Abstract
This study examines the interaction between dynamic limit order placement activities and market quality around the two system upgrades by the Australian Securities Exchange (ASX) which aims at reducing the latency of trades. We show that after the 2006 system upgrade from Stock Exchange Automated Trading System to Integrated Trading System, liquidity falls and short-term volatility heightens. Lower latency provides capacity for traders to position themselves to take liquidity when it is cheap. After the second upgrade in 2010 (launch of ASX Trade), the harmful effect reverses. Our evidence shows that in large-capitalisation stocks, algorithmic trading/high-frequency trading provides liquidity and stabilises the price when short-term volatility is high. Since we find that the market quality could be unfavourably affected after a system upgrade (i.e., the 2006 system upgrade), regulators need to be prepared for near-time reactions and rapid investigations in the event of market stress.
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Notes
ASX200 is Australia’s primary stock market index and contains the top 200 ASX listed companies by float-adjusted market capitalisation. ASX200 index came into operation in 2000 and acts as the benchmark for Australian equity performance.
According to Frino et al. (2011), AT/HFT participation in Australia, in terms of daily dollar value proportion of HFT trades, ranged between 30 and 80% for the period of October 2006 and October 2009. According to ASIC reports, as of November 2010, ASX participants estimate their levels of AT/HFT at 30–40% of total volumes traded. From May to July 2012, the percentages of HFT in the total number of orders for new, revised, and cancelled orders are estimated at 61%, 60%, and 59%, respectively.
For robustness, we extend the event window to 2 months and our conclusion remains largely unchanged.
The ratio can exceed 100% due to the possibility of multiple revisions for each submission.
This definition is somewhat arbitrary. Hasbrouck and Saar (2013) argue that longer runs represent low-latency activity. For robustness, we include all runs with messages > 1. For example, a limit order submission that is followed by a full execution has the message of 1. If the order is revised once prior to its full execution, it has the message of 2. Unlike Hasbrouck and Saar (2013), we are utilising order book data that has a unique order identifier which allows us to track down the order events with no issue of misclassification, hence our measure of strategic runs is fairly accurate. Our conclusion, however, becomes less consistent with this new definition as it invariably inflates the value of DLOPA when limit orders stay in the book for a prolonged period of time. The introduction of noise to the measure makes it difficult to generate a sensible conclusion. A viable avenue for future research is to improve the measure by considering a different time-weighting mechanism given that our data contains a unique order identifier.
Unlike Hasbrouck and Saar (2013) who impute links between cancellation and resubmission of orders based on how close between consecutive events (within 100 ms) where the orders matched with order types and size, SIRCA provides unique order identifiers which allow us to track the event sequence (submission, revision, cancellation or execution) of each order without error.
For details of how the time-weighting is computed, see Footnote 16 of Hasbrouck and Saar (2013).
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Le, A.T., Le, TH., Liu, WM. et al. Dynamic limit order placement activities and their effects on stock market quality. Ann Oper Res 330, 155–175 (2023). https://doi.org/10.1007/s10479-021-04282-y
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DOI: https://doi.org/10.1007/s10479-021-04282-y