Analyzing the Importance of Broker Identities in the Limit Order Book Through Deep Learning.

Big Data
Samuel Ping-Man ChoiHie-Yiin Hung

Abstract

Limit order books (LOBs) have been widely adopted as a trading mechanism in global securities markets, and the degree of LOB transparency is one of the most studied topics in market design. In the past, this issue was mainly researched through the comparison of LOB transparency in a market before and after a policy change, although such instances were rare and occurred decades ago. This article analyzes the importance of broker identities (IDs) in the LOB with respect to price movement predictability by proposing a different approach. By analyzing raw LOB data, an enormous dataset of selected Hong Kong stocks is divided into two parts, namely the prices and order volumes (anonymous LOBs), and a list of broker IDs in the bid and ask queues. A deep learning model is then employed to predict the mid-price movement after 20 ticks. Our result indicates that the best F1 scores of the anonymous LOB and broker ID models are fairly high, ranging from 57.63% to 68.70% and from 53.70% to 59.39%, respectively. When comparing the performance of both datasets, surprisingly, the overall F1 prediction performance based solely on the broker ID dataset can reach, on average, 85.13% that of the anonymous LOB dataset. The contributions of this stu...Continue Reading

References

Oct 23, 1997·Neural Computation·S Hochreiter, J Schmidhuber
Jan 1, 1997·IEEE Transactions on Neural Networks·S LawrenceA D Back
Jan 7, 2016·Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society·Max Ortiz-CatalanBo Hakansson

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Software Mentioned

AnonymousLOB
BrokerID
Tensorflow
DeepLOB
Keras
ADAM
HFT

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