Low-Latency Trading and Price Discovery without
Trading:
Evidence from the Tokyo Stock Exchange
the Pre-Opening Period and the Opening Batch Auction
February 26, 2016
Mario Bellia
Loriana Pelizzon
SAFE - Goethe University
Goethe University and
Ca’ Foscari University of Venice
Marti G.
Subrahmanyam
New York University
Jun Uno
Darya Yuferova
Waseda University and
Ca’ Foscari University of Venice
Erasmus University
Agenda
1.
2.
3.
4.
5.
6.
7.
Motivation
Literature Review and Hypotheses
Summary of Findings
Data and the TSE market architecture
HFT Identification Strategy
Empirical Analysis
Conclusion
Motivation
• Growing presence of High Frequency Traders
(HFTs) have prompted the debate on the switch
from the current continuous auction to a periodic
auction.
The key question:
• in the batch auction, low-latency traders still
participate in the equity market?
• How does the presence of low-latency traders
contribute to price discovery in the opening call
auction?
INSTITUTIONAL BACKGROUND
Tokyo Stock Exchange’s Pre-opening
Pre-opening
8 AM
Morning session
9 AM
Call auction
Pre-opening
11:30
Afternoon session
12:30
3:00 PM
Call auction
• TSE starts receiving orders at 8 am, and pre-opening quotes
are disseminated.
• At 9 am, opening single price auction kicks-off, about 5-10% of
total daily volume is executed at the opening price for our
sample stocks.
Role of pre-opening quotes
• The purpose of disseminating the pre-opening quotes is to provide guide for
today’s opening price. (TSE)
• Execution is not allowed during the pre-opening period
although orders are crossed.
• Definition of best ask and bid is different from continuous
session. They are indication of opening price.
• Visibility of order flows Best quotes and status of the entire
book
• Order submission in the pre-opening period can be very different from that in
the continuous session
• No time priority
• Free cancellation without fear of execution
These factors may induce strategic actions as the opening time is
approaching.
Literature on market pre-opening and HFT
1.
Continuous vs. Batch Auction
• Budish, Cramton, and Shim(2015)
• Fricke and Gerig (2014)
2.
Price discovery in the pre-opening
•
•
•
•
3.
Biais, Hillion and Spatt (1999)
Cao, Ghyles, and Hatheway (2000)
Ciccotello and Hatheway (2000)
Barclay and Hendershott (2003)
Speed advantage of HFTs (theory)
•
•
•
•
•
4.
Jovanovic and Menkveld (2012)
Gerig and Michayluk (2013)
Ait-Sahalia and Saglam (2014)
Biais, Foucault, and Moinas (2015)
Foucault and Hombert and Rosu (2015)
Speed advantage of HFTs (empirics)
•
•
•
•
Hirschey (2013)
McInish and Upson (2012)
Brogaard, Hendershott, and Riordan (2014)
Foucault, Kozhan, and Tham (2014)
Research question on HFT and Price Discovery
No other papers that investigate the presence of HFT
in the pre-opening session and opening call auction
Questions:
• In the absence of trading, do low-latency traders
(including HFTs) still participate in the market preopening period (and opening call auction)?
• If they do participate,
• whether they are more or less active in the pre-opening
period than during the continuous session that follows,
• how and precisely when do they participate during the
pre-opening period,
• how does the presence of low-latency traders contributes
to price discovery in the pre-opening period.
Summary of Findings
• We classify traders based on speed and inventory
groups on a stock-day basis.
• We show that fast traders participate to the pre-opening
period to a lesser extent:
• only 25% of those that could be considered fast
traders with small inventories during the continuous
section participate to the pre-opening section.
• The fast traders largely participate during the first and
the last 10 minutes in the pre-opening.
• Fast traders contribute to price discovery significantly.
Data, sample stocks and period
• Tick-by-tick data: Thompson Reuters and NIKKEI
• Order data with server ID: TSE
• Quotes, Trades, Orders, sever ID
• Universe
• 97 Japanese stocks listed in Tokyo Stock (TOPIX100)
• Excluding three stocks from TOPIX100 which have
primary market other than TSE
• Period
• April and May 2013
Virtual Server (VS) ID
• We utilize a novel dataset on ID of the virtual servers
provided by the Tokyo Stock Exchange, which allows us to
distinguish trading desk activities individually.
