Presentation on theme: "Neural Networks in Financial Analysis"— Presentation transcript:
1 Neural Networks in Financial Analysis ByRajesh Amradi( )Nikhil Prakash ( )Under the guidance ofProf. Pushpak Bhattacharya
2 Outline Introduction to Neural Networks Neural Networks in Finance Time Series AnalysisStock Market AnalysisCapital Budgeting and RiskNN Model for BankruptcyVariablesModelBond Credit RatingProblem StatementVariables and ModelsComparative StudyModifications in Neural NetworksWavelet Neural NetworksFuzzy Wavelet Neural Networks
3 What are Neural Networks? Neural networks mimic the role and capacity of human brain to process informationIt maps some type of input stream of information to output stream of dataConsists of ways to connect data/information to produce output that it consistent with the processesMay seem simple, but far from trivial
4 What are Neural Networks?(Contd.) Neural network is computer science phenomenon which useProcessing ElementsHigh Degree of InterconnectivityDependence of VariablesInput to Output ratio not 1 to 1Many 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 relationshipCapability to recognize pattern and speed of its techniques to accurately solve complex processes in many applicationsHelp 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 applicationsClassification of Input StreamAssociation of output given sectors of input groupingCodification of input by producing output within a reduced dimensional subspaceSimulation of output from input relationships and interconnectionsAdded 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 relationshipsDriven and shaped solely by the nature of the dataHas profound implications and applicability to the finance field
9 Time Series AnalysisSpecial form of data where past values influence future valuesUnderstanding time series can predict the functionality of financial marketsEquation of the random model, used to model market pricesp(t) = p(t-1) + uWhere 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 AnalysisStock pricing is an important aspect of financial economicsDividend Discount Model(DDM) applied to neural networks in order to verify if the entities are relatively stableAlso to verify if prices are efficient and fair for stocksDDM 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 managementPlanning expenditures on assets whose cash flows are expected to extend beyond one yearInvolves stock values and forecasting mechanisms, because of the presence of large values of money and financing to be planned in advanceHence, neural networks come into play
12 Neural Network Model for Bankruptcy Prediction Multivariate statistical analysis technique called Discriminant Analysis – widely used model in bankruptcy predictionRatios used in discriminant analysis :X1 – Working Capital/Total AssetsX2 – Retained Earnings/Total AssetsX3 – Earnings before Interest and Taxes/Total AssetsX4 – Market Value of Equity /Total DebtX5 – Sales/Total Assests
13 NN Model for Bankruptcy Prediction Consists of an input layer, hidden layer and an output layerInput layer consists of 5 nodes, one for each ratioHidden layer consists of 5 nodesOutput layer consists of only one neuron, with a response of 0(bankrupt) and 1(nonbankrupt)The network was presented with the ratios of the firmsFirms with output>0.5, nonbankrupt and <0.5, bankrupt
15 NN model for bankruptcy prediction Uses backpropogation rule neural networkOne problem with the backpropogation model is the number of iterations needed to learn the dataAfter training for 24 hours, and 191,400 iterations with the subsample of the training data of various firms with correct output of each dataCorrectly identified 36 firms in the test data as non bankrupt and 38 firms as bankruptMuch 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 ProblemGeneralization ProblemBoth Problems use a trained Neural Network for data set of Input/output PairsRecognition : 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 RatingGrade given to Bonds that indicates their credit qualityRating 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 InvestmentBB,B,CCC,CC,C:Low Quality or ‘Junk Bonds’D: Bond in default for non-payment of principal and interest
20 Problem StatementLet 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 unknownMultivariate 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 RatingHave Limitations because of having Standard Mathematical and Statistical Techniques
23 Neural Network ModelMultilayer 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 LiabilityDebt ProportionSales/Net WorthProfitFinancial StrengthEarningPast five-year revenue Growth RateProjected next five year revenue growth RateWorking CapitalSubjective prospect of company
25 Details of the Experiments All the Ten Variables were used to Predict Bond RatingsTwo Configurations of Neural Networks were ExperimentedTwo Layered and Three Layered Configurations
26 Correct Prediction(in percent) using Different Models By 10 Credit Rating OrganizationsOrganizationLinear RegressionTwo LayeredThree LayeredA61.576.989.4B62.474.582.4C38.555.661.6D48.967.963.4E23.949.458.9F44.560.365.3G56.867.569.7H43.165.267.4I81.287.3J54.969.1
27 ResultsNeural Network Model consistently outperforms Regression Model in predicting Bond RatingIncreasing 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 NetworksWavelets ,a technique used in multi-resolution analysis in signal processing, is used to overcome the limitations in Neural NetworksWavelet Neural Networks approximates a function f better than neural networksWNN has universal L2 approximation properties and is a consistent function estimator
30 Fuzzy Wavelet Neural Networks Fuzzy Wavelet Neural Networks improves function approximation accuracy and are used for modeling Nonlinear Dynamic SystemsSet of Fuzzy Rules generalize the basis function of wavelets and thus approximates better
32 ReferencesMartin P. Wallace. “Neural Networks and their application to finance”. Business Intelligence Journal, July 2008.Marcus D.Odom, Ramesh Sharda. “A Neural Network Model for Bankruptcy Prediction”. IJCNN International Joint Conference on Neural Networks, 1990.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 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 ApplicationsDr 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,AustraliaJun 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