Forecasting the S&P 500 index using time series analysis and simulation methods

[摘要] (cont.) ARIMA and Double Exponential smoothing models underperformed in comparison. ARIMA model does not adjust well in the ;;beginning;; of a downward/upward pattern, and should be used when a clear trend is shown. However, the Double Exponential Smoothing is a good model if a steep incline/decline is expected. ARMAX + ARCH/EGARCH performed below average and is best used for volatility forecasts instead of mean returns. Lastly, Neural Network residual models indicate mixed results, but on average outperformed traditional time series models (ARIMA/Double Exponential Smoothing). Additional research includes forecasting the S&P 500 with other nontraditional time series methods such as VARFIMA (vector autoregressive fractionally integrated moving averages) and ARFIMA models. Other Neural Network techniques include Higher Order Neural Networks (HONN), Psi Sigma network (PSN), and a Recurrent Neural Network (RNN) for additional forecasting comparisons.

[发布机构] Massachusetts Institute of Technology