1. Financial market instability: price shocks
and the role of high-frequency trading
Sergey Ivliev
IASC-ARS 2015, 17 December 2015
2. Motivation
Financial markets are intrinsically unstable and are characterized by
many “large” price fluctuations at all time scales
Many of these shocks are not explained by public news:
Joulin et al. (2008) investigated one minute returns of a large set of
liquid US stocks. They detected eight 4-sigma jumps per stock per day
and one 8-sigma jump every one day and a half per stock. Moreover they
find that “neither idiosyncratic news nor market wide news can explain
the frequency and amplitude of price jumps”
Financial market stability is a growing concern of regulators, investors,
exchanges, media and politicians
3. Definition
Market shock – marker equilibrium disruption, caused by demand or supply
change, leading to abrupt price change
4. 4
“FLASH DUMP” В АКЦИЯХ APPLE 25.01.2013
AAPL “Flash Dump” 25.01.2013
5. 5
“FLASH DUMP” В АКЦИЯХ APPLE 25.01.2013
http://www.nanex.net/aqck2/4689.html
Dollar Flash Crash 18.03.2015
7. Research questions
What are the characteristics of price shocks on equity market?
How are they interrelated?
What is the behavior of HFT at times of price shocks?
M.Frolova, S.Ivliev, F.Lillo. Price Shocks on Multiple Time Scales:
the Russian Stock Market case study.
8. Historical data sample (1):
We use database recording of all transactions and orders submission/ cancellation events
of 29 blue chip stocks (included in MICEX Index in 2010) traded on the MICEX in 2010
(82 trading days).
The selected stocks are the most capitalized and liquid on the Russian market, yielding
80% of the total market cap and most of the traded volume.
Sample: 310 mln orders; 32 mln trades.
Historical data sample (2):
We use database recording of all transactions and orders submission/ cancellation events
of 30 blue chip stocks traded on liquid equity market in Asia in 2012 (250 trading days).
Sample : 62 mln orders; 10 mln trades.
Data sample
9. Identification of market shocks
Time scales & Filters:
Hours scale (macro): Absolute & Relative filters
Source: G.Mu, W.Zhou, W.Chen and J. Kertesz (2010). Order flow dynamics around extreme
price changes on an emerging stock market
Minutes scale (meso): Minute returns exceeds S times local volatility
Source: A.Joulin, A.Lefevre, D.Grunberg, J-P Bouchaud (2010). Stock price jumps: news and
volume play a minor role
Tick scale (micro): NANEX filter
Source: N. Johnson, B.Tivnan (2011). Flash Crash Phenomena and a Taxonomy of Extreme
Behaviors, Eur. Phys. J. Special Topics 205 65-78
10. Y
ttp
ttptp
tr
)(
)()(
)(
Macro scale
n
i
i Ttv
n
Mtr
1
),(
1
)(
Relative filter:
Cumulative intraday returns exceeding M times the average volatility in the same period of the
day in the sample:
),( Ttvi – intraday volatility (s.d.) at moment t for a time window ∆𝑇at day i
M=6
Y=4%
Δt=∆𝑇=60 min
Parameters:
Shocks identified: 1820.
On average: 16 shocks/month per stock
Consistent with Kertesz et.al. (2010) results for Shenzhen Stock
Exchange in 2003: 20 shocks/month per stock
Absolute filter:
Cumulative intraday mid-price returns exceeding threshold Y within time window Δt:
12.
t
Tt
r
T
Str
)(
1
)(
Meso shocks filter:
1-minute return of mid-prices exceeds S times moving average of T previous returns:
S=8
∆𝑇=60 min
Parameters:
Shocks identified: 13368.
