In this section, we will learn word and phrase vectors from annual US Securities and Exchange Commission (SEC) filings using gensim to illustrate the potential value of word embeddings for algorithmic trading. In the following sections, we will combine these vectors as features with price returns to train neural networks to predict equity prices from the content of security filings.
In particular, we use a dataset containing over 22,000 10-K annual reports from the period 2013-2016 that are filed by listed companies and contain both financial information and management commentary (see Chapter 3, Alternative Data for Finance). For about half of the 11-K filings for companies, we have stock prices to label the data for predictive modeling (see references about data sources and the notebooks in the sec-filings folder for details).
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