INTERNATIONAL EVIDENCE ON ALGORITHMIC TRADING

Ekkehart Boehmer, EDHEC Business SchoolKingsley Fong, UNSWJulie Wu, University of Georgia

What is algorithmic trading?

Using computer algorithms to generate, monitor, and cancel orders automatically process prices, LOB, news predict flows, liquidity, prices

Has been around for at least 25 years (ATD) but saw enormous growth over last decade milestones: Limit order display rules (NYSE

1993, Nasdaq 1997), Reg ATS (1998), Reg NMS (2005)

U.S. Trading centers (SEC 2010)

Trading centers and estimated % of share volume in NMS stocks (September 2009)

Agency vs. proprietary algos

Agency algos – used by buy-side to minimize costs of portfolio turnover

Proprietary algos goal is to profit from trading

environment rather than investing a subset of these proprietary algo users

we consider high frequency trading (HFT) – traders with response times measured in milliseconds

Who are HFT?

Proprietary trading firms, potentially organized as B/D

Proprietary trading desk of large B/D firm

Hedge funds

Four broad categories of HFT strategies (SEC 2010)

Passive market making strategies – provide liquidity, earn the spread

Arbitrage strategies – seek short-horizon patterns

Structural strategies – exploit features of trading protocol or regulation

Directional strategies – order anticipation, momentum ignition, …

None of these strategies is new, but the technology to implement them is.

Issues

HFT is for real – between 60% and 80% of volume, depending on the market

Proportion of good vs bad strategies is unknown

What are consequences of HFT for• market quality?• welfare (of traders, society, …)?• systemic risk?

Our priors regarding HFT’s effect on market quality

Depends on strategies passive market making should improve liquidity stat arb should improve efficiency structural and directional strategies could be

wealth transfers

WE DON’T KNOW ACTUAL STRATEGIES, SO THE BEST WE CAN DO IS LOOK AT THE AGGREGATE EFFECT of AT/HFT

(Ideally, we should care about aggregate welfare: competition among HFT and with other traders may be desirable, IT arms races are probably not)

Theoretical literature

Modeling HFT

Standard approach: design a model with (typically slow) liquidity

traders and/or market makers, add HFT typically assume that HFT is informed or sees

information first not surprisingly, result is a wealth transfer from

slow to fast traders (e.g., Cartea and Penalva 2011, Jarrow and Protter 2011)

Instead, we may need models where the choice to become HFT is endogenous or liquidity traders can penalize HFT

Social welfare

Probably most important: under what conditions can HFT increase welfare Hoffman 2012 lets traders endogenously invest in

fast trading technology. Welfare gains when markets are sufficiently efficient. Otherwise IT overinvestment.

Biais, Foucault, and Moinas 2010 show that HFT can generate gains from trade and gains from adverse selection, but a social planner would only consider gains from trade, not from adverse selection. Again, HFT overinvest in technology.

Ambiguous effects in Jovanovic and Menkveld 2011

Empirical literature

Available data sources

Transactions data where we infer the presence of AT/HFT indirectly from periods of very high message traffic

Transactions data with trader category information that have a set of transactions attributed to AT/HFT

Transactions data with trader account information

Results based on inferred AT/HFT activity: Message counts Hendershott, Jones, and Menkveld

2011 use order-level message counts as a proxy for AT sample covers NYSE activity 2001-2005 algo trading is positively related to

market quality (improves spreads and price discovery, and reduces information asymmetry)

AT improve price discovery (Hendershott and Riordan 2009)

Results based on inferred AT/HFT activity: Millisecond episodes Hasbrouck and Saar 2011 use

2007/2008 INET Itch data infer HFT from millisecond responses to

market events such as quote updates identify fast trades that are linked in

time to identify episodes of HFT find that HFT improves short-term

volatility, spreads, and depth

Results based on inferred AT/HFT activity: Spikes in quoting activity Egginton et al. 2011 use 2010 TAQ data

select one-minute intervals where quotes-per-minute exceed 20 historical 20-day s.d. (on days where daily quoting is less than two s.d. away from the mean)

discard days with information events

Episodic spikes of (TAQ) quoting activity are quite frequent in many stocks are associated with degraded liquidity

and elevated short-term volatility

Results based on the Nasdaq sample with trader categories Brogaard (2010) uses a 2008-2009 Nasdaq

sample covers 120 Nasdaq stocks (selected by

academics) 26 HFT firms/traders that account for 77% of

trades/74% of dollar volume in this sample (selected by Nasdaq, excludes large prop desks)

HFT activity is associated with better liquidity, mixed effect on volatility, better price discovery (Brogaard 2010, Hendershott and Riordan 2011)

Problems with the Nasdaq sample with trader categories

Problem with the Nasdaq sample: potential selection bias less than sparkling clean traders would rationally veto

reporting included transactions do not include same-trader

activity on other markets and may not include all their activity on Nasdaq (?)

exchange may purposely select liquidity suppliers

This sample probably contains HFT activity that is more benign than that in a random sample of HFT

Results based on data with individual trader IDs

Kirilenko, Kyle, Samadi, and Tuzun 2011 see individual strategies in S&P500 e-minis find that HFT worsened (but did not cause)

the Flash Crash of 2010.

