Improving News Traders using CRF: Using Conditional Random Fields to reduce Feature Space
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Since financial markets react to news very quickly it is necessary to react even quicker in order to make money. Normal text data has very high dimensionality and reducing the number of features needed to classify a document reduces the time needed to do so. This thesis looks at a way to reduce the feature space by use of Conditional Random Fields. To do this, a new data set is made using mandatory stock messages released to the Oslo Stock Exchange. The messages are combined with financial data on all trades completed in a three-year period. A Conditional Random Field is trained on the textual data and used to extract important features. The features are then used to train a Support Vector Machine classifier and a Random Forest classifier. Both are evaluated against using all features and using randomly selected features. The thesis find that reducing the number of features results in a 4 percentage point reduction in accuracy and a 81,25 reduction in run time. We conclude that it is possible to reduce the feature space without significant reduction in accuracy. We also conclude that using this method is not good enough for making a significant profit on the financial market. This is consistent with earlier work on feature reduction.