Second Annual Conference on Financial Market Regulation, May 1, 2015
A Blessing or a Curse? The Impact of High
Frequency Trading on Institutional Investors
Lin Tong
Fordham University
Characteristics and Strategies of HFT – Concept Release
on Equity Market Structure, SEC (2010)
Characteristics:
High-speed
“Co-location”
Large intraday volume but finish the trading day as close to a
neutral position as possible
Popular Strategies:
Passive market making (rebate capturing)
Arbitrage
Structural vulnerabilities: e.g. “Flash orders”, Michigan
Consumer Confidence Index
Directional trading:
Order anticipation
Momentum ignition (Spoofing): “Flash Crash” of 2010
“May pose particular problems for long-term investors”
Institutional Investors’ Concerns about HFT
Concerns about increased trading costs
Norwegian Sovereign Wealth Fund, NY Times, October 2013
Order anticipation
“Institutional investors questioned whether our market
structure meets their need to trade efficiently and fairly, in
large size.”
2010 speech by Mary Schapiro, former SEC Chairperson.
Increased popularity of ATSs (Alternative Trading Systems)
Dark pools: account for 40% of stock trading volume now NY Times, April 6, 2013
“Flash Boys” boosts IEX trades 40%
Current Literature 1
Narrower bid–ask spreads, reduced volatility, and faster price
discovery
Hendershott et al. (JF 2011), Hasbrouck and Saar (JFM
2013), Menkveld (JFM 2013), Brogaard, Hendershott, and
Riordan (RFS 2014), Chaboud et al. (JF 2014), Boehmer et
al.(2014)
No additional costs to other investors:
Brogaard et al. (FR 2014): London Stock Exchange – HFT
participate in about 20% of the trading volume in 2008 and
rise to 40% in 2012 – much lower than the 70% in the U.S. in
2009
Korajczyk and Murphy (2015): Canadian stock exchanges;
Need to identify HFT and large trades in trade and quotes data
Malinova, Park and Riordan (2013): Retail investors
Current Literature 2
Problems of HFT:
Illusory liquidity: Kirilenko et al. (2011) and Easley et al.
(2011a)
Order anticipation: Hirschey (2013)
Increased volatility of quotes: Hasbrouck (2014)
Why institutions are not better off?
Institutions trade in large orders
Millions of shares per order
To reduce price impact, a large order is divided into small
pieces
Institutional trading costs
Bid-ask spreads are only a small component of trading costs.
Market impact can be 5-10 times of bid-ask spreads – major
component of institutions’ trading costs.
This Paper (1)
Examines the impact of HFT on trading costs of traditional
institutional investors in the U.S market
Mutual funds, pension funds, endowments, foundations (low
frequency traders)
I find that HFT increases the trading costs of institutional
investors.
One STDEV. increase of HFT activity is associated with an
increase in average trading costs by 1/3.
Various analyses to rule out reverse causality (i.e., HFT
attracted to stocks with high trading costs)
This Paper (2)
When and how does HFT increase institutional investors’ costs?
Opportunistic liquidity provision when there is large
institutional buy-sell imbalance
Short-lived liquidity: when institutional imbalance is higher,
HFT more intense, but HF traders keep zero positions at
market close
Expensive: HFT impact on trading costs is strongest when
institutions have large buying imbalance
Directional trading: momentum ignition or order anticipation
HFT impact most pronounced when HF trade directions are
not random
Less negative effects on institutions with good trading skills
HFT Data
NASDAQ HFT data
All trades on a sample of 120 randomly selected stocks,
2008-2009; 40 largecap, 40 midcap, and 40 smallcap
Identify trades by HF traders and non-HF traders
Papers that use this dataset: e.g., Brogaard, Hendershott and
Riordan (RFS 2014)
Measure of HFT intensity
HFT Intensity : total HFT volume for stock i on day t, divided
by the stock’s average trading volume in past 30 days
Why not separating HFT liquidity supply/demand? – An HFT
strategy can be combine with market and limit orders, e.g.,
order anticipation
Institutional Transaction Data
Ancerno institutional transaction dataset
Formerly known as Abel/Noser (Anand et al., RFS 2012, JFE
2013)
Execution-level data, including institution identity code, stock
ticker, stock price at placement, execution price, number of
shares executed, direction of trades, etc.
