Machine Learning for Algorithmic Trading - Second Edition

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Word Embeddings for Earnings Calls and SEC Filings

In the two previous chapters, we converted text data into a numerical format using the bag-of-words model. The result is sparse, fixed-length vectors that represent documents in high-dimensional word space. This allows the similarity of documents to be evaluated and creates features to train a model with a view to classifying a document's content or rating the sentiment expressed in it. However, these vectors ignore the context in which a term is used so that two sentences containing the same words in a different order would be encoded by the same vector, even if their meaning is quite different.

This chapter introduces an alternative class of algorithms that use neural networks to learn ...