On average: 5,6 shocks/day per stock
3-times more frequent than recorded in Bouchaud et.al. (2010) for
NASDAQ, NYSE in 2006-2010: 0,75 shocks/day per stock
Meso scale
14. Price path @ shocks
Macro
0.95
0.96
0.97
0.98
0.99
1
1.01
-120 -100 -80 -60 -40 -20 0 20 40 60 80 100 120
Marketprice(=1atshocktime)
Time before and after the identified shock, minutes
0.999
1.004
1.009
1.014
1.019
1.024
1.029
1.034
1.039
1.044
1.049
-120 -100 -80 -60 -40 -20 0 20 40 60 80 100 120
Marketprice(=1atshocktime)
Time before and after the identified shock, minutes
Meso
0.995
0.996
0.997
0.998
0.999
1
1.001
-60 -40 -20 0 20 40 60
Marketprice(=1atshocktime)
Time before and after the identified shock, minutes
0.999
1
1.001
1.002
1.003
1.004
1.005
-60 -40 -20 0 20 40 60
Marketprice(=1atshocktime)
Time before and after the identified shock, minutes
15. Trading volume @ shocks
Macro
1
2
3
4
5
6
7
8
9
10
11
-120 -100 -80 -60 -40 -20 0 20 40 60 80 100 120
Tradingvolume
Time before and after the identified shock, minutes
1
2
3
4
5
6
7
-120 -100 -80 -60 -40 -20 0 20 40 60 80 100 120
Tradingvolume
Time before and after the identified shock, minutes
0
1
2
3
4
5
6
7
8
-60 -40 -20 0 20 40 60
Tradingvolume
Time before and after the identified shock, minutes
0
1
2
3
4
5
6
7
-60 -40 -20 0 20 40 60
Tradingvolume
Time before and after the identified shock, minutes
Meso
16. Relative bid-ask spread @ shocks
Macro
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
-120 -100 -80 -60 -40 -20 0 20 40 60 80 100 120
Relativebid-askspread
Time before and after the identified shock, minutes
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
-120 -100 -80 -60 -40 -20 0 20 40 60 80 100 120
Relativebid-askspread
Time before and after the identified shock, minutes
0.80
0.85
0.90
0.95
1.00
1.05
1.10
1.15
1.20
-60 -40 -20 0 20 40 60
Relativebid-askspread
Time before and after the identified shock, minutes
0.85
0.90
0.95
1.00
1.05
1.10
1.15
-60 -40 -20 0 20 40 60
Relativebid-askspread
Time before and after the identified shock, minutes
Meso
17. Buy imbalance @ shocks
sb
b
VV
V
I
Vb – buy orders quantity in the limit order book at the end of each time moment;
Vs – sell orders quantity in the limit order book at the end of each time moment
Macro
Meso
0,99
1,04
1,09
1,14
1,19
1,24
1,29
1,34
-60 -40 -20 0 20 40 60
Buyimbalance(orders)
Time before and after the identified shock, minutes
0,70
0,75
0,80
0,85
0,90
0,95
1,00
-60 -40 -20 0 20 40 60
Buyimbalance(orders)
Time before and after the identified shock, minutes
0,990
0,995
1,000
1,005
1,010
1,015
1,020
1,025
1,030
1,035
-60 -40 -20 0 20 40 60
BuyImbalance(orders)
Time before and after the identified shock, minutes
0,96
0,97
0,98
0,99
1,00
1,01
1,02
-60 -40 -20 0 20 40 60
BuyImbalance(orders)
Time before and after the identified shock, minutes
20. NANEX filter:
down-draft event: the stock had to tick down at least 10 times before ticking up - all
within 2 seconds and the price change had to exceed 0.8%
up-draft event: tick up at least 10 times before ticking down - all within 2 seconds and
the price change had to exceed 0.8%
Shocks identified: 369.
On average: 4,5 shocks/per day
Comparable to Nanex frequency of 4.13 shocks per day in 2010.
Micro scale
25. shocks embedded?
* Only included those events with the time interval more than 60 minutes to previous event
Meso into Macro
Event type
Macro events
quantity*
Time bounds
Meso events in
time bounds
Total meso
events quantity
Share of
embedded
events
Up-draft 93 ±1 hour 35 3354 1%
Down-draft 68 ±1 hour 35 3406 1%
Event type
Meso events
quantity
Time bounds
Micro events in
time bounds
Total micro
events quantity
Share of
embedded
events
Up-draft 3354 ±1 min 42 157 26%
Down-draft 3406 ±1 min 44 212 21%
Micro into Meso
26. Conclusion (1)
A large number of shocks are present in financial markets
Shocks frequency is pretty consistent across markets
Less shocks in liquid stocks
Micro shocks are likely to be embedded into Meso shocks
Meso shocks are not embedded into Macro shocks and have different nature
Next research questions:
Are HFTs involved?
Can shocks be predicted?
