Neural Network in Financial Analysis

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Neural Networks in Financial

Analysis

Rajesh Amradi(08005041)

Nikhil Prakash (09026015)

Under the guidance of

Prof. Pushpak Bhattacharya

Outline• Introduction to Neural Networks• Neural Networks in Finance• Time Series Analysis• Stock Market Analysis• Capital Budgeting and Risk • NN Model for Bankruptcy

▫ Variables▫ Model

• Bond Credit Rating▫ Problem Statement

▫ Variables and Models▫ Comparative Study

• Modifications in Neural Networks▫ Wavelet Neural Networks▫ Fuzzy Wavelet Neural Networks

Neural Networks and similarities with working of human

Why use Neural Networks?

• Interest in Neural Networks stems primarily fromits nonlinear models that can be trained to mappast and future values of input output

relationship• Capability to recognize pattern and speed of its

techniques to accurately solve complex processesin many applications

• Help to charactize relationships via a nonlinearnon-parametric inference technique

Why use Neural Networks(Contd.)

• Usage of these networks distinguished by four types of applications▫ Classification of Input Stream

▫ Association of output given sectors of input grouping

▫ Codification of input by producing output within a reduceddimensional subspace

▫ Simulation of output from input relationships and interconnections

• Added advantage of being able to establish a 'trainingphase'

• Can generalize results and lead to logical and unforeseenconclusions through the model

Neural Networks in Finance

• NNs are trained without restriction of a model todeprive parameters and discover relationships

• Driven and shaped solely by the nature of the data

• Has profound implications and applicability tothe finance field

Stock Market Analysis

• Stock pricing is an important aspect of financialeconomics

• Dividend Discount Model(DDM) applied to

neural networks in order to verify if the entitiesare relatively stable

• Also to verify if prices are efficient and fair forstocks

• DDM assumes that the value of a share of common stock is the present value of all futuredividends

NN Model for Bankruptcy Prediction

• Consists of an input layer, hidden layer and anoutput layer

• Input layer consists of 5 nodes, one for each ratio

• Hidden layer consists of 5 nodes• Output layer consists of only one neuron, with a

response of 0(bankrupt) and 1(nonbankrupt)

• The network was presented with the ratios of the

• Firms with output>0.5, nonbankrupt and <0.5, bankrupt

Neural Networks

• Two Domains :▫ Recognition Problem

▫ Generalization Problem

• Both Problems use a trained Neural Network fordata set of Input/output Pairs

• Recognition : Problem of Recognizing output OJ corresponding to input IJ which can be a Noise

Corrupted Input.• Generalization: Given n pairs of I/O, predicting

On+1 for corresponding In+1.

Bond Credit Rating

• Grade given to Bonds that indicates their creditquality

• Rating given to financial strength of a bond issues or

its ability to pay a bond‟s principal and interest in agiven time.

• The Process of Bond Credit Rating is a non-conservative domain and highly non linear, but is of

enormous importance in real world of finance.

• Given by Standard Independent Rating Servicessuch as Standard and Poor, Moody‟s,Fitch‟s,etc.

Type of Grades

• AAA and AA : High credit quality investmentgrade

• AA and BBB: Medium credit Quality Investment

• BB,B,CCC,CC,C:Low Quality or „Junk Bonds‟

• D: Bond in default for non-payment of principal

and interest

• Precise Mathematical form of f is unknown

• Multivariate regression models have tried toapproximate the function f .

• But success was Limited.

• Approximation for f is attempted using NeuralNetworks and they are proved to be better than

the Classical Regression Methods.

Problem Statement (contd.)

Variables Selected for Predicting Bond

Ratings• Liability • Debt Proportion• Sales/Net Worth

• Profit• Financial Strength• Earning• Past five-year revenue Growth Rate• Projected next five year revenue growth Rate• Working Capital• Subjective prospect of company

Details of the Experiments

• All the Ten Variables were used to Predict BondRatings

• Two Configurations of Neural Networks wereExperimented

• Two Layered and Three Layered Configurations

Organization LinearRegression

Two Layered Three Layered

A 61.5 76.9 89.4

B 62.4 74.5 82.4

C 38.5 55.6 61.6

D 48.9 67.9 63.4

E 23.9 49.4 58.9

F 44.5 60.3 65.3G 56.8 67.5 69.7

H 43.1 65.2 67.4

I 67.4 81.2 87.3

J 54.9 67.4 69.1

Correct Prediction(in percent) using Different ModelsBy 10 Credit Rating Organizations

• Neural Network Model consistently outperformsRegression Model in predicting Bond Rating

• Increasing Number of layers was giving

considerable difference in prediction rate exceptin some cases.

• Reasons for Better Performance is thatRegression Models have Statistical and

Mathematical Techniques while in neuralnetworks , model improves itself after every iteration.

Results

A Wavelet Neural Network[6]

Fuzzy Wavelet Neural Networks

• Fuzzy Wavelet Neural Networks improvesfunction approximation accuracy and are usedfor modeling Nonlinear Dynamic Systems

• Set of Fuzzy Rules generalize the basis functionof wavelets and thus approximates better

References

[1]Martin P. Wallace. “Neural Networks and their application to finance”.Business Intelligence Journal, July 2008.

[2]Marcus D.Odom, Ramesh Sharda. “A Neural Network Model for Bankruptcy Prediction”. IJCNN International Joint Conference on Neural Networks, 1990.

[3]Daniel W. C. Ho, Ping-An Zhang, and Jinhua Xu. “Fuzzy Wavelet Networks forFunction Learning”. IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 9, NO. 1,

FEBRUARY 2001 [4] R. Campos, F. J. Ruiz, N. Agell & C. Angulo. “Financial credit risk

measurement prediction using innovative soft-computing techniques”International Conference on Computational Finance & its Applications

• [5]Dr Clarence N W Tan, PhD. “An Artificial Neural Networks Primer withFinancial Applications Examples in Financial Distress Predictions and ForeignExchange Hybrid Trading System”. School of Information Technology, Bond

University, Gold Coast, QLD 4229,Australia• [6]Jun Zhang, Member, IEEE, Gilbert G. Walter, Yubo Miao, and Wan Ngai

Wayne Lee, Member, IEEE “ Wavelet Neural Networks for Function Learning”IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 43, NO. 6. JUNE 1995

Thank You

Neural Network in Financial Analysis

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