Stock Market Prediction with Deep Learning: A Character based Neural Language Model for Event based Trading
In the last few years, machine learning has become a very popular tool for analyzing financial text data, with many promising results in stock price forecasting from financial news, a development with implications for the E cient Markets Hypothesis (EMH) that underpins much economic theory. In this work, we explore recurrent neural networks with character level language model pre training for both intraday and interday stock market forecasting. In terms of predicting directional changes in the Standard Poor's 500 index, both for individual companies and the overall index, we show that this technique is competitive with other state of the art approaches.
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