High-Frequency Trading and Price Discovery Terrence Hendershott ( Jonathan Brogaard Ryan Riordan)

High-Frequency Trading andPrice Discovery

Terrence Hendershott (Jonathan Brogaard

Ryan Riordan)

Technology has been good for markets Is every use of technology good? How do we think about (evaluate) HFT? What are costs/benefits of those closest to the market?

Analysis of Nasdaq HFT data in terms of price discovery Statistical model of HFT and “information” and “noise” in prices Challenges in interpreting these results Cross-correlations between HFT and price changes Explore what types of information HFT has (not in paper yet):

Macro news announcements Market-level (as opposed to individual stocks) results Limit order book imbalances

Overview

High-Frequency Trading and Price Discovery 2

The old trading floor, NYSE 1873

4

More recent trading floor

High-Frequency Trading and Price Discovery

Securities Market

Algorithmic & High-Frequency Traders/Systems

Algorithmic and High-Frequency Trading

Algo Trade Definition: “The use of computer algorithms to manage the trading process.‘‘(Hendershott et al. 2011); HFT similar, but for short holding periods

5

Current trading floor


Is the evolution good?Some News Articles

- “...the stock market is more prone than ever to large intraday moves with little or no fundamental catalyst.”

- “locusts … feeding off the equity market.”

- “High-frequency traders generated about $21 billion in profits last year.”

- “...use rapid-fire computers to essentially force slower investors to give up profits, then disappear before anyone knows what happened. ”

- “...rule changes are need to control risks...”- “…development have had negative

effects…”- “HFT firms are accused of flooding markets

with orders that are cancelled…, leading to volatility”

Ultra fast trading needs curbs – global regulatorsJuly 7, 2011 - London

6

It's hard to imagine a better illustration (of social uselessness) than high-frequency trading. The stock market is supposed to allocate capital to its most productive uses, for example by helping companies with good ideas raise money. But it's hard to see how traders who place their orders one-thirtieth of a second faster than anyone else do anything to improve that social function.Paul Krugman August 2, 2009 - New York Times

Longer perspective on technological changes affecting liquidity

Figure 1. Bid-ask spreads on Dow Jones stocks(all DJ stocks 1900-1928, DJIA stocks 1929-present)

0.00%

0.20%

0.40%

0.60%

0.80%

1.00%

1.20%

1.40%

1.60%

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Prop

ortio

nal s

prea

d

Technological advances have made the markets better: Evidence from trends and specific events/introduction


Liquidity (Trading Costs/Spreads) on NYSE

8

Decline is from lower averse selection (less information in trading) -- Hendershott et al. 2011How well can we measure liquidity as trade size -> 0? Transitory impact of large orders?


Algorithmic Trading on NYSE

9

Hendershott et al. 2011


10High-Frequency Trading and Price Discovery

Correlation is not causality: we need a “natural experiment”: Autoquote

Algo Trading is a superset of HFTWhat is HFT?

HFT is always AT – but AT is not always HFTTypical properties of HFT:

–High-speed trading

–Sophisticated computer programs

–Use of co-location services and data feeds

–Short time frames for establishing and liquidating positions, high trading volume and intensity(SEC, 34-61358, Concept Release on Equity Market Structure)

• HFT is a mixture of the use of technology and trading strategies (do they differ?)• What is new is direct access, increased electronic information sources, and more computing


• Anything new here? –Short-lived strategies with tight risk management

– Based on public information, technology, and sophisticated tools

Is the focus the technology, strategies, or interaction?

HFT Strategies (SEC)

Passive Market Making Arbitrage

Structural Directional

Strategies

• Make money at the bid-ask spread and liquidity rebates

• Cross asset and cross market arbitrage

• Statistical arbitrage

• Latency Arbitrage• Flash orders

• News Trading• Liquidity Detection (order anticipation)

• Momentum trading and ignition


HFT and Price Discovery (beyond liquidity)

13

Sept. 19th, 2002 Sept. 6th, 2008 10:58 Sept. 8th, 2008

United Airlines files for Ch. 11 to cut costs - Bloomberg

Sept. 8th, 2008

United Airlines Share Price


Price Discovery: Efficient Price & Noise Explicitly model observed prices each minute as efficient

price (martingale) and noise (pricing error)

