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DEVELOPMENT OF INTELLIGENT PREDICTIVE MODEL FOR STOCK DATA PREDICTION WITH FEATURE EXTRACTION AND SELECTIONPresented By:Richa HandaAsst. Professor

Contents:ObjectiveIntroductionIntelligent Techniques for predictionANN TechniquesStock DataFeature ExtractionTechnical IndicatorsFeature SelectionFramework for stock market predictionResult and AnalysisWavelet techniquesDe-noising stock data using SWTProposed Model: Hybridization of SWT and ANNResult and analysisConclusionReferences

ObjectiveIn this research work a framework is designed for an optimal stock data prediction to develop an intelligent decision support system.This developed system remove the non linearity that exist in financial time series data using some feature extraction and selection. For De-noising the data of extracted features SWT is used.These extracted de-noised features are apply to model of ANN and data mining techniques is used to get the accurate prediction of stock price.

IntroductionThe stock market is a complex and dynamic system with noisy, non-stationary and chaotic data series. Prediction of a financial market is more challenging due to chaos and uncertainty of the system. Soft computing techniques are progressively gaining presence in the financial world. This research work describes the application of Artificial Neural Network (ANN) for the prediction of Stock Market using some technical indicators.. A new model with ANN and SWT is purposed with ranking based feature selection technique.Stationary Wavelet Transform(SWT) is used for de noising the data.Hybrid of SWT and ANN is used for stock market prediction for better accuracy.

Intelligent Techniques for Prediction:Intelligent techniques for stock market predictionArtificial Intelligent Techniques(ANN)Wavelet techniquesHybrid TechnologyContinuous Wavelet Transform(CWT)Discrete Wavelet Transform(DWT)Stationary Wavelet Transform(SWT)

ANNWavelet Transform

ANN Techniques:ANN TechniquesSupervisedLearningUnsupervisedLearningKSOMRBFN

EBPN

It is a supervised learning method, and is a generalization of the delta rule. It requires a dataset of the desired output for many inputs, making up the training set. It is most useful for feed-forward networks. Architecture of EBPN is given below:

Error Back Propagation Network (EBPN):

Radial Basis Function (RBF) Neural Network:

Radial basis functions are powerful techniques for interpolation in multidimensional space. A RBF is a function which has built into a distance criterion with respect to a center. Architecture of RBFN as given below:

Stock Data The data used in this study consist of BSE30 data collected from the historical data available on the website yahoo finance. The actual data contains 6 features:Date: The date of stock market data.Open: The value of stock open on a particular date.Close: The value of stock Close on a particular date.Low: The lowest value of stock on a particular date.High: The highest value of stock on a particular date.Volume: Total number of units sold on a particular date. This dataset encompasses five years data. The collected data is Non linear by nature, so preprocessing technique has been done to make the data smoother. For preprocessing of data some technical indicators are used suggested by some researchers.

Sample of DataMACD histrogram10 days EMARSID%ROCMFI%RcloseOBVADCHOATRADXCCIPPOCMF0.8300.9040.0200.0740.0210.2090.1970.460-0.4690.041-0.0080.2110.001-0.0080.000-0.0690.8270.9060.0200.0790.0190.1870.1480.457-0.7100.0570.0090.2070.001-0.008-0.001-0.0720.8280.9050.0200.0710.0290.1930.1200.450-0.4710.0740.0000.2480.001-0.007-0.001-0.0710.8310.9050.0180.0620.0250.1980.0980.4460.4770.076-0.0040.2840.001-0.006-0.001-0.0710.8330.9070.0170.0570.0060.2060.1100.4430.7160.1040.0090.2990.001-0.004-0.001-0.0720.8330.9160.0180.0620.0230.2020.1830.4450.4720.2120.0270.3290.001-0.006-0.002-0.0690.8310.9200.0180.0690.0300.1970.1790.4550.2270.2550.0170.3660.001-0.007-0.006-0.0670.8310.9250.0150.0800.0510.2280.2110.4550.4740.155-0.0090.4240.001-0.0070.004-0.0680.8330.9280.0110.0850.0390.2670.2170.4590.4930.071-0.0100.4670.001-0.0020.001-0.0650.8380.9280.0130.1000.0370.2710.2380.4590.2460.4470.0740.4960.001-0.0070.001-0.0700.8410.9320.0120.1040.0350.2630.4380.4590.4960.8010.0810.5590.001-0.0070.001-0.0700.8410.9360.0120.1190.0130.2590.2960.4650.7480.533-0.0210.5940.001-0.0060.001-0.0650.836

Feature Extraction:Feature extraction method is transformative: that is we are applying transformation to our data to project it into new feature space with lower dimension. Its main task is to select or combine the features that preserve most of the information and remove the redundant components in order to improve the efficiency of the subsequent classifiers without degrading their performances.

Technical Indicators:Exponential Moving Average(EMA)Moving Average Convergence-Divergence(MACD)Relative Strength Index(RSI)Stochastic OscillatorRate of Change(ROC)Money Flow Index(MFI)William %RAccumulation Distribution Line(A/D)On Balance Volume(OBV)Chaikin Oscillator(CHO)Average True RangeAverage Directional Index(ADX)Commodity Channel Index(CCI)Chaikin Money Flow(CMF)Percentage Price Oscillator(PPO)Force Index(FI)

Feature Selection Technique:

One of the essential feature of data mining is feature selection technique, this technique is mostly based on the machine learning for selection set of feature for improving the efficiency of the prediction. Feature selection techniques are used to automatically discover the best features and it helps to solve the problems of having too much data.

