Our primary Hypothesis to market forecasting is that history has the tendency to repeat itself, we are using several methods for analysis including: Linear Regression, Time Series Models: Autoregressive Integrated Moving Averages (ARIMA), Double Exponential Smoothing, Neural Networks, GARCH, and Bootstrapping Simulations. Along with the following metrics for the evaluation range: Root Mean Square Error (RMSE), Absolute Error (MAE), Akaike information criterion (AIC) and Schwartz Bayesian criterion (SBC). The primary forecasting measure includes MAE and Mean Absolute Percentage Error (MAPE), which uses the forecasted value and the actual S&P 500 level as input parameters.


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