204 institutions traded on the 120 stocks during sample period
Measure of Trading Costs
Execution Shortfall = [(P1 − P0 )/P0 ] ∗ D
P1 : value-weighted execution price of the order
P0 : price at time of order placement
D = 1 for buy order and D = −1 for sell order
Captures bid-ask spreads, market impact, and price slippage
Volume-weighted average over all orders for a given stock on
a given day
process.pdf
T1: Summary Statistics
Average
Average
Average
Amihud
Average
Average
Market Capital (in billions)
HFT Total Trading Volume (in millions)
Execution Shortfall (in percentage)
Illiquidity Measeure
Institutional Order Size
Trades Per Order
All
Large Cap
Mid Cap
Small Cap
17.500
54.570
0.167
0.006
244,286
2.303
46.780
158.230
0.146
7.6E-05
487,871
3.126
1.590
3.650
0.163
0.002
154,823
1.861
0.400
0.380
0.196
0.019
63,943
1.850
Fig 1,2: HFT, Liquidity, and Execution Shortfall
HFT is positively correlated with liquidity
Trading costs are negatively correlated with liquidity.
HFT intensity and liquidity
HFT intensity
0.3
0.25
0.2
Illiquid stocks
0.15
Morderate liquid stocks
0.1
Liquid stocks
0.05
0
Large Stocks
Mid Stocks
Small Stocks
Execution Shortfall (in
%)
Execution shortfall and liquidity
0.4
0.3
Illiiquid stocks
0.2
Morderate liquid stocks
0.1
Liquid stocks
0
Large Stocks
Mid Stocks
Small Stocks
Fig 3: HFT, Liquidity, and Execution Shortfall (Cont’d)
Trading costs are positively correlated with HFT intensity
Execution Shortfall (in
%)
HFT intensity and execution shortfall
0.3
0.25
0.2
0.15
0.1
0.05
0
Low HFT intensity
Mid HFT intensity
High HFT intensity
Large Stocks
Mid Stocks
Small Stocks
Multivariate Panel Regression
Execution Shortfallit = αi +γt +a∗HFT Intensityit +b ∗Xit +it
HFT Intensityit : HFT activity for stock i on day t
Control variables Xit : 1) firm size, 2) book-to-market ratio, 3)
event dummy, 4) daily return volatility, 5) absolute daily
institutional trading imbalance, 6) prior 1-day, 1-month, and
12-month stock returns, 7) Amihud illiquidity ratio, 8) daily
dollar turnover, 9) average institutional order size, 10) absolute
institutional imbalance, 11) average trades per order, 12) prior
1-month market volatility, 13) lagged market daily return
Firm-fixed effects (αi ) and day-fixed effects (γt )
Two-way clustered standard errors
T3: HFT’s Impact on Execution Shortfall
Dependent Variable
Intercept
HFT Intensity
Log Market Cap
Book-to-Market Ratio
Prior 1-day Return
Prior 1-month Return
Prior 12-month Return
Amihud Illiquidity Ratio
Daily Return Volatility
Daily Dollar Turnover
Average Institutional Order Size
Absolute Institutional Imbalance
Average Trades Per Order
Prior 1-month Market Volatility
Prior 1-day Market Return
Day-fixed Effects
Stock-fixed Effects
Two-way Clustered Standard Deviations
Execution Shortfall
Execution Shortfall
Coefficient
t-value
Coefficient
t-value
0.025
0.336
-0.004
-5.978
-0.072
0.017
0.013
3.955
0.324
-0.007
0.743
0.271
0.000
0.285
-0.031
(0.24)
(4.48)
(-0.66)
(-0.95)
(-0.24)
(0.25)
(0.92)
(3.14)
(1.42)
(-1.66)
(1.37)
(2.56)
(0.16)
(3.24)
(-0.05)
-1.144
0.309
0.043
6.303
-0.178
-0.037
-0.004
4.687
0.046
-0.001
0.735
0.281
0.000
(-1.77)
(3.37)
(1.08)
(1.23)
(-0.64)
(-0.69)
(-0.26)
(3.36)
(0.30)
(-0.19)
(1.42)
(2.67)
(-0.44)
No
No
Yes
Yes
Yes
Yes
Endogeneity
Reverse causality: is HFT more prevalent among stocks with
high trading costs?
