Predicting Daily Returns for the IBM Stock

The goal of the work described in this paper is to predict the daily returns of the closing prices for the IBM stock. From the original data of IBM daily quotes a new data set was built using technical indicators as predictor variables. Using this new data set, two modelling approaches were tried: regression and classification. Early analysis and experiments suggested that this prediction problem has some specific properties that make it difficult for standard learning algorithms. Resulting from this analysis we propose a two-steps approach to overcome these difficulties. Initial experimental analysis shows that this approach is promising. However, the actual results are still far from the ideal performance achievable by our proposed methodology. Our analysis of these results show that further work must be done, namely in improving the performance of the classification stage of our approach.