The Impact of Structured Event Embeddings on Scalable Stock Forecasting Models

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.

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