Neural network learns ‘universal model’ for stock-price moves
Relationships between order flow and price “are stable through time and across stocks and sectors”
Academics have used machine learning to create a “universal” model for predicting short-term stock-price changes – disproving common assumptions among dealers, hedge funds and high-frequency traders about how such models should be built.
In a recent study, Rama Cont, a professor at Imperial College London, and Justin Sirignano, assistant professor at the University of Illinois at Urbana-Champaign, used a neural network trained on two years of intraday data from Nasdaq’s limit order book to
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