The Impact of Structured Event Embeddings on Scalable Stock Forecasting Models
Janderson Borges do Nascimento, Marco Antônio Pinheiro de Cristo · 2015
According to the efficient market hypothesis, financial prices are unpredictable. However, meaningful advances have been achieved on anticipating market movements using machine learning techniques. In this work, we propose a novel method to represent the input for a stock price forecaster. The forecaster is able to predict stock prices from time series and additional information from web pages. Such information is extracted as structured events and represented in a compressed concept space. By using such representation with scalable forecasters, we reduced prediction error by about 10%, when compared to the traditional auto regressive models.