Learning multi-market microstructure from order book data

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In this paper, we investigate market behaviors at high-frequency using neural networks trained with order book data. Experiments are done intensively with 110 asset pairs covering 97% of spot-futures pairs in the Korea Exchange. An efficient training scheme that improves the performance and training stability is suggested, and using the proposed scheme, the lead-lag relationship between spot and futures markets are measured by comparing the performance gains of each market data set for predicting the other. In addition, the gradients of the trained model are analyzed to understand some important market features that neural networks learn through training, revealing characteristics of the market microstructure. Our results show that highly complex neural network models can successfully learn market features such as order imbalance, spread-volatility correlation, and mean reversion.
Publisher
ROUTLEDGE JOURNALS
Issue Date
2019-09
Language
English
Article Type
Article
Citation

QUANTITATIVE FINANCE, v.19, no.9, pp.1517 - 1529

ISSN
1469-7688
DOI
10.1080/14697688.2019.1622305
URI
http://hdl.handle.net/10203/270063
Appears in Collection
IE-Journal Papers(저널논문)
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