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Neural Network Forecasting with the S&P 500 Index Across Decades

Neural Network Forecasting with the S&P 500 Index Across Decades. M.E. Malliaris & A.G. Malliaris. July 23, 2013 The 9 th International Conference on Data Mining, Las Vegas. QUESTIONS. Are there stable patterns of directional movement in the S&P 500?

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Neural Network Forecasting with the S&P 500 Index Across Decades

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  1. Neural Network Forecasting with the S&P 500 Index Across Decades M.E. Malliaris & A.G. Malliaris July 23, 2013 The 9thInternational Conference on Data Mining, Las Vegas

  2. QUESTIONS • Are there stable patterns of directional movement in the S&P 500? • If so, can we use those patterns to forecast? • Do these patterns and their importance change over time?

  3. DATA SET • Closing values of the S&P 500 from 1950 through 2010 [6 decades] • Derived variables: • % change in closing • Moving averages • Patterns of Up and Down movement • Number of Ups

  4. DATA SET DIVISIONS • Divided by Decade for analysis and training • 10 Years used for training, • e.g., 1950 - 1959 • The Year immediately following a Decade was used as a validation set • e.g., 1960

  5. Two-Day Strings

  6. Two-Day Strings

  7. Two-Day Strings

  8. Up Movement Only

  9. Up Movement Only

  10. Up Movement Only

  11. Inputs

  12. Data Sets

  13. METHODOLOGY • Neural Network • Structure: Identical for all networks • 14 Inputs • One Hidden Layer with 9 nodes • 1 Output [tomorrow’s direction] • Random Seed: 229176228 • 30% used to prevent over-fitting

  14. Software: IBM’s SPSS Modeler 14

  15. Results

  16. Percent Correct Directions

  17. Top Five Inputs

  18. Top Five Inputs

  19. Top Five Inputs

  20. Top Five Inputs

  21. Top Five Inputs

  22. Summary • Neural Networks with the same structure were trained for six decades • This identicalstructure,usingthe same inputs, wasuseful for over six decades. • All variables were generated from the S&P 500 closing price • Variable importance shifted slightly over time • Successfulforecastingwas possible

  23. Future Research • Future researchmightinvestigate a smaller training time, say a rollingwindow of one or twoyears. • This mightenable us to see the importance of specific variables graduallyshifting over time.

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