Observed price is martingale (efficient price) plus noise:

Trading makes cov(s,w) <> 0 Standard market microstructure approach: Innovations in

martingale are related to innovations in trading:

Transitory component also relates to trading:

Identification from Cov(m,u)=0 (Hasbrouck (1993) and others)High-Frequency Trading and Price Discovery 14

pi,t=mi,t+ si,t mi,t= mi,t-1+ wi,t.

wi,t= 𝜅𝑖𝐴𝑙𝑙𝐻𝐹𝑇෫ 𝑖,𝑡𝐴𝑙𝑙 + 𝜇𝑖,𝑡

si,t= 𝜙𝑠𝑖,𝑡−1 + 𝜓𝑖𝐴𝑙𝑙𝐻𝐹𝑇𝑖,𝑡𝐴𝑙𝑙 + 𝜐𝑖,𝑡

Price Discovery: Interpretation

k trading’s (order flow) relation to efficient price + informed about change in efficient price - adversely selected on efficient price

y trading’s (order flow) relation to noise + direction of trading correlates with more noise

Transitory price impact, risk mgt., manipulation, order anticipation - trading against noise, making prices more efficient

Statistical arbitrage via identifying transitory effects

Decompose overall HFT order flow into Liquidity Demand and SupplyExamine highest volatility days (stability?)


wi,t= 𝜅𝑖𝐴𝑙𝑙𝐻𝐹𝑇෫ 𝑖,𝑡𝐴𝑙𝑙 + 𝜇𝑖,𝑡

si,t= 𝜙𝑠𝑖,𝑡−1 + 𝜓𝑖𝐴𝑙𝑙𝐻𝐹𝑇𝑖,𝑡𝐴𝑙𝑙 + 𝜐𝑖,𝑡

120 stocks: small, medium, large cap Identifies 26 independent HFT firms

Via unique “port” which firms use Does not identify large integrated firms, e.g., Goldman All data is aggregate across all HFT firms

Is this a representative sample? If not, why not?

HFT firms worried about regulatory response Data is available via NDA with Nasdaq Nasdaq had 20-40% market share HFT is 42% of volume in large stock, 12% in small stocks Market/limit order volume similar in large, less limit in small

Nasdaq HFT Data


Overall HFT trade to make prices more efficient Results remain (and are stronger) on high-volatility days

Market order trading is responsible for this + correlation with efficient price, - with pricing error/noise Consistent with forecasting both parts of returns

Limit order trading coefficients have opposite signs - correlation with efficient price, + with pricing error/noise Efficient price: standard adverse selection of liquidity providers Noise: risk mgt., adverse selection, manipulation, order anticipation?

Do these passive trades make money? Yes, earn the spread and liquidity rebates Overall HFT profitability per $ is low: ~$0.03/$10,000 traded

Nasdaq HFT (SSM) Price Discovery Results


Is HFT being informed good or bad? HFT liquidity demand imposes adverse selection But, also trade against noise in prices It is possible to do one without the other? How important is this information? How long-lived? Would it get into price anyway?

HFT liquidity supply looks a lot like market making (good) How could we measure excess intermediation (bad)?

Next, cross-correlations for individual stocks and market-wide and macro news announcements

Interpretation of Statistical Model


HFT Demand positively predict stock returns for a few seconds HFT Supply negatively predict stock returns for a few seconds Demand effect dominates overall

Individual Stocks:HFT and Future Stock Returns (1sec periods)


t t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8 t+9 t+10

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

HFT Demand/Supply/Allt - Rtnt, t+10

HFT DemandHFT SupplyHFT All

Time (in seconds)

Corr

elati

on C

oeffi

cient

s

Aligning return and HFT times is challenge (Nasdaq BBO for subset) HFT Supply is adversely selected; HFT Demand is transmitting info Overall Supply dominates; how to think about this HFT Demand? Regressions show HFT Demand is not associated with noise

