An Automated Framework for Incorporating News into Stock Trading Strategies

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Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction (2017, background): “In order for seeking maximized profit, stock investors continuously attempt to predict the future trends of market [20], which, however, is quite challenging due to highly volatile and non-stationary nature of the market [1].”
Automated trading with performance weighted random forests and seasonality (2014, background): “Inspiration for such trading systems comes from a variety of fields, ranging from fundamental analysis and econometric modelling to evolutionary computation, machine learning and even news mining (Nuij et al., 2013).”
Stock Price Prediction via Discovering Multi-Frequency Trading Patterns (2017, background): “Stock investors attempt to discover latent trading patterns in stock market to forecast the future price trends for seeking profit-maximization strategies [13, 22].”
Stock Market Prediction with Deep Learning: A Character-based Neural Language Model for Event-based Trading (2017, methodology): “A di ↵ erent approach to incorporate news into stock trading strategies was proposed by Nuij et al. (2014), which used an evolutionary algorithm to combine trading rules us-ing technical indicators and events extracted from news with expert-deﬁned impact scores.”