Only with trader IDs can we track each trader’s orders across

stocks and markets and infer actual strategies overcome endogeneity problem accurately determine triggers and

consequences of HFT activity

To sum up: we have somewhat mixed results and suboptimal data Broad samples (with inferred AT/HFT

activity) imply mostly positive effects on average (MQ) mixed results on volatility negative effects during periods of extremely

high message traffic and around quote updates

Trader type samples positive effect on MQ and efficiency mixed results on volatility selection concerns

Our motivation

What are the consequences of AT/HFT for markets, traders, firms, countries?

To address gaps in the literature, to answer market structure questions, to evaluate whether LTI or AT/HFT are more important: we need more and broader evidence. from new samples. this paper is a first step in that direction.

Our objectives

Use an international sample to measure the impact of AT/HFT on execution cost for different types of

traders price discovery volatility

Document differences across firms, trading protocols, and countries in these measures (incomplete)

Data

• Intraday quote and trade data from Thomson-Reuters Tick History (TRTH) 39 exchanges, 36 countries, 2001-2009 on average about 13,000 stocks will add NYSE and Nasdaq data from TAQ

Daily data on returns, volume, high-low prices from Datastream

Information about trading protocols from Speedguide, Exchange Handbook, WFE

Accounting data from Datastream and WorldScope

Our variables

Proxy for algorithmic trading

AT = -trading volume / # messages measures negative of trading

volume in USD 100 per message follows Hendershott, Jones, and

Menkveld 2011 messages include trades and quote

updates

Liquidity measures

Time weighted quoted spreadsRQS=(ask-bid)/MQ

Trade weighted effective spreadsRES=2*|P-MQ|/MQ

Amihud = |Return|/volumeAncerno (formerly Abel-Noser) buy-

side trading costs

Information vs. transient price impact

RES captures total price impact of a trade.

Decompose RES into RPI, the permanent component, the

change in MQ from trade to 5 minutes later, measures information content (“toxicity”)

RRS, the transient component, measures reward to liquidity providers, RES = RRS + 2* RPI

Informational efficiency

|autocorrelation| for 10-60 minutes intervals for each stock, compute midquote returns for 5,

10, 20, 30 , 60 minute intervals then compute autocorrelation of those returns ignore overnight and zero returns note that there is no bid-ask bounce in this

measure

The more efficient the stock price (the closer it is to a random walk), the smaller is |ARnn|

Volatility

Use several standard volatility measures |R|, for raw and market-adjusted return R^2, for raw and market-adjusted return Log (Ret10_Var), Log (Ret30_Var) daily relative price range =

(High-Low)/Close

Econometric methodology

Methodology

Have three-dimensional unbalanced panel 39 markets, about 2250 days, about 330 stocks

per market standard unobservable firm-level and time effects unobservable market or country effects

Use daily standardization of all variables so coefficients are comparable across markets

Regress variable of interest on AT and controls

Methodology

All regressions control for Volume (share turnover) Volatility (relative price range, excluded from

volatility regressions) price level (1/price) firm size (ln market cap)

Volatility regressions additionally control for RES and |AR30|

Estimation strategy estimate firm/day panel regression for each market then aggregate across markets

Methodology

Use two different approaches within markets Two-way dynamic panels within each market ▪ use firm and day fixed effects▪ use Arellano and Bond (1991) standard errors for

market-level inference Fama-MacBeth within each market (same

results, not reported)

▪ For global inference, compute means across markets, use cross-sectional t-test for inference

Regression results

The relationship between algorithmic trading and liquidity

More AT activity is associated with lower spreads and smaller temporary and permanent price impacts.

RQS RES RPI RRS AmihudMean coefficient on AT -0.013 -0.013 -0.002 -0.018 -0.009t-stat -9.1 -7.4 -0.7 -9.2 -6.5%positive 5% 8% 54% 5% 15%

Cross-sectional tests

How does the effect of AT differ for stocks with different characteristics?