• A virtual server is a logical device that needs to be set up
between the computer systems of the market participant
and the exchange, in order to trade.
Matching Engines
Multiple Servers in the co-location
connects to TSE matching engines
Stocks
F to M Stocks
F to M Stocks
A to E Stocks
N to V
Stocks
A to E Stocks
N to V Stocks
W to Z TSE Matching Engines Stocks
W to Z Trading Desk Group of Servers = Trading Desk
3021 entities in our sample period
Total number
of servers is
5,580 VS.
Number of Servers Traders use
The larger the size of operation (#servers),
the lower the latency.
Traders classification
Classification is stock-day basis using data of the
same day continuous session.
Two proxies.
(1) “speed” = time elapsed between two consecutive
orders to same stock
- FAST, MEDIUM, and SLOW.
(2) “end of day inventory” = absolute ratio of buy
minus sell volume to buy plus sell volume per
stock
- LARGE, MEDIUM, SMALL, and NO TRADING.
Classification
Speed
Inventory
Average # of
traders
FAST
LARGE
MEDIUM
SMALL
NO_TRADE
Latency
Inventory
79
91
94
43
0.04
0.02
0.02
0.04
100.0%
66.8%
16.4%
MEDIUM
LARGE
MEDIUM
SMALL
NO_TRADE
98
81
76
50
11.63
10.28
9.41
12.73
100.0%
65.7%
17.0%
SLOW
LARGE
MEDIUM
SMALL
NO_TRADE
214
43
35
38
4035.39
2393.04
2398.59
2579.21
100.0%
64.9%
16.2%
Note: Latency Unit: Second
• Fast speed and small inventory traders as HFT market makers,
• fast speed and medium inventory traders as HFT position takers.
Day-to-day Transition Matrix
Date t
LARGE
Date t-1
FAST
LARGE
MEDIUM
SMALL
NO_TRADE
24.83%
12.82%
8.29%
15.14%
FAST
MEDIUM SMALL
15.04%
31.47%
23.28%
3.66%
10.15%
23.84%
41.87%
3.51%
NO_TRADE
8.20%
1.73%
1.56%
35.84%
Within the same speed group, ignoring the differences in inventory we observe more
persistence: on average in 63.44% of cases traders tend to remain in the same speed group.
Within the same inventory group ignoring the speed dimension, in 46.96% of the cases,
traders tend to remain
Average persistency is 28.12%.
Question 1: Do low latency traders present in the
Pre-opening and Call Auction :
YES, but lesser extent
Presence Ratio of Traders in the pre-opening
60
50
40
30
20
10
0
Large
Medium
Small
FAST
Large
Medium
Small
Medium
Large
Medium
Slow
Small
Order submissions in the pre-opening
April-May 2013, 8:00-8:59am (second-by-second)
Impossibile v isualizzare l'immagine.
Order submissions in the pre-opening
April-May 2013, 8:00-8:59am (second-by-second)
Impossibile v isualizzare l'immagine.
Flow of order submission (new orders and cancellation)
in the last ten minutes of the prepre-opening session
sessi
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Order flow in the last 1000 millisecond
(number of orders, 97 stocks over 41 days)
500 millisecond to 9 am
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130 millisecond to
9 am
Absolute Deviation from the opening price
,
−1
100
Where:
Md,s is mid-quote at time s on day d
Od is opening price on day d
Average Absolute Deviation from the opening price
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,
−1
100
Aggressive Orders
• “Aggressive orders” = those orders with the potential to
impact the prevailing quotes.
• There are four cases in which we categorize orders as
aggressive:
1. all market orders;
2. a limit buy order with a limit price greater than or
equal to the prevailing best bid;
3. a limit sell order with a limit price less than or equal to
the prevailing ask;
4. any orders submitted at a time when the best bid
equals the best ask
Weighted Price discovery contribution
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• Price discovery contribution (PDC)
Since Deviation is absolute term, so negative PDC
indicates narrower deviation which is interpreted as
contribution.
