MoneyLab

  This is part II of the three-part series on building analytical models using financial news data to predict stock price movement (up or down). So far, we have explored the use of LSTM to predict stock price movement based on trend and momentum indicators. We also tried the n-gram TF-IDF scheme to model financial news data for predicting stock price movement. Unfortunately, both methods failed to pick up any useful signals that would help us trade profitably. For those who missed the two earlier posts, or would like to recap the analyses, you may access them through here and here. One issue with our earlier attempt to predict stock price movement using financial news was that the language model proposed in part I of the series was not sophisticated enough to understand the meaning of words nor the sequential dependencies of words in sentences. This post introduces word embedding and Bi-directional Long Short-Term memory (Bi-LSTM) to model financial news data that could overcome th