Event Study: Macro Announcements


-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10-8,000,000.00

-6,000,000.00

-4,000,000.00

-2,000,000.00

0.00

2,000,000.00

4,000,000.00

6,000,000.00

8,000,000.00

-0.20%

-0.15%

-0.10%

-0.05%

0.00%

0.05%

0.10%

0.15%

0.20%

Negative Macro News

HFT Demand HFT Supply HFT All VW Return

Seconds Around Macro News Announcements

HFT

Ord

er F

low

( $1

0,00

0)

Retu

rns

Similar to Negative announcements Predominantly HFT is providing liquidity at most stressful time 209 Announcements in: Construction Spending, Consumer Confidence, Existing Home Sales,

Factory Orders, ISM Manufacturing, ISM Services, Leading Indicators, Wholesale Inventories

Event Study: Macro Announcements


-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10-8,000,000.00

-6,000,000.00

-4,000,000.00

-2,000,000.00

0.00

2,000,000.00

4,000,000.00

6,000,000.00

8,000,000.00

-0.20%

-0.15%

-0.10%

-0.05%

0.00%

0.05%

0.10%

0.15%

0.20%

Positive Macro News

HFT Demand HFT Supply HFT All VW Return

Seconds Around Macro News Announcements

HFT

Ord

er F

low

($10

,000

)

Retu

rns

HFT Demand positively predict market-wide returns for a few seconds HFT Supply negatively predict market-wide returns for a few seconds Supply effect dominates overall (also true in a market-level SSM) Past market returns predict HFT similarly (E-mini predicts HFT Demand) HFT has a longer lasting role in market-wide price discovery

Market-wide:HFT and Future Stock Returns (1sec periods)


t t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8 t+9 t+10

-0.50

-0.30

-0.10

0.10

0.30

0.50

HFTtD, S, and All - VW Rett, t+10

HFT DemandHFT SupplyHFT All

Corr

elati

on C

oeffi

cient

s

Limit order book imbalance LOBI (Size at offer minus Size at bid) LOBI predicts future returns (-), HFT Demand (-), HFT Supply (+) Not much in the small stocks

Another source of HFT Information


Panel A: 𝑹𝒆𝒕𝒕+𝟏

Large Medium Small All 𝑳𝑶𝑩𝑰𝒕 -0.01 -0.01 0 -0.01

(t-stat) (-16.36) (-4.35) (0.01) (-1.19)

Panel B: 𝐇𝐅𝐓𝐭+𝟏𝑫 𝑳𝑶𝑩𝑰𝒕 -0.19 -0.21 -0.13 -0.18

(t-stat) (-15.17) (-7.55) (-2.05) (-8.49)

Panel C: 𝐇𝐅𝐓𝐭+𝟏𝑺 𝑳𝑶𝑩𝑰𝒕 0.07 0.05 0.06 0.06

(t-stat) (5.90) (1.86) (1.18) (3.33)

Panel D: 𝐇𝐅𝐓𝐭+𝟏𝑨𝒍𝒍 𝑳𝑶𝑩𝑰𝒕 -0.06 -0.11 -0.03 -0.07

(t-stat) (-12.36) (-11.81) (-0.63) (-4.20)

Other sources of “Public” information in past prices, orders? In other assets (market-wide, announcement results) Large stock leading small stocks

HFT Demand predicts returns How to think about short-lived nature of (public) information?

Foucault, Hombert, and Rosu (2012) Reducing the transitory part is good Trading on (soon to be) public information is not How do we know what information will get into price without HFT?

Sources of HFT Information


Technology has improved markets Algorithms: prices more efficient and markets more liquid

HFT is technology applied to certain strategies Make prices more efficient; true on high-volatility days also

What are the benefits of getting info into prices in seconds? Does this improve financing and investment decisions?

Should traders closest to the market mechanism be informed? Focus attention on HFT liquidity demanding strategies? Are long-term investors losing out (zero sum trading)? If so, will HFT become competitive and offer technology services to

long-term investors? Will market structures evolve to support LFT (without HFT)?

What is the optimal configuration of intermediation sector? Free entry or regulated monopoly/oligopoly? Specific regulations on liquidity supply and demand?

Conclusions/Thoughts


Documents

High-Frequency Trading and Price Discovery Terrence Hendershott ( Jonathan Brogaard Ryan Riordan)