• Weighted Price Discovery Contribution (WPC)
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Weighted-average Price Discovery
by Speed group, showing without minus sign
WPC 8:10-8:59 AM
WPC 8:00-8:59 AM
12,00%
45,00%
40,00%
10,00%
35,00%
8,00%
30,00%
25,00%
6,00%
20,00%
4,00%
15,00%
10,00%
2,00%
5,00%
0,00%
0,00%
Fast
Medium
指値注文
成行注文
Slow
Fast
Medium
Limit Orders
Market Orders
Slow
Panel Analysis: What type of orders
contribute to PD?
Dependent variable: change in the absolute deviation in
each 100 millisecond-stock-day, the last value of the midquote.
Explanatory variable: activity for each stock-day: # of new
order, cancellation, and price revision for buy and sell
market and limit orders submitted by each group of traders
28
Panel regression
Impossibile v isualizzare l'immagine.
Change in Deviationj,k,t = the change in the deviation of the
mid-quote from the opening price for stock on date , is
the 100-millisecond interval, and refers to a particular
group of traders.
Panel regression – Limit Orders submitted by Fast
traders
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Panel regression Market Orders submitted by
Fast traders
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Additional Analyses:
Any difference among stocks?
What drives it?
• Does it matter whether larger percentage of one group such
as Fast Small affects deviation from opening price?
• Does it matter if the stock has an ADR or not?
• FAST/SMALL are more active in the stock if it has an ADR.
• Does industry matter?
• Stocks from Machinery and Business Equipment attract
less activity (in most cases insignificant)
• How good does predictability of the pre-opening quotes
about opening price ?
The Unbiasedness Regression, Biais et al.(1999)
• Estimate the following equation per stock and per
second
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Close(T-1) to
Open(T) Return
Close(T-1) to
Pre-open quote(T,-j) Return
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v: Open (T), actual opening price,
P: pre-opening mid-quote (open time – j millisecond)
E(v|I0): Price Close (T-1) previous day’s closing price
The Unbiasedness Regression: β coefficient
• We split stocks into two groups based on the activity of
FAST/SMALL, FAST/MEDIUM, MEDIUM/SMALL, and
MEDIUM/MEDIUM traders.
• Group 1: when the activity of the four groups (fast
traders with small and medium inv. & medium speed
traders with small and medium inv.) crosses a
threshold of 30% (18 stocks)
• Group 2: all the other stocks (79 stocks).
The Unbiasedness Regression: average β
coefficient: Last 3 minutes
Impossibile v isualizzare l'immagine.
Conclusion 1
• FAST traders participate in the pre-opening period to a lesser
extent than in the continuous session.
• With respect to the total number of new orders, however, FAST
traders play a dominant role in the pre-opening period.
• FAST submit 51% of the total number,
• MEDIUM: 42%
• SLOW: 7%
Conclusion 2
• FAST/SMALL traders, that we identify as high-frequency market
makers and FAST/MEDIUM traders contribute the most to price
discovery
• Largely with new limit orders and price revisions
• Stocks in which these categories are dominant are those for
which prices converge faster to the opening price.
• FAST/SMALL aggressiveness is negatively related to the
aggressiveness of the other traders.
• Stocks characteristics matters for the aggressiveness of
FAST/SMALL traders strategies.
Traders classification
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Classification
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Impossibile v isualizzare l'immagine.
•
•
Fast speed and small inventory traders as HFT market makers,
fast speed and medium inventory traders as HFT position takers.
Classification
Impossibile v isualizzare l'immagine.
Impossibile v isualizzare l'immagine.
•
•
Fast speed and small inventory traders as HFT market makers,
fast speed and medium inventory traders as HFT position takers.
Deviation from the opening price - Groups of stocks
Two groups, based on the relative activity:
Impossibile v isualizzare l'immagine.
• Group 1: when the activity of the four
groups (fast traders with small and
medium inv. & medium speed traders
with small and medium inv.) crosses a
threshold of 30% (18 stocks)
• Group 2: all the other stocks (79 stocks).
Aggressive orders
Order Aggressiveness (a ratio in total order)
60
Ratio (%) = number of aggressive orders / number of orders
submitted by each group
50
40
30
20
10
0
Large
Medium
Small
Notrade
Large
Medium
FAST
Small
Notrade
Medium
Market Orders
Aggressive Limit Orders
Large
Medium
Slow
Small
Notrade