Rank based FSTFeature Extraction Extracted FeaturesBased on technical IndicatorsNew feature space after applying FST

EMARSISOROCMFI%RA/DOBVCHOATRADXCCICMFPPOFIMACD

DATEOPENCLOSELOWVOLUMEHIGH

EMARSIROC%RCHOATRCLOSE

Initial feature Space

Framework for Stock Market PredictionFeature Extraction and SelectionTrainingTestingEBPNRBFNStock DataFeature ExtractionNew Stock Data With Extracted FeaturesData NormalizationFeature Selection TechniqueRank Based MethodStock Data With reduced feature subsetData PartitioningANN ModelMAERMSEMAPEStock Prediction

Result and AnalysisANN TechniquesNo of Features selectedMAPERMSEMAEEBPN165.5140.0360.026136.0850.0360.026116.1100.0360.028105.9790.0340.02575.4850.3430.025RBNF168.2600.0120.008129.0370.0140.009117.6630.0140.008106.7500.1370.00775.9020.0130.007

Comparative MAPE of EBPN and RBFN with reduced feature subset

MAPEFeatures

Comparative EBPN of Actual and Predicted value of stock marketComparative RBFN of Actual and Predicted value of stock market

Wavelet TechniqueA wavelet is a wave-like oscillation with an amplitude that begins at zero, increases, and then decreases back to zero.Wavelet analysis is characterized by a wavelet. The wavelet transform can provide information about both the time and frequency domains.

Types of Wavelet TransformCWT(Continuous Wavelet Transform)DWT(Discrete Wavelet Transform )SWT(Stationary Wavelet transform)

Denoising of Stock Data using SWT Time series data are very non linear and noisy by nature and these noisy data might degrade the quality of discovered pattern. A MATLAB GUI tool is used to apply SWT for de-noising stock data.

Signal before preprocessing.Signal after preprocessing using SWT.

GUI for De-noising

Porposed Model: Hybridization of SWT and ANN

Hybrid Model

Feature selectionNew feature Subset subsetANN

Model Prediction StageData Pre-Processing Stage Selection of type of WT:SWTFeature extractionMAPERMSE

MAEStock Data (Normalized)

Stock Prediction

Select thresholding Method

Choosing level of decomposition

De-noised signals

Result Analysis

ANN TechniquesNo of Features selectedMAPERMSEMAEEBPN162.7370.0300.008142.7420.0300.016132.6180.0340.015114.3550.0420.025102.6440.0350.01694.1540.0400.02484.0580.0360.02272.6140.0340.015RBNF162.8510.0370.011142.8410.0420.018133.0010.0380.0175112.6400.0340.015103.2570.0420.01993.9930.0480.02482.7960.0390.01772.6270.0370.017

MAPE comparison of EBPN and RBFN in selected Features of original data and de-noise data.

ConclusionANN based techniques learns the pattern by mapping input with corresponding output. If there are variations in input output pattern, ANN may not map pair of input output in better way. In order to overcome this problem input pattern are required to be de-noise( Remove noise from the pattern). Wavelet transform like SWT may be the best alternative for this. SWT is used to de-noise the data with 16 extracted features and the data are applied to ANN with ranking based features selection technique and the proposed hybrid of SWT and ANN produces comparative better result.The outcome of the research work is as hybridization of SWT and ANN, where SWT is used for data smoothing and ANN is used for prediction of stock data.

ReferencesAbhyankar, A., Copeland, L. S., and Wong, W. (1997). Uncovering nonlinear structure in real-time stockmarket indexes: The SandP 500, the DAX, the Nikkei 225, and the FTSE-100. Journal of Business and Economic Statistics, 15, 114.Amelia Bilbao-Terol , Mar Arenas-Parra, Vernica Caal-Fernndez (2012), Selection of Socially Responsible Portfolios using Goal Programming and fuzzy Technology, Information Sciences ,Vol. 189 ,Pp.110125.Ashoka H N, Manjaiah D H, Rabindranath Bera, Feature Extraction/Selection and Statistical Classification Technique for Character Recognition, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 5, May 2012,Pp.414-420.Asadi S, Hadavandi E, Mehmanpazir F, Nakhostin MM(2012) Hybridization of evolutionary Levenberg Marquardt Neural Networks and data Preprocessing for stock market Prediction. Knowledge based system.Bartosz Kozowski, Time series denoising with wavelet transform,Journal of telecommunication and Information Technology(2005),Pp. 91-95Boyaciaglu MA, Avci D (2010) An Adaptive Neural-Based Fuzzy Inference System(ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange. Expert system with Applications 37(12):7908-7912.Broomhead, D. S. and Lowe D.(1988). Multivariable functional interpolation and adaptive networks. Complex Systems. 2 , 321-355.Chih-Fong Tsai , Yu-Chieh Hsiao. Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches, Decision Support Systems, Volume 50, Issue 1, December 2010, Pp 258269.

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