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• W2340183428 abstract "This paper develops a new neural architecture for modeling distributions (i.e., distributions on R^d) which is computationally efficient and specifically designed to take advantage of the structure of limit order books. The new architecture yields a low-dimensional model of price movements deep into the limit order book, allowing more effective use of information from deep in the limit order book (i.e., many levels beyond the best bid and best ask). This spatial neural network models the joint distribution of the state of the limit order book at a future time conditional on the current state of the limit order book. The neural outperforms other models such as the naive empirical model, logistic regression (with nonlinear features), and a standard neural architecture. Both neural networks strongly outperform the logistic regression model. Due to its more effective use of information deep in the limit order book, the neural especially outperforms the standard neural in the tail of the distribution, which is important for risk management applications. The models are trained and tested on nearly 500 stocks. Techniques from deep learning such as dropout are employed to improve performance. Due to the significant computational challenges associated with the large amount of data, models are trained with a cluster of 50 GPUs." @default.
• W2340183428 created "2016-06-24" @default.
• W2340183428 creator A5052064725 @default.
• W2340183428 date "2016-01-01" @default.
• W2340183428 modified "2023-09-26" @default.
• W2340183428 title "<i>Extended Abstract</i>: Neural Networks for Limit Order Books" @default.
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• W2340183428 doi "https://doi.org/10.2139/ssrn.2710331" @default.
• W2340183428 hasPublicationYear "2016" @default.
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