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Regression Model to predict BSE SENSEX

Regression Model to predict BSE SENSEX

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Munish Virang Rp Munish Virang Rp Presentation Transcript

  • FORECASTING MODEL TO PREDICT BSE (BOMBAY STOCK EXCHANGE) SENSEX USING REGRESSION ANALYSIS BY :MUNISH VIRANG INDUSTRIAL & SYSTEM ENGINEER ARIZONA STATE UNIVERSITY
  • Outline  DATASET Description  Full Model  Issues & Remedy  Final Model  Comparison between Full Model & Final Model  Conclusion & Scope of Improvement  Tools Used
  • DATASET Description Response Variable: BSE (Bombay Stock Exchange) SENSEX (No Unit)  Predictor Variables:  S.No Description of the Type Units Regressor 1 DOW Numeric (Global Queue) No Unit 2 NASDAQ (NAS) Numeric (Global Queue) No Unit 3 DAX Numeric (Global Queue) No Unit 4 FTSE 1000 (FTS) Numeric (Global Queue) No Unit 5 NIKKEI 225 (NIK) Numeric (Global Queue) No Unit 6 Straits Times (ST) Numeric (Global Queue) No Unit 7 FOREX (FOR) Numeric $ Billion 8 Crude Oil (CO) Numeric $/BBL ($ US) 9 Gold Bullion (GOLD) Numeric $/Troy Ounce 10 GDP Numeric $ Billion 11 Inflation Rate (IR) Numeric % 12 Rs vs Dollar (RD) Numeric Rupee
  • Full Model: All Predictors in the Model BSE SENSEX (Y) = - 6069 - 0.095 DOW - 0.229 NAS - 0.128 DAX + 1.06 FTS + 0.129 NIK + 0.23 ST+ 2.00 GDP + 8.5 FOR - 17.2 IR - 33 RD + 11.0 CO + 14.4 Gold Issues With The Full Models: 160 140 The Condition Number R-Sq = 97.5% > R-Sq (adj) = 96.5% 120 Κ=λmax =1374.84 Prediction Way Off from Target 100Residual show a pattern are not λmin Strong multicollinearity is present in theLot ACTUAL BSE Statistics Indicating: data set. of Over fitting the MODEL structure less. 80 SENSEX Redundant Predictor Variables in Multicollinearity Present PREDICTED As 60 Nasdaq and DOW rise BSE also rise ie Positive the Model for which model is been BSE SENSEX Correlation (Subject Matter Expert). Model Adequacy Check Fail Model not good for future prediction 40 penalized in tern of accuracy. Model indicate the other way 20 0 1 4 7 10 13 16 19 22 25 28 31 34 37 40
  • How To Deal With Issue  Collecting More Data  Standardization  Ridge Regression  Re specify the Model (using Principle Component Analysis) First three Approach No Change.
  • Principle Component Analysis (Re specifying The Model ) Predictor variables can be classified as :  Global Queue /Stock Market Indices from US,EUROPE and ASIA  Commodities & Exchange Approach: Instead of using all the predictor variables in the model a linear combination of variables are used
  • New Variables • PC QUE = 0.391*DOW+0.406*NAS+0.406*DAX+0.437*FTS+0.441*NIK+0.334*ST •All the Global Queue Equally Loaded PC Que Linear Combination • PC FCG = 0.511*GDP+0.508*FOR+0.473*CO+0.507*Gold •Eigen • Linear Combination of GDP,FOREX,CRUDE OIL & GOLD Value • Equally Loaded PC GFCG •Scree Plot • Unchanged Variables Infaltion Rate & Rupee Dollar Rate
  • Variable Selection Summary Statistics Method Suggested Predictors S = 369 Forward Selection R-sq = 96.54 RD,PC QUE and PC GFCG (α–to-enter: 0.1) R-sq(adj) = 96.26 C-p = 3.2 S = 369 R-sq = 96.54 Backward Elimination RD,PC QUE and PC GFCG R-sq(adj) = 96.26 (α–to-remove: 0.1) C-p = 3.2 S = 369 R-sq = 96.54 Stepwise Regression RD,PC QUE and PC GFCG R-sq(adj) = 96.26 ( α–to-enter: 0.1, α–to-remove: 0.1) C-p = 3.2 Model: Y = - 14323 + 125 RD + 14.1 PC GFCG + 0.363 PC Queue
  • Model Adequacy Check Fail Curve Pattern (Ideal Structure less) Model not good for future Prediction Residual Plots for Y Normal Probabilit y Plot Versus Fit s 99 1000 90 500 Residual Percent 50 0 -500 10 -1000 1 -1000 -500 0 500 1000 2000 4000 6000 8000 10000 Residual Fitted Value Hist ogram Versus Order 1000 12 500 9 Frequency Residual 0 6 -500 3 -1000 0 -800 -400 0 400 800 1 5 10 15 20 25 30 35 40 Residual Observation Order
  • How to Deal with Model Inadequacy Data Transformation: Using a square root transformation on response variable ,BSE Sensex Model good for future prediction Structure less Residual Plots for SQRT RES Normal Probabilit y Plot Versus Fit s 99 Standardized Residual 2 90 1 Percent 50 0 -1 10 -2 1 -2 -1 0 1 2 60 70 80 90 100 Standardized Residual Fitted Value Hist ogram Versus Order 8 Standardized Residual 2 6 Frequency 1 4 0 -1 2 -2 0 -2 -1 0 1 2 1 5 10 15 20 25 30 35 40 Standardized Residual Observation Order
  • Variable Selection Summary Statistics Method Suggested Predictors S = 2.16 Forward Selection R-sq = 97.26 PC QUE and PC GFCG (α–to-enter: 0.1) R-sq(adj) = 97.11 C-p = 1.4 S = 2.16 R-sq = 97.26 Backward Elimination PC QUE and PC GFCG R-sq(adj) = 97.11 (α–to-remove: 0.1) C-p = 1.4 S = 2.16 R-sq = 97.26 Stepwise Regression PC QUE and PC GFCG R-sq(adj) = 97.11 ( α–to-enter: 0.1, α–to-remove: 0.1) C-p = 1.4 Model: SQRT (BSE Sensex) = - 13.2 + 0.0875 PC GFCG + 0.00218 PC Queue
  • Final Model SQRT (BSE SENSEX) = - 13.2 + 0.0875 PC GFCG + 0.00218 PC Queue 120 Better Prediction 100 80 60 BSE SENSEX PREDICTED BSE 40 20 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
  • Comparison of Full Model and Final Model 160 140 120 100 BSE SENSEX 80 FINAL PREDICTED MODEL FULLMODEL 60 40 20 0 12 2 13 32 3 5 11 15 31 22 33 35 1 40 7 17 18 37 8 21 23 25 38 4 6 9 10 14 16 19 30 27 28 34 36 39 24 20 26 29
  • Conclusion & Scope of Improvement  As per the model the important predictor are Global Queue 1. GDP of the Country 2. FOREX 3. Gold and Crude oil Prices 4.  Including top 30 share of BSE Sensex (Direct Cause and Effect relation) will enhance the capability of the model to predict more accurately
  • Tools & References Tools:  MINTAB 14  SAS 9.1 References:  Montgomery D.C., Introduction to Linear Regression, Fourth Edition, Wiley & Sons Inc  www.finance.yahoo.com  www.bseindia.com