Classifying Financial News With Neural Networks
نویسنده
چکیده
One of the biggest challenges facing financial research and trading organizations is how to well exploit unstructured financial information such as textual announcements. The automatic classification of this type of data poses many challenges for learning systems because the feature vector used to represent a document must capture some of the complex semantics of natural language. In this paper we discuss the use of neural networks in the classification of financial news. The input dimensionality has been reduced using the 2 χ statistic. The generalization was controlled using a number of cross-validation sets, one for each experiment.
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