Learning multi-market microstructure from order book data |
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Authors: | Geonhwan Ju Kyoung-Kuk Kim Dong-Young Lim |
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Affiliation: | 1. Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, South Koreagh.ju@kaist.ac.krhttps://orcid.org/0000-0001-9661-5162;3. Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, South Koreahttps://orcid.org/0000-0002-9661-8707;4. Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, South Koreahttps://orcid.org/0000-0002-4677-965X |
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Abstract: | 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. |
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Keywords: | High-frequency data Limit order book Neural network Lead–lag relationship Market microstructure |
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