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Using full limit order book for price jump prediction

Kairat Mynbaev ()

MPRA Paper from University Library of Munich, Germany

Abstract: Institutional investors, especially high frequency traders, employ the order information contained in the Limit Order Book (LOB). The main purpose of the paper is to investigate how full information about the LOB can help in predicting the price jump. Normally, a full LOB contains total volumes of orders for hundreds of prices. Using the full information runs into the curse of dimensionality which manifests itself in multicollinearity, insignificant coefficients, inflated estimate variances and high computation time. Due to these problems, order volumes for prices that are distant from ask and bid prices are usually not used in prediction procedures. For this reason we call such information a silent crowd. Here we propose a summary measure of the silent crowd and quantify its influence on trade jump prediction. We use a realistically simulated LOB as a vehicle for experiments and logistic regression as the prediction tool. The full code in Matlab includes 18 blocks.

Keywords: Simulation; trade jump prediction; high frequency trading; logistic regression; limit order book (search for similar items in EconPapers)
JEL-codes: C25 C61 G12 (search for similar items in EconPapers)
Date: 2020-06
New Economics Papers: this item is included in nep-mst
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Published in Kazakh Mathematical Journal 2.20(2020): pp. 44-53

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