Long time readers of this blog know that I haven't found data mining or artificial intelligence techniques to be very useful for my own trading, for they typically overfit to non-recurring past patterns. (Not surprisingly, they are much more useful for driverless cars.) Nevertheless, one must keep an open mind and continues to keep tabs on new developments in this field.
To this end, here is a new paper written by an engineering student at UC Berkeley which uses "support
vector machine" together with 10 simple technical indicators to predict the SPX index, purportedly with 60% accuracy. If one includes an additional indicator which measures the number of news articles on a stock in the previous day, then the accuracy supposedly goes up to 70%.
I did not have the chance to reproduce and verify this result yet, but I invite you to try it out and share your findings here. If you do so, you may find this new data mining product called 11Ants Analytics useful. It is an Excel-based software that includes 11 machine learning algorithms including the aforementioned support vector machines. It also includes decision trees which are sometimes quite useful in automatically generating a small set of trading rules from an input set of technical indicators. (Whether those rules remain profitable in the future is another question!) If you have tried this product, I would also appreciate your comments here.
(If you are a die-hard MATLAB fan, support vector machines are available in their Bioinformatics Toolbox, and classification and decision trees in their Statistics Toolbox.)