Unlikely since HFT likes liquidity – HFT are most popular
among large and liquid stocks (Fig. 1)
Unlikely since HFT withdrew at the 2008 short selling ban
when institutioanl trading costs are high.
Unlikely since Granger causality test results
Omitted variables: are there any unobserved factors that
influence both HFT and institutional trading costs?
Control for time- and firm-fixed effects (Table 3)
Control for stock and institutional trading characteristics
documented to affect liquidity/trading costs (Table 3)
Control for events that may jointly affect HFT and trading
costs (earnings announcements and M&A announcements)
T5: HFT’s Impact on Execution Shortfall on Event Days
Dependent Variable
Intercept
HFT Intensity*Event Dummy
HFT Intensity*No Event Dummy
Event Dummy
Log Market Cap
Book-to-Market Ratio
Prior 1-day Return
Prior 1-month Return
Prior 12-month Return
Amihud Illiquidity Ratio
Daily Return Volatility
Daily Dollar Turnover
Average Institutional Order Size
Absolute Institutional Imbalance
Average Trades Per Order
Day-fixed Effects
Stock-fixed Effects
Two-way Clustered Standard Deviations
Adjusted R-squared (%)
Number of Observations
Execution Shortfall
Coefficient
t-value
-1.129
0.155
0.375
0.058
0.041
6.284
-0.181
-0.037
-0.005
4.711
0.039
0.002
0.725
0.285
0.000
-(1.74)
(1.29)
(3.88)
(1.39)
(1.03)
(1.23)
-(0.65)
-(0.70)
-(0.31)
(3.37)
(0.26)
(0.24)
(1.40)
(2.69)
-(0.49)
Yes
Yes
Yes
3.49
54963
Fig 4,5: Short selling Ban - Sep 19, 2008
16 stocks in my sample are subjected to the ban.
Excution shortfall (in %) for banned and unbanned stocks
3
2
1
0
09/04/08
09/09/08
09/14/08
09/19/08
09/24/08
09/29/08
10/04/08
10/09/08
10/14/08
10/19/08
14/10/08
19/10/08
-1
-2
Banned Exec. Shortfall
Unanned Exec. Shortfall
HFT Activity for Banned and Unbanned Stocks
0.6
0.5
0.4
0.3
0.2
0.1
0
04/09/08
09/09/08
14/09/08
19/09/08
24/09/08
Banned HFT
29/09/08
04/10/08
Unbanned HFT
09/10/08
Granger Causality Test
Determining causal relations between HFT and trading cost
(ES) in statistical lead/lag sense
a1,i
b11,i b12,i
ESi,t−1
ESi,t
=
+
HFTi,t
a2,i
b21,i b22,i
HFTi,t−1
1,i,t
+
2,i,t
b12,i 6= 0: HFT Granger causes ES; b21,i 6= 0: ES Granger
causes HFT
Making inference on causalities jointly on 120 stocks:
Estimating above VAR(1) for all 120 stocks; obtain sample
cross-sectional averages of b12,i and b21,i ; Compare the sample
averages with bootstrapped averages of b12,i and b21,i
Bootstraps are performed under the null of no causality, while
keeping the correlations between residuals across stocks
T6: Granger Causality Test: Results
Results:
Variable: b 12
Q1
Sample Coefficients
-0.215
Sample t-statistic
(-0.456)
Bootstraped p-value [0.043]
Mean
Median
Q3
0.317
(0.311)
[0.002]
0.117
(0.265)
[0.010]
0.486
(0.977)
[0.008]
Variable: b 21
Q1
Sample Coefficients
-0.002
Sample t-statistic
(-0.725)
Bootstraped p-value [0.695]
Mean
Median
Q3
0.001
(0.039)
[0.341]
0.000
(-0.031)
[0.583]
0.002
(0.793)
[0.141]
HFT Granger cause trading costs, but not vice versa
Further Analysis
How and when HFT affects institutional trading costs
Liquidity provision when institutions have large trade imbalance
Directional trading: momentum ignition or order anticipation
Less negative effects on institutions with good trading skills
HFT and Institutional Buy-Sell Imbalance
Does HFT provide liquidity when institutions have large trade
imbalance?
