Abstract a valuable tool for classification, decision support, financial

Abstract

From the last few years
artificial neural network is playing a very important role in business analytics
and applications. The study of application of artificial neural network in the
field of marketing and business reveals that most of the work is done on the
marketing segmentation, financial distress and bankruptcy problems, stock price
forecasting, etc. In this segmentation is analyzed and the results of the
classification are reported by using the artificial neural network method.

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!


order now

Introduction

Artificial neural
network is the computing system based on the biological neural networks that
constitutes the human brain. It can be explain well with an example of image
recognition, to recognize a cat they do it by using their prior knowledge about
the cats, i.e. they have furs, tails, whiskers, cat like face etc. The
characteristics of artificial neural networks such as efficiency, robustness
and adaptability make them a valuable tool for classification, decision
support, financial analysis or credit scoring made its utilization in various
fields for example scientific fields as well as in many business applications. The
study of the role of artificial neural network in business has seen that most
of the research was done in the bankruptcy predictions and stock forecasting.Also
studied the work of it in the collection of data and analyzing of these
data.  There are also various disciplines
that have been studied including accounting, costs monitoring, customer
analysis, finance, marketing or sales, manufacturing, process optimization, engineering
or operational research have not been included. In the second paper we have
studied the application of ANN in segmentation.

Research
Methodology

To study this we have
use the keywords “literature review” “artificial neural network in marketing””business”,
“finance”, “corporate”, “stocks”, “capital”, “costs”, “financial analysis”,
“accounting”, “bankruptcy”, “exchange rates”, “financial distress”, “inflation”,
“marketing”, “customers”, “bonds”and “segmenatation”.

In the second
paper to study the application of ANN in segmentation Expert systems (ES) with

application and
Information system are the most common approach.

 

Application
area

Neural networks
captures data by using iterative algorithms by comparing their synaptic
weights. But the main disadvantage was that it considers only the data with
large weights and do not consider data with small data because small data do
not provide significant result. Primarily due to unavailability of data
researchers use artificial data. Application of neural network in the field of
business is very significant because it is use to extract valuable information
from complex, nonlinear and noisy data. The applications of neural network in
business are as follows:

·        
Auditing and accounting

·        
Cost monitoring

·        
Credit scoring

·        
Customers metrics

·        
Decision support

·        
Derivatives

·        
Exchange and interest rates

·        
Financial analysis

·        
Financial distress and bankruptcy

·        
Fraud analysis

·        
Inflation

·        
Marketing

·        
 Sales

·        
Shares and bonds

In the second
paper we have studied that in market segmentation methods can be largely
classified based on two criteria for the four categories: a priori or post hoc,
and descriptive or predictive statistical methods. When the type and number of
segments are determined in advance by the researcher then the apriority
approach is used and when the type and number of segments are determined based
on the results of data analyses then the post hoc approach is used. The
post-hoc methods are relatively powerful and frequently used in practice. A single
set of segmentation bases that has no distinction between dependent and
independent variables are related with the descriptive methods. When one set
consists of dependent variables to be explained or predicted by a set of
independent variables then the predictive methods are applied.

There are four
major classes of traditional algorithms for conducting traditional post hoc
segmentation studies: Cluster analysis, Correspondence analysis, Search
procedures, and Q-type factor analysis. Among clustering methods, the K-means
method is the most frequently used. An unsupervised neural network of the
artificial neural networks (ANNs) where the

Outcomes are not
a priori having been recently applied to a wide variety of business areas. The

Kohonan
Self-Organizing Map of unsupervised ANN used in clustering for large and
complex

data.

 

Neural
networks

In the application of
neural networks in business almost all types of neural networks are used. But
there are cases in which uncertain work on neural network is done. So there is
additional work should be done on these neural network so that we can get
outcomes.

The most popular neural
networks used in the study was multilayer feed -forward neural networks in
which neurons are organized into series of layers and information signal flows
through the network solely in one direction, from the input layer to the output
layer.