Sort within each market according to each characteristic

Create dummies LOW and HIGH for low and high tercile

Include interactions with AT in regression models

The effect of AT on liquidity for different firm sizes

Solid colors indicate significance at the 5% level

More AT reduces liquidity in small stocks

-0.020

-0.010

0.000

0.010

0.020

0.030

0.040

0.050

0.060

0.070

RQS RES Amihud

Small cap

Mid cap

Large cap

The effect of AT on liquidity for different share prices

Pale colors indicate no significance at the 10% level

AT does not benefit low-priced stocks

According to Amihud, liquidity even declines significantly for low priced stocks

-0.015

-0.010

-0.005

0.000

0.005

0.010

0.015

0.020

RQS RES Amihud

Low price

Mid price

High price

The effect of AT on liquidity for different volatility levels

Volatility is SD of the 20 most rcent daily returns

AT only benefits liquidity of low and mid volatility firms

There is no AT effect on high volatility firms

-0.020

-0.015

-0.010

-0.005

0.000

0.005

Low volatility

Mid volatility

High volatility

The relationship between algorithmic trading and informational efficiency

More AT activity is associated with better informational efficiency.

|AR10| |AR30|Mean coefficient on AT -0.017 -0.006 t-stat -6.1 -4.5Percent positive 5% 23%

The effect of AT on efficiency in the cross-section

Pale colors indicate no significance at 10%

AT does not improve efficiency for small, low priced firms

-0.010

-0.008

-0.006

-0.004

-0.002

0.000

0.002

0.004

0.006

0.008

0.010

Market cap

Price Volatility

Small

Mid

High

The relationship between algorithmic trading and volatility

• More AT activity is associated with higher volatility.• We control for efficiency and liquidity of each stock.• Thus, the volatility increase does not represent “good” volatility that may arise with very efficient markets.

|ret| Ret^2PriceRang

eLn(Ret10_Var)

Ln(Ret30_Var)

Mean effect of AT

0.033 0.025 0.045 0.017 0.029

t-stat 7.4 6.6 7.9 3.4 4.9%positive 87% 85% 87% 67% 79%

The effect of AT on volatility in the cross-section

Effect on relative intraday price range (Results are robust for several other volatility measures)

0.0000.0100.0200.0300.0400.0500.0600.0700.0800.090

Market cap Price Volatility

Small

Mid

High

Volatility increases most for stocks that are small, low priced, or have volatile returns to begin with

AT = market making?

We do not observe what strategies algo traders use

Liquidity provision for mid and large cap stocks implies that at some AT supply liquidity

How resilient is this liquidity supply over time?

When is market making difficult?

MMs dislike one-sided order flow that moves price. E.g., consider sell imbalance: price moves down, MM is long, faces inventory losses

Will cut back on liquidity provision on such one-way days

Will cut back more when imbalance continues through the next day

Create a proxy for difficult MM days

Select all days where daily return has the same sign as on the previous day

Set HARD dummy to one on these days if the cumulative return exceeds one 20-day standard deviation

Interact with AT as before

How does the effect of AT change on difficult market-making days?

AT increases volatility and efficiency more

AT still increases liquidity, but significantly less than on regular days

AT significantly increases the information content of trades

If AT use MM strategies on average, they tend to resort to other strategies when market making is unusually difficult

Contrasts to traditional MM with affirmative obligations

-0.020 0.000 0.020 0.040

RES

Amihud

RPI

RRS

|AR30|

Price Range

Change on HARD days

Mean coefficient on AT

Which way does causality go?

Use co-location within each market as an instrument for AT

Estimate IV regression at the market level1. compute value-weighted daily averages for

each market2. estimate first stage regressions of AT on CL

dummy and market-day fixed effects3. estimate second-stage IV model with market

and day fixed effects using predicted values from (2)

IV estimation using co-location as an exogenous shock to ATLiquidityDependent

Estimate t

RQS -1.0087 -6.6RES -1.1546 -6.2RPI -0.9285 -7.4RRS -0.2718 -1.8Amihud -0.0001 -6.0

Efficiency

DependentEstimat

e t|AR10| -0.0006 -4.5|AR30| -0.0001 -0.5

Volatility    PriceRange 0.0002 10.0|Ret| 0.0001 3.2Ret^2 0.0000 0.1

Results are largely unchanged with IV – evidence that causality originates with AT.

Summary

We contribute evidence from 39 countries to shed some light on how algo trading affects market quality

Algo trading improves liquidity and informational

efficiency worsens volatility, even when controlling

for efficiency and liquidity

Summary

But AT worsens liquidity of the smallest third

of firms in each market AT increases volatility the most for firms

that are small, low priced, and more volatile

on days when market making is more difficult, AT provides less liquidity, increases information content of trades, and increases volatility more.

Conclusions

Results are amazingly consistent across markets

Volatility increases with more algo trading – what exactly are the implications?

In assessing the current market structure, market observers should take into account that the effects of AT are not uniform.

Next steps

Will higher volatility discourage investors in the long run and thus increase firm’s cost of capital? Or affect its ability to raise new capital?

We will use our 11-year panel to assess longer-term effects of AT on

firm/country characteristics distribution of benefits and costs over

good/bad times and across stocks

Thank you for your attention.