introduce the design of our proposed model to predict the directional movements of Standard & Poor’s 500 index and individual companies stock price using financial news titles. The model is named as Attention-based LSTM (At-LSTM) and shown in Fig \[fig:traditional-convolutional-network\].

Background
----------

### Bag of Words Model and Word Embedding

The Bag-of-Words model is a simplifying representation often used in Natural Language Processing and Information Retrieval [@Bag]. Also known as the vector space model. In this model, a text (such as a sentence or a document) is represented as a bag of its words, disregarding grammar and even word order but keeping multiplicity. The bag-of-words model is commonly used in document classification where the occurrence of each word represents a feature for training a classifier.

Word Embedding is the collective name for a set of language modeling and feature learning techniques in Natural Language Processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers [@Word]. Conceptually it involves a mathematical embedding from a space with one dimension per word to a continuous vector space with much lower dimension. Neural networks can be used to generate this mapping and the most common algorithm used are 