Deep Learning from Earning Calls for Stock Price Movement Prediction

A system for predicting stock price movements, the system comprising:

• a storage device configured to store program instructions;
• and
• one or more processors operably connected to the storage device and configured to execute the program instructions to cause the system to:
• extracting a number of sentences from a number of earning call transcripts related to a stock of a publicly traded company;
• encoding, by a first neural network embedding layer, each extracted sentence into a sentence vector;
• calculating, by a first neural network attention layer, an earning call representation vector that is a weighted sum of the sentence vectors;
• encoding, a by a recurrent neural network, a time series vector of historical prices for the stock over a specified time period;
• assigning, by a second neural network attention layer, weights to time steps comprising the time series vector;
• encoding, by a second neural network embedding layer, an industry sector vector representing categorical features of an industry sector to which the company belongs;
• calculating a concatenated vector from the earning call representation vector, the time series vector, and industry sector vector;
• and
• predicting, by a discriminative network according to the concatenated vector, a direction of price movement of the stock over a specified future time period after a new earning call conference.

A computer program product predicting stock price movements, the computer program product comprising:

• a computer-readable storage medium having program instructions embodied thereon to perform the steps of:
• extracting a number of sentences from a number of earning call transcripts related to a stock of a publicly traded company;
• encoding, by a first neural network embedding layer, each extracted sentence into a sentence vector;
• calculating, by a first neural network attention layer, an earning call representation vector that is a weighted sum of the sentence vectors;
• encoding, a by a recurrent neural network, a time series vector of historical prices for the stock over a specified time period;
• assigning, by a second neural network attention layer, weights to time steps comprising the time series vector;
• encoding, by a second neural network embedding layer, an industry sector vector representing categorical features of an industry sector to which the company belongs;
• calculating a concatenated vector from the earning call representation vector, the time series vector, and industry sector vector;
• and
• predicting, by a discriminative network according to the concatenated vector, a direction of price movement of the stock over a specified future time period after a new earning call conference.