Sorted portfolio analysis: each day, sort all stocks by size and
institutional trade imbalance.
HFT intensity, HFT imbalance
Panel regression on trading costs in subsamples with different
levels of institutional trade imbalance
T9: HFT and Institutions’ Buy-Sell Imbalance (Sorted
Portfolio)
Panel A: Distribution of HFT and institution buy-sell imbalance
HFT Buy-Sell Imbalance
Institution Buy-Sell Imbalance
Mean
Q1
Median
Q3
0.000
0.003
-0.009
-0.022
0.000
0.001
0.009
0.024
Panel B: Institutional buy-sell imbalance
Large Stocks
Mid Stocks
Small Stocks
Institutions net selling
Institutions balanced
Institutions net buying
-0.062
-0.104
-0.116
0.000
0.002
0.002
0.060
0.106
0.138
Panel C: HFT intensity
Large Stocks
Mid Stocks
Small Stocks
Institutions net selling
Institutions balanced
Institutions net buying
0.246
0.171
0.093
0.226
0.151
0.082
0.255
0.166
0.095
Panel D: HFT buy-sell imbalance
Large Stocks
Mid Stocks
Small Stocks
Institutions net selling
Institutions balanced
Institutions net buying
0.001
0.003
0.002
0.000
0.000
-0.001
-0.001
-0.002
-0.002
T10: Impact of HFT and Institutional Buy-Sell Imbalance
Dependent Variable
Execution Shortfall
Institutions net selling Institutions balanced Institutions net buying
Intercept
HFT Intensity
Log Market Cap
Book-to-Market Ratio
Prior 1-day Return
Prior 1-month Return
Prior 12-month Return
Amihud Illiquidity Ratio
Daily Return Volatility
Daily Dollar Turnover
Average Institutional Order Size
Absolute Institutional Imbalance
Average Trades Per Order
Day-fixed Effects
Stock-fixed Effects
Two-way Clustered Std.
Coefficient
t-value
Coefficient
t-value
Coefficient
t-value
3.176
-0.178
-0.198
32.267
0.594
0.157
0.065
3.236
0.312
0.024
0.528
0.359
0.000
(2.82)
(-1.77)
(-2.79)
(2.72)
(1.50)
(1.40)
(1.76)
(1.38)
(0.89)
(2.36)
(1.04)
(3.12)
(0.12)
-1.525
0.524
0.083
1.584
-0.603
-0.041
0.019
2.657
-0.174
-0.009
0.531
7.783
-0.003
(-1.37)
(2.24)
(1.18)
(0.34)
(-0.99)
(-0.45)
(0.61)
(0.96)
(-0.75)
(-1.05)
(0.30)
(2.02)
(-1.28)
-2.473
0.612
0.177
24.048
-0.438
-0.172
-0.054
-0.054
0.043
-0.015
0.858
0.258
0.000
(-2.86)
(4.78)
(2.36)
(2.41)
(-1.01)
(-1.22)
(-1.31)
(-1.31)
(0.20)
(-2.14)
(1.11)
(2.09)
(-0.23)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Detecting Directional Trading
HFT strategies and randomness of HF trade directions (buy or
sell):
Market making: B-S-B-S-B-S-B-S-B-S
Directional trading (momentum ignition or order anticipation):
B-B-B-B-B-S-S-S-S-S
Random walk: B-S-S-B-B-B-S-B-S-S
Detecting HFT strategies:
Runs test (classic non-parametric test of random walks)
Market making: negative & significant
Directional trading: positive & significant
Random walk: not significant
T11: Directional HF Trades
Dependent Variable
Intercept
HFT Intensity
Log Market Cap
Book-to-Market Ratio
Prior 1-day Return
Prior 1-month Return
Prior 12-month Return
Amihud Illiquidity Ratio
Daily Return Volatility
Daily Dollar Turnover
Average Institutional Order Size
Absolute Institutional Imbalance
Average Trades Per Order
Day-fixed Effects
Stock-fixed Effects
Two-way Clustered Std.
