Artificial Neural Network for Predicting Indonesia Stock Exchange Composite using Macroeconomic Variabels
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This document describes research using an artificial neural network to predict Indonesia's stock exchange composite index using macroeconomic variables. The research used historical data for exchange rates, interest rates, inflation rates, and money supply as inputs to train an ANN model. The best-performing model used a time delay of two months and achieved a prediction accuracy of 96.38% with an average error of 0.0045. The researchers concluded the ANN successfully predicted index movements and that macroeconomic indicators are suitable for this type of prediction. They suggest adding other variables or using daily data for more complex and beneficial predictions.
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Artificial Neural Network for Predicting Indonesia Stock Exchange Composite using Macroeconomic Variabels
- 1. ARTIFICIAL NEURAL NETWORK FOR PREDICTING INDONESIA STOCK EXCHANGE COMPOSITE USING MACROECONOMIC VARIABLES 1 Management of Business in Telecommunication and Informatics School of Economic and Business, Telkom University ©2018
- 2. 2 High Risks High Return Losses Profits
- 3. 3
- 4. 4 Make a prediction! Lower the risks!
- 5. 5 Technical Analysis Fundamental Analysis
- 6. 6 Technical Analysis Fundamental Analysis
- 7. 0 5000 10000 15000 20000 Des-05 Agu-06 Apr-07 Des-07 Agu-08 Apr-09 Des-09 Agu-10 Apr-11 Des-11 Agu-12 Apr-13 Des-13 Agu-14 Apr-15 Des-15 Agu-16 Apr-17 Exchange Rate 0,0000 0,0200 0,0400 0,0600 0,0800 0,1000 0,1200 0,1400 Des-05 Sep-06 Jun-07 Mar-08 Des-08 Sep-09 Jun-10 Mar-11 Des-11 Sep-12 Jun-13 Mar-14 Des-14 Sep-15 Jun-16 Mar-17 Interest Rate 0 1000000 2000000 3000000 4000000 5000000 6000000 Des-05 Sep-06 Jun-07 Mar-08 Des-08 Sep-09 Jun-10 Mar-11 Des-11 Sep-12 Jun-13 Mar-14 Des-14 Sep-15 Jun-16 Mar-17 Money Supply0,00 1000,00 2000,00 3000,00 4000,00 5000,00 6000,00 7000,00 Dec-05 Sep-06 Jun-07 Mar-08 Dec-08 Sep-09 Jun-10 Mar-11 Dec-11 Sep-12 Jun-13 Mar-14 Dec-14 Sep-15 Jun-16 Mar-17 IDX Composite http://www.bi.go.id/id/statistik/seki/terkini/moneter/Contents/Default.aspx 0,0000 0,0500 0,1000 0,1500 0,2000 Des-05 Agu-06 Apr-07 Des-07 Agu-08 Apr-09 Des-09 Agu-10 Apr-11 Des-11 Agu-12 Apr-13 Des-13 Agu-14 Apr-15 Des-15 Agu-16 Apr-17 Inflation
- 8. -2 -1 0 1 2 3 4 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 101 103 105 107 109 111 113 115 117 119 121 123 125 127 129 131 Exchange Rate Interest Rate Inflation Rate Money Supply IDX Composite
- 9. ARTIFICIAL NEURAL NETWORK (ANN) • Elegant & Powerful • Approximate most complex functions • Blackbox BIG DATA & FAST PROCESSING • Make nonlinear math solutions feasible and imperative • Realistic performance BACKPROPAGATION ALGORITHM • Solve complex pattern at its best • Lower gradient to minimize errors
- 10. RESEARCH FRAMEWORK Capital Market Composite Stock Price Index Historical Data (t) Interest Rate InflationInvestment Macroeconomic Variables Historical Data (t) Data Mining Prediction Artificial Neural Network Backpropagation Composite Stock Price Index (t+1) Exchange Rate Money Supply
- 11. RESEARCH STAGE
- 12. PREPROCESSING Outlier Replacement Data Smoothing Data Normalization Data Partition ANN DESIGN PERFORMANCE PARAMETER 1 2 3 4 ANN Architecture ANN Parameter Train, Validate, Test Prediction 1 2 3 4 Accuracy (R-value) Error (MSE) 1 2
- 13. Exchange Rate (ER) Historical Data Interest Rate (IR) Historical Data Inflation Rate (IF) Historical Data Money Supply (M2) Historical Data IDX Composite Future Values 5 6 7 8 4 3 2 9 1 Bias Bias Heaton (2008) in Kumar & Marugan (2017)
- 14. Dec ‘05 – Nov ‘16 Dec ’16 – Nov ‘17 Train (92), Test (20), and Validation (20) Phase Prediction Phase (12) Data Sample (144) Source: processed data result by researcher (2018)
- 15. NON PREPROCESSED PREPROCESSED Under Fit Model R-Value : 0,98842 Good Fit Model ✓ R-Value : 0,99826 ----- Train ----- Validation ----- Test ----- Train ----- Validation ----- Test
- 16. Time Delay : 1 Time Delay : 2 Time Delay : 3 Time Delay : 6 Over Fit Model Good Fit Model ✓ ----- Train ----- Validation ----- Test
- 17. Time Delay : 1 Time Delay : 2 Time Delay : 3 Time Delay : 6 Good Fit Model ✓ R-Value : 0,99826 Good Fit Model ✓ R-Value : 0,99789 Over Fit Model R-Value : 0,99972 Over Fit Model R-Value : 0,99966 Model (based on time delay) Overall Accuracy (R-value) Overall Error (MSE) 1 99,826% 0,002997392 2 99,789% 0,003634197
- 20. Conclusion 2. The best time to predict IDX Composite is two months before the predicted month. 3. Macroeconomic variables are the proper indicators to predict IDX Composite movements. 1. ANN predict the IDX Composite successfully with final results of accuracy 96,38% and 0,0046 average errors.
- 21. Suggestion 1. Add or try another variables such as microeconomic variables 2. Daily data is highly suggested since the investors and issuers more require daily prediction rather than monthly data. Daily data also have more complex functions which more challenging and beneficial.
- 22. Thank You! 22 Management of Business in Telecommunication and Informatics School of Economic and Business, Telkom University ©2018