Neural Network Stock Index Forecasting

1. Neural NetworkStock Index Forecasting 2004/05/20

2. Outline • The problem of forecasting stock market • The development of ANN model • Neural function • Neural architecture • Neural learning • Case study • Kula Lumpur Composite Index (KLCI) • Discussion

3. The problem of forecasting stock market • People tend to invest in equity because of its high returns over time. • Stock markets are affected by many factors, and hence it is very difficult to forecast the movements of stock markets. • Prediction in stock market has been a hot topic for investors and researchers.

4. The development of ANN model • Neural function: • Neural net architecture: feedforward network • 5-3-1 • 5-4-1 • 5-3-2-1 • 6-3-1 • 6-5-1 • 6-4-3-1 • Neural learning: LMS

5. Case study - KLCI • The Kuala Lumpur Stock Exchange (KLSE) is considered a young and speculative market • 10 years of history • 492 companies • Kuala Lumpur Composite Index (KLCI) is calculated on the basis of 86 major Malaysian stocks.

6. Case study -- KLCI • Major types of indicators • Stock index (It-1, It, It+1) • Moving average (MA5, MA10, MA50) • Momentum (M) • Relative Strength Index (RSI) • Stochastics (%K) • Moving average of stochastics (%D)

7. Case study -- KLCI • Daily data from Jan 3, 1984 to Oct 16 1991 are collected.

8. Case study -- KLCI • Normalization • Building neural network model • Profit strategy • Seed money is used to buy a certain number of indexed stocks when the prediction shows a rise • The basket of stocks will be held until the next turning point that the neural network predicts

9. Case study -- KLCI

10. Discussion • How do we determine the architecture of neural network? • General rule • Trail and error • Can we predict all stock markets? • Random walk vs predictable