Execution Shortfall
Directional
Market Making
Random Walk
Coefficient t-value
Coefficient t-value
Coefficient t-value
0.143
0.409
-0.019
10.538
0.075
-0.019
0.038
9.170
-0.213
-0.024
1.275
0.220
0.000
Yes
Yes
Yes
(0.14)
(2.60)
(-0.30)
(2.49)
(0.21)
(-0.20)
(1.38)
(4.61)
(-1.41)
(-1.70)
(0.95)
(1.18)
(-0.53)
0.217
0.291
0.054
2.742
-0.339
0.046
0.001
5.798
0.172
0.004
-0.903
0.595
0.000
Yes
Yes
Yes
(-0.18)
(1.94)
(0.69)
(0.42)
(-0.65)
(0.38)
(0.03)
(2.43)
(0.69)
(0.43)
(-1.45)
(3.83)
(-0.20)
-1.371
0.196
0.093
-2.678
-0.316
-0.130
-0.026
2.208
0.223
0.010
1.525
0.135
-0.001
Yes
Yes
Yes
(-1.44)
(1.64)
(1.63)
(-0.15)
(-0.66)
(-1.48)
(-0.85)
(1.09)
(0.62)
(0.92)
(2.95)
(0.88)
(-0.39)
T11: Heterogeneity of the impact of HFT
Institutions with better trading skills are less affected by HFT
Dependent Variable
Execution Shortfall
Coefficient t-value
Intercept
HFT Intensity
HFT Intensity × High Trading Skill Dummy
High Trading Skill Dummy
Log Market Cap
Book-to-Market Ratio
Prior 1-day Return
Prior 1-month Return
Prior 12-month Return
Amihud Illiquidity Ratio
Daily Return Volatility
Daily Dollar Turnover
Average Institutional Order Size
Absolute Institutional Imbalance
Average Trades Per Order
Day-fixed Effects
Stock-fixed Effects
Two-way Clustered Standard Deviations
-1.129
0.514
-0.308
-0.006
0.035
5.326
-0.209
-0.053
-0.005
5.509
0.089
0.002
0.505
0.286
0.000
Yes
Yes
Yes
(-1.74)
(4.01)
(-2.55)
(-0.19)
(0.88)
(0.99)
(-0.81)
(-0.98)
(-0.32)
(3.72))
(0.51)
(0.35)
(0.96)
(2.74)
(-0.85)
Conclusions
HFT significantly increases the trading costs of institutional
investors
Robust to the controls of stable stock liquidity characteristics
and events that might jointly affect HFT and trading costs
Analyses on the Short-selling ban and Granger causality tests
further rule out reverse causality/simultaneity
HFT serves as an opportunistic liquidity provision when
institutional investors have large trade imbalances (especially
for imbalance on buy side)
The impact of HFT is most pronounced when their trading is
directional (likelihood of momentum ignition or order
anticipation)
The negative effects of HFT is weaker on institution with
good trading skills.