Price jump prediction in a limit order book

By Ban Zheng, Eric Moulines, Frédéric Abergel. 2012

48 citations recorded by the source corpus.

A limit order book provides information on available limit order prices and their volumes. Based on these quantities, we give an empirical result on the relationship between the bid-ask liquidity balance and trade sign and we show that liquidity balance on best bid/best ask is quite informative for predicting the future market order's direction. Moreover, we de ne price jump as a sell (buy) market order arrival which is executed at a price which is smaller (larger) than the best bid (best ask) price at the moment just after the precedent market order arrival. Features are then extracted related to limit order volumes, limit order price gaps, market order information and limit order event information. Logistic regression is applied to predict the price jump from the limit order book's feature. LASSO logistic regression is introduced to help us make variable selection from which we are capable to highlight the importance of di erent features in predicting the future price jump. In order to get rid of the intraday data seasonality, the analysis is based on two separated datasets: morning dataset and afternoon dataset. Based on an analysis on forty largest French stocks of CAC40, we nd that trade sign and market order size as well as the liquidity on the best bid (best ask) are consistently informative for predicting the incoming price jump.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

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Citations in the corpus

Tensor representation in high-frequency financial data for price change prediction (2017, methodology): “Popular machine learning methods in HFT include regression analysis [1], [2], [3], [4], [5], multilayer feed forward network [6], [7], [8], convolutional neural network [9] , recurrent neural network [10], [11], [12].”
Modelling high-frequency limit order book dynamics with support vector machines (2015, methodology): “This includes the methods of logistic regression (Zheng et al. 2013) and SVMs (Cortes and Vapnik 1995, Joachims 1999).”
Sequence classification of the limit order book using recurrent neural networks (2017, result): “In contrast, several other studies state that such limit orders are informative(Parlour, 1998; Bloomﬁeld et al., 2005; Cao et al., 2009; Zheng et al., 2013; Kearns and Nevmyvaka, 2013; Cont et al., 2014; D. and Pollak, 2014).”
Benchmark Dataset for Mid-Price Prediction of Limit Order Book data (2017, methodology): “[57] utilize logistic regression in order to predict the inter-trade price jump.”
Ergodicity and scaling limit of a constrained multivariate Hawkes process (2013, unknown): “On the one hand, an extensive statistical study on the limit order book dynamics and limit order book information content can be found in [9, 26, 39, 19, 30, 10, 25, 14, 41]; on the other hand, many researchers have endeavored to propose different models, including equilibrium model, agent-based mod”
A High Frequency Trade Execution Model for Supervised Learning (2017, unknown): “In contrast, several other studies state that such limit orders are informative (Parlour, 1998; Bloomﬁeld et al., 2005; Cao et al., 2009; Zheng et al., 2013; Kearns and Nevmyvaka, 2013; Cont et al., 2014; Palguna and Pollak, 2014).”