27. In 30 liquid stocks of one of the equity markets in Asia…
1091 8-sigma 50bp shocks identified on 1-minute time scale, this correspond on average to one
shock per 7 trading days per stock
1029 of “isolated” shocks (501 up / 529 down)
no seasonal effects during trading year
shocks are more frequent at the opening and closing of the trading day (U-shape)
only 1 systemic shock (4 stocks jump together) recorded
average price change during upshocks and downshocks is approximately 0.7%, there is a 0.2%
price-relaxation after shock (relative to shock price)
0.990
0.992
0.994
0.996
0.998
1.000
1.002
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
Marketprice(p=1atshocktime)
Time before andafter the identifiedshock, minutes
0.998
0.999
1.000
1.001
1.002
1.003
1.004
1.005
1.006
1.007
1.008
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
Marketprice(p=1atshocktime)
Time before andafter the identifiedshock, minutes
Shocks identification
28. Shock profiles
relative bid-ask widens on average 2x times at the shock minute and doesn’t fully relax to pre-
shock level after one hour
trading volume peaks at shock time, exceeding by 12x times normal trading activity for positive
events and 10x times for negative events
0
2
4
6
8
10
12
14
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
Tradingvolume
Time before andafter the identifiedshock, minutes
0
2
4
6
8
10
12
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
Tradingvolume
Time before andafter the identifiedshock, minutes
0.500
0.700
0.900
1.100
1.300
1.500
1.700
1.900
2.100
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
Relativespread
Time before andafter the identifiedshock, minutes
0.500
0.700
0.900
1.100
1.300
1.500
1.700
1.900
2.100
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
Relativespread
Time before andafter the identifiedshock, minutes
29. Shock profiles
number of trades increases more than 12 times compared to pattern value during up shocks
and more than 9 times during down shocks
number of submitted orders increases 8 times compared to pattern for up shocks and 7 times
for down shocks
0
2
4
6
8
10
12
14
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
0
1
2
3
4
5
6
7
8
9
10
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
0
1
2
3
4
5
6
7
8
9
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
0
1
2
3
4
5
6
7
8
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
30. False positive rate
Truepositiverate
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
AUC= 61.01 %
Shock profiles
significant imbalance of buy orders in the effective market orders flow up to 80% during
positive events and down to 20% during negative events
Though there are precursors in buy imbalance behavior, buy imbalance has poor predictive
power and generate a lot of noisy signals
0.30
0.40
0.50
0.60
0.70
0.80
0.90
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
BuyImbalance
Time before andafter the identifiedshock,minutes
0.10
0.20
0.30
0.40
0.50
0.60
0.70
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
BuyImbalance
Time before and after the identified shock, minutes
Predicted
Actual SHOCK NO SHOCK
SHOCK 156 112
NO SHOCK 626 209 883 513
Predictive power of buy imbalance for down shocks at minute t=-1
31. HFT behavior during shocks
High Frequency Traders…
increase trading volume before and during the shock, and decrease it after the shock
increase the cancellation rate before and during the shock as well as the ratio between
cancellation and limit orders
increase the average distance of the new limit orders from the corresponding best price (or the
spread) before, during, and after the shock
On an average number of buy aggressive orders submitted by HFTs during up shocks exceeds
pattern value 4.5 times, number of sell aggressive orders submitted during down shocks exceeds
pattern value 3.5 times
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
0.0
0.5
1.0
1.5
2.0
2.5
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
0.0
0.5
1.0
1.5
2.0
2.5
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
Aggressive orders submitted by HFTs (buy orders during up shock - left, sell orders during down shocks – right)
32. HFT behavior during shocks
The empirical study of the HFT share on the best bid and ask shows that HFT tend to modify
their liquidity provision strategy after the shocks, but not prior to them.
When the price goes abruptly up, the share of HFT volume at the best ask declines, while it
increases at the best bid
When the price goes abruptly down, the share of HFT volume at the best bid declines, while it
increases at the best ask
One possible explanation is that HFTs withdraw the liquidity from the side of the book affected
by price shock (caused by big trades), being concerned about the possible informativeness of
these trades; another explanations is that they are trend-following.
25%
30%
35%
40%
45%
50%
-240 -180 -120 -60 0 60 120 180 240
Minute (shock at t=0)
HFT Share at Best Bid HFT Share at Best Ask
25%
30%
35%
40%
45%
50%
-240 -180 -120 -60 0 60 120 180 240
Minute (shock at t=0)
HFT Share at Best Bid HFT Share at Best Ask
Down shocksUp shocks
33. We studied the 8-sigma 50bp+ market shocks on 1-minute time scale in 30 liquid
stocks in one of the Asian market
We found more than 1000 of these events per year, approximately 1 per week
per stock (3x less frequent than in US, 20x less frequent than in Russia)
There is significant imbalance of orders prior to the shock, but it is considered to
be weak precursor of shocks
High Frequency Traders increase trading volume before and during the shock,
and decrease it after the shock, increase the average distance of the new limit
orders from the corresponding best price
Market taking HFTs submit on average 4x times more aggressive orders than
normal at the moment of shock
Market making HFTs tend to remove liquidity from the book from the side
affected by shock (i.e. rebalancing their position)
Deliverable (iii). Report on HFT
Conclusions (2):
34. Perm Winter School
Perm Winter School 2016
Dates: 4-6 Feb 2016
Key-note speakers:
- Prof. Dr. Rosario Mantegna (CEU)
- Dr. Richard Olsen (Lykke)
- Dr. Ann-Christine Lange (Copenhagen
Business School)
More information:
permwinterschool.ru