Classification
of framework

Cluster analysis is a
common tool for market segmentation. Conventional research usually employs the
multivariate analysis procedures. Comparison of three clustering methods were
done and proposed that SOM performs better clustering than the other
conventional methods. A data mining associatiation rule based on SOM has been
developed and applied to a sample of sales records from database for market
fragmentation. It was found that NN models outperforms the multinomial logut
model in determining the most profitable time in a purchasing history to
classify and target prospective consumers new to their category. An ANN guided
by genetic algorithms (GAs) successfully to target households. Targeting of
customer segments with tailored promotional activities is an important aspect
of customer relationship management. Application of the SOM networks to a
consumer data set the research established that the SOM network performs better
than the two-step procedure that combines factor analysis and K-means cluster
analysis in uncovering market segments. The proposed classification scheme is
consisting of the following phases:

·        
Online
database search

·        
Initial
classification by the researcher

·        
Verification of the classification
result

The following 14 types
of ANN algorithms are found to be applied on market segmentation:

i) NN algorithm,

 ii) Meta Heuristic tools,

 iii) ARNN(association reasoning neural
network),

iv) ART2

v) Bayesian NN,

vi) Back Propagation
NN,

 vii) Data Mining,

 viii)hybrid fuzzy tools,

 ix) Genetic Algorithm(GA),

 x) hopefield NN,

xi) hybrid NN,

 xii) SelfOrganizing Map(SOM),

 xiii) support vector machine (SVM)

 xiv) Vector Quantization.

LEARNING
ALGORITHM

The process by which
neural network updates its free parameters to capture the patterns in the
presented sample is called the learning. The most common algorithm used in
reviewing business applications was the backpropagation learning. This method
is generally used because of its simplicity, universality and good availability
in softwares.

 

Hybridization

The group of hybrid
networks may be divided into two categories depending upon the methodology
used:

 (a) dealing with learning process,

 (b) dealing with net-work architecture.

 

Benchmark
method

 The most common benchmark methods identified
in our research are discriminant analysis, linear regression, logit and ARIMA.
The significant advantage of using this method is its transparency and
capability to comprehensibly interpret received results.

Conclusions

In last few decades
artificial neural network has progressed very much. It has various applications
in business fields but there were so much less papers were published in this field.  In our study the focus is on financial
distress and bankruptcy analysis, stock price prediction, and credit scoring.
It is interesting that the average number of financial analysis and derivatives
articles stayed approximately the same throughout the examined period. On the
other hand, research on shares, marketing, financial distress, and credit
scoring has significantly increased compared to the early years of our survey.
After using neural networks in the business fields there were also the fields
which were not investigated. This is true not only for the qualitative data but
it also includes the quantative data just like cost, debt financing and bonds.

In hybridization
secondary methods perform much better than the traditional feedforward networks
trained by gradient based techniques. The specific hybrid networks might work
well only for particular tasks, our survey suggests that proper integration of
met heuristic methods into the neural network methodology might be a key for
achieving the optimal performance.

Neural networks have
been successfully applied in wide range of business tasks and were able to
detect complex and nonlinear relationships without requiring any specific
assumptions about the distribution or characteristics of the data. There is lack
of formal background and the explanatory abilities are the two essential problems
that have to be resolved to improve the neural network business studies. The
further research therefore should focus on universal guidelines and general
methodology for the setting of control variables, selection of hidden layers
and overall design of the topology, since the quality of models reviewed in
this study considerably depended on experiences of the researchers. Moreover,
robust measures that could assess the relevance of individual explanatory
variables are very desirable, since researchers are currently still careful
with interpretation of their results and perform their validation using
conventional methods. We are convinced that research on artificial neural
networks in business has still much to offer. With their undisputed advantages,
general availability of data and increasing user-friendliness of soft-ware
packages, neural networks will surely attract more authors and offer additional
possibilities for applications.

Application of
artificial neural network techniques in market segmentation is an emerging
inclination in the industry and academia. It has paying the attention of
researchers, industry practitioners and academics. The main aim is to give a
research review on the application of neural network in the market segmentation
domain and techniques which are most often used. While this review work cannot
claim to be exhaustive, but it presents reasonable insights and shows the
prevalence of research on this area under discussion.

·        
The majority of the reviewed articles 34.38%
(22articles) and 21.88% (14 articles) are related common neural network
algorithms.

·        
Thus a trend of ANN research to
segmentation is more obvious from the articles published in the kind of journal
related to expert system development.

·        
These articles could provide insight to
organization strategists on the familiar artificial neural network practices used
in market segmentation.

·        
There are relatively fewer articles with
the metaheuristic, ART2, data mining, Genetic Algorithm and fuzzy algorithms.
Despite the fewer number of articles related to the above category of
artificial neural network application to market segmentation, it does not mean
the application of artificial neural network in this aspect is less mature than
in the others. Applications of those algorithms in other domains, such as
clustering or classification, may also be applied in segmentation if they
possess the same purpose of analysing the distinctiveness of customers/market.

·        
The k-means clustering model is the most
commonly applied model in segmentation by partitioning a large market into the
smaller groups or the clusters of customers.

·        
In order to maximize an organization’s
profits through segmentation, strategists have to both segment the market and
thus increase the profitability of the organisation.