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Title: A generative model of a limit order book using recurrent neural networks Authors:  Hanna Hultin - KTH Royal Institute of Technology (Sweden) [presenting]
Henrik Hult - KTH Royal Institute of Technology (Sweden)
Alexandre Proutiere - KTH Royal Institute of Technology (Sweden)
Ala Tarighati - SEB (Sweden)
Samuel Samama - SEB (Sweden)
Abstract: A generative model based on recurrent neural networks for the complete dynamics of a limit order book is developed. The model captures the dynamics of the limit order book by decomposing the probability of each transition into a product of conditional probabilities of order type, price level, order size, and time delay. Each such conditional probability is modeled by a recurrent neural network. Several evaluation metrics for generative models related to trading execution are introduced. Using these metrics, it is demonstrated that the generative model can be successfully trained to fit both synthetic and real data from the Nasdaq Stockholm exchange.