1. Neural Networks in Financial AnalysisBy
Rajesh Amradi( )
Nikhil Prakash ( )
Under the guidance of
Prof. Pushpak Bhattacharya

2. 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

3. What are Neural Networks?
Neural networks mimic the role and capacity of human brain to process information
It maps some type of input stream of information to output stream of data
Consists of ways to connect data/information to produce output that it consistent with the processes
May seem simple, but far from trivial

4. What are Neural Networks?(Contd.)
Neural network is computer science phenomenon which use
Processing Elements
High Degree of Interconnectivity
Dependence of Variables
Input to Output ratio not 1 to 1
Many interactions between inputs and backward linkages from output to inputs

5. Neural Networks and similarities with working of human brain

6. Why use Neural Networks?
Interest in Neural Networks stems primarily from its nonlinear models that can be trained to map past and future values of input output relationship
Capability to recognize pattern and speed of its techniques to accurately solve complex processes in many applications
Help to charactize relationships via a nonlinear non-parametric inference technique

7. 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 reduced dimensional subspace
Simulation of output from input relationships and interconnections
Added advantage of being able to establish a 'training phase'
Can generalize results and lead to logical and unforeseen conclusions through the model

8. Neural Networks in Finance
NNs are trained without restriction of a model to deprive parameters and discover relationships
Driven and shaped solely by the nature of the data
Has profound implications and applicability to the finance field

9. Time Series Analysis
Special form of data where past values influence future values
Understanding time series can predict the functionality of financial markets
Equation of the random model, used to model market prices
p(t) = p(t-1) + u
Where p represents market prices, t index of time, u stochastic variable u ≈ (0,c)
Neural networks are an appropriate model to analyze financial time series

10. Stock Market Analysis
Stock pricing is an important aspect of financial economics
Dividend Discount Model(DDM) applied to neural networks in order to verify if the entities are relatively stable
Also to verify if prices are efficient and fair for stocks
DDM assumes that the value of a share of common stock is the present value of all future dividends

11. Capital Budgeting and Risk
One of the most important functions of financial management
Planning expenditures on assets whose cash flows are expected to extend beyond one year
Involves stock values and forecasting mechanisms, because of the presence of large values of money and financing to be planned in advance
Hence, neural networks come into play

12. Neural Network Model for Bankruptcy Prediction
Multivariate statistical analysis technique called Discriminant Analysis – widely used model in bankruptcy prediction
Ratios used in discriminant analysis :
X1 – Working Capital/Total Assets
X2 – Retained Earnings/Total Assets
X3 – Earnings before Interest and Taxes/Total Assets
X4 – Market Value of Equity /Total Debt
X5 – Sales/Total Assests

13. NN Model for Bankruptcy Prediction
Consists of an input layer, hidden layer and an output 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
Firms with output>0.5, nonbankrupt and <0.5, bankrupt

14. NN Model for bankruptcy prediction

15. NN model for bankruptcy prediction
Uses backpropogation rule neural network
One problem with the backpropogation model is the number of iterations needed to learn the data
After training for 24 hours, and 191,400 iterations with the subsample of the training data of various firms with correct output of each data
Correctly identified 36 firms in the test data as non bankrupt and 38 firms as bankrupt
Much more promising when compared to Discriminant Risk Analysis

16. Bond Credit Rating: Assessing Credit Risk of a Corporation using Artificial Neural Networks

17. Neural Networks Two Domains :
Recognition Problem
Generalization Problem
Both Problems use a trained Neural Network for data 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.

18. Bond Credit Rating
Grade given to Bonds that indicates their credit quality
Rating given to financial strength of a bond issues or its ability to pay a bond’s principal and interest in a given 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 Services such as Standard and Poor, Moody’s,Fitch’s,etc.

19. Type of Grades AAA and AA : High credit quality investment grade
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

20. Problem Statement
Let B represent the space of n bonds, B1, B2,…. Bn, and R be the set of possible m bond ratings, R1, R2,….. Rm . And Let F represent the k dimensional feature space ,F1, F2,…, Fk, describing each bonds then Each bond Bi can be considered as a k-tuple(F1Bi, F2Bi,.., F1Bi ) in the cartesian space F1 x F2 x … x Fk. And rating the bonds involves finding the one to one mapping function f such that:
f : F1 x F2 x … x Fk R

21. Problem Statement (contd.)
Precise Mathematical form of f is unknown
Multivariate regression models have tried to approximate the function f .
But success was Limited.
Approximation for f is attempted using Neural Networks and they are proved to be better than the Classical Regression Methods.

22. Classical Regression Model
Regression Models are Classical Models for predicting Bond Credit Rating
Have Limitations because of having Standard Mathematical and Statistical Techniques

23. Neural Network Model
Multilayer network is used having simple processing elements called ‘units’.
Each ‘unit’ interacts with other using weighted connections.
A ‘state’ is assigned to each unit which is decided by the units in the layer below.
Activation function used is Monotonic Nonlinear Function

24. 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

25. Details of the Experiments
All the Ten Variables were used to Predict Bond Ratings
Two Configurations of Neural Networks were Experimented
Two Layered and Three Layered Configurations

26. Correct Prediction(in percent) using Different Models
By 10 Credit Rating Organizations
Organization
Linear Regression
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
65.3 G
56.8
67.5
69.7 H
43.1
65.2
67.4 I
81.2
87.3 J
54.9
69.1

27. Results
Neural Network Model consistently outperforms Regression Model in predicting Bond Rating
Increasing Number of layers was giving considerable difference in prediction rate except in some cases.
Reasons for Better Performance is that Regression Models have Statistical and Mathematical Techniques while in neural networks , model improves itself after every iteration.

28. Modification in Neural Networks
Wavelet Neural Networks
Wavelets ,a technique used in multi-resolution analysis in signal processing, is used to overcome the limitations in Neural Networks
Wavelet Neural Networks approximates a function f better than neural networks
WNN has universal L2 approximation properties and is a consistent function estimator

29. A Wavelet Neural Network[6]

30. Fuzzy Wavelet Neural Networks
Fuzzy Wavelet Neural Networks improves function approximation accuracy and are used for modeling Nonlinear Dynamic Systems
Set of Fuzzy Rules generalize the basis function of wavelets and thus approximates better

31. Fuzzy Wavelet Neural Network [3]

32. 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 for Function 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 with Financial Applications Examples in Financial Distress Predictions and Foreign Exchange 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

33. Thank You