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Modeling the S&P 500 Index using the Kalman Filter and the LagLasso
This video was recorded at International Workshop on Advances in Machine Learning for Computational Finance (AMLCF), London 2009. This video introduces a method to predict upward and downward monthly variations of the S&P 500 index by using a pool of macro-economic and financial explicative variables. The method is based on the combination of a denoising step, performed by Kalman filtering, with a variable selection step, performed by a Lasso-type procedure. In particular, we propose an implementation of the Lasso method called LagLasso which includes selection of lags for individual factors. We provide promising backtesting results of the prediction model based on a naive trading rule.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
