PMID- 33202194
OWN - NLM
STAT- MEDLINE
DCOM- 20211025
LR  - 20211025
IS  - 2167-647X (Electronic)
IS  - 2167-6461 (Linking)
VI  - 9
IP  - 2
DP  - 2021 Apr
TI  - Analyzing the Importance of Broker Identities in the Limit Order Book Through 
      Deep Learning.
PG  - 89-99
LID - 10.1089/big.2020.0053 [doi]
AB  - 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 study are twofold. First, a 
      machine learning-based tool for finance researchers is proposed to quantitatively 
      measure the price predictability of LOB features, and the results of the impact 
      of LOB transparency on traders' profitability are novel as this study is 
      empirical. Second, the empirical result strongly suggests that the broker ID 
      queues in the LOB consist of significant information content for price 
      prediction, and thus, the study provides insights for regulators to determine the 
      appropriate degree of LOB transparency to guarantee a fair market for all 
      investors.
FAU - Choi, Samuel Ping-Man
AU  - Choi SP
AD  - Lee Shau Kee School of Business and Administration, and The Open University of 
      Hong Kong, Homantin Kowloon, Hong Kong.
FAU - Chan, Yin-Hei
AU  - Chan YH
AD  - School of Science and Technology, The Open University of Hong Kong, Homantin 
      Kowloon, Hong Kong.
FAU - Lam, Sze-Sing
AU  - Lam SS
AD  - Lee Shau Kee School of Business and Administration, and The Open University of 
      Hong Kong, Homantin Kowloon, Hong Kong.
FAU - Hung, Hie-Yiin
AU  - Hung HY
AD  - Lee Shau Kee School of Business and Administration, and The Open University of 
      Hong Kong, Homantin Kowloon, Hong Kong.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20201117
PL  - United States
TA  - Big Data
JT  - Big data
JID - 101631218
SB  - IM
MH  - Books
MH  - *Deep Learning
MH  - Machine Learning
OTO - NOTNLM
OT  - broker identities
OT  - deep learning
OT  - limit order book
OT  - market transparency
OT  - microstructure
OT  - price predictability
EDAT- 2020/11/18 06:00
MHDA- 2021/10/26 06:00
CRDT- 2020/11/17 20:05
PHST- 2020/11/18 06:00 [pubmed]
PHST- 2021/10/26 06:00 [medline]
PHST- 2020/11/17 20:05 [entrez]
AID - 10.1089/big.2020.0053 [doi]
PST - ppublish
SO  - Big Data. 2021 Apr;9(2):89-99. doi: 10.1089/big.2020.0053. Epub 2020 Nov 17.