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