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

The S&P 500 represents a diverse pool of securities in addition to Large Caps. A range of audiences are interested in the S&P 500 forecasts including investors, speculators, economists, government and researchers. The primary objective is to attempt to provide an accurate 3 month and 12 month forecast using the recent credit crisis data, specifically during the time range of 10/2008 - 09/2009. Several methods were used for prediction fit including: Linear Regression, Time Series Models: Autoregressive Integrated Moving Averages (ARIMA), Double Exponential Smoothing, Neural Networks, GARCH, and Bootstrapping Simulations. The criteria to evaluate forecasts were the following metrics for the evaluation range: Root Mean Square Error (RMSE), Absolute Error (MAE), Akaike information criterion (AIC) and Schwartz Bayesian criterion (SBC). But most importantly, 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. S&P 500 empirical results indicate that the Hybrid Linear Regression outperformed all other models for 3 month forecasts with the explanatory variables: GDP, credit default rates, and VIX volatility conditioned on credit crisis data ranges, but performed poorly during speculation periods such as the Tech Bubble. The Average of Averages Bootstrapping Simulation had the most consistent historical forecasts forl2 month levels, and by using log returns from the Great Depression, Tech Bubble, and Oil Crisis the simulation indicates an expected value -2%, valid up to 12 months.

Department dc:contributor.department

System Design and Management Program.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2009

Author dc:creator

• Chan, Eric Glenn

Advisor dc:contributor.advisor

• Roy E. Welsch.

• System Design and Management Program.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/55206

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/55206