Developments in Equity Trading over the last Decade

• computerized (equity) trading
  • no more phones and human specialists
  • electronic access through brokers
  • electronic traders: autonomous and fast (high-frequency traders)
• multiple marketplaces
• regulation that mandates electronic linkage of multiple markets
  • U.S. and Canada: protected quotes  (or no-trade through rule)
    • = market-wide price priority

Popular Line of Reasoning

• traders report that after they submit orders, all hell breaks loose:

  • quotes "fade"/"slide" on other venues

  • "others" get to trade on other venues before them

• => HFTs and fragmented markets are at fault

What do HFTs do after trades?

• What explains HFTs' reactions (if present)?
• Is there an impact?  
• What is the role of fragmentation?

Trading with multiple markets

1,000

300

400

Regular Joe sends buy order to broker

buy 1,500 shares

no trade through => broker must split among three venues

400

100

2,000

Through the microscope

1,000:

Shares at Canadian Offer

300:

400:

100

100

100

100

100

100

100

• They may not want to trade all that they post.
  • will try to cancel quickly
    • once their order gets hit or
    • once they see trades
  • => flurry of cancellations

Flurry of HFT Activities after Trades

50% of trades -- by a non-HFT -- are quickly followed by a cancellation -- by an HFT -- on a different venue within 5ms of the trade

Within 50ms: about 70% of trades followed by cancels

And: the reaction is more extreme

after multi-market trades!

Flurry of HFT Activities after Trades

13% of trades -- by a non-HFT -- are followed by an aggressive trade -- by an HFT -- on another venue in the direction of the original trade within 5ms

Within 50ms, 29% of trades are followed

Again: the reaction is more extreme for multi-market trades!

 This project's goal:

Characterize this behavior and its impact in fragmented markets

• Step 1: Characterize/describe fast (HFT) traders’ reaction to trades:

  • Do they cancel their orders? (yes)
  • Do they submit own aggressive orders? (yes)
  • Different reaction to single vs multi-market orders? (yes)
• Step 2: What explains the different reaction?

  • size?

  • type of trader?

  • information?

• Step 3: Does the HFT behavior have an impact on the market?

Not the first to look at fragmented markets

• Long literature, including
  • Joel Hasbrouck (e.g., "One Security, Many Markets: Determining the Contributions to Price Discovery", JF 1995)
  • O'Hara & Ye (JFE 2011): good for mkt quality
  • Degryse, de Jong, van Kervel (2014): visible vs. dark fragmentation
  • Bernales, Riarte, Sagade, Valenzuela, Westhei (2017): fragmentation without competition
• ​most closely related:
  • van Kervel (RFS 2015): over-posting exists
  • Baldauf & Mollner (WP 2015) (theory): splitting of liquidity across markets
  • Brogaard, Riordan, Hendershott (JF 2018): HFT generate price discovery with limit orders, without trading.

Critical Ingredients

• Our focus:

  • HFTs in regulation-mandated integrated mkts

• What do we need?
  • examine HFT? => need trader level info
  • impact of mandated multi-mkt? => need trader level data to identify multi-mkt vs single mkt
  • examine the impact => need identification

• proprietary masked trader-level data for all Canadian equity markets (provided by IIROC, the Canadian regulator) 

  • use 30 most frequently traded non-crosslisted stocks, March - May, 2013.

• a critical market-organization change that eliminated latency between two of the three main markets (markets A and B) on April 29, 2013 => identification

Data on Investors vs Traders

Identifying Traders

• Similar to Comerton-Forde, Malinova, Park (2018)

• Fast traders: Use three criteria (across many securities on many days: 307 securities, Jan& Feb, 2013)

  • regularly submit and cancel orders very quickly

    • median submit-to-cancel times < 250ms.

  • submit/cancel most orders very quickly subsequent to someone else’s activity

    • 85% of activity within 1ms of someone else.

  • react quickly (500ms) to a particular, regular, market-wide news announcement (the market-on-close imbalance).

  • classified: ~82 (out of ~4,900)

• Retail: special order type that can only be used by retail
• Institutions: trade-strings:
  • at least 10 distinct orders
  • single direction on a day

What is a multi-market trade?

• same trader ID
  • caveat: "trader ID" is not the same as "trader"
• submits marketable order on separate markets within 5 milliseconds

Post-trade Cancellation

• cancellation by fast trader
• within 1,2,...,5 milliseconds

Post-trade Aggressive Order

What should we expect?

• Trades = information.

  • Baldauf and Mollner (2015): only smart trade everywhere
  • van Kervel (2015): only sophisticated have access to smart order routers/multiple venues

• Market makers post everywhere but only want to trade once.
  • Cancel existing orders (van Kervel (RFS 2015))

Are multi-market trades different?

• Market making should lead to flurry of activities
  • cancellations are to be expected
  • aggressive orders can be part of market-making
• Are the HFT actions merely seamlessly integrating markets?
  • First steps: are there any "in plain sight" differences between single- and multi-market orders?
    • ​Are they submitted by more informed traders?
    • Are they larger?

Measure of Interest: Price Impact

• signed difference of the prevailing mid-point of the NBBO bid-ask spread \(\tau\) time units in the future and the mid-point that prevailed at the time of the trade

• \(q_{it}\) = 1 for buyer-initiated and -1 for sells
• \(m_{it}\) = midpoint at time t

 Are multi-market trades more informed?

Simple summary stat: price impact

Naive conclusion: multi-market trades have higher price impact, therefore they are more informed

Consistent with Baldauf & Mollner and van Kervel:
multi-market = smarter

HFTs must react stronger!

End of story?

Who trades in multiple markets?

All trades

Multi-market trades

Multi-market trades:

\(\approx\) 32% of total $-value

Price impact for retail trades

Conclusion: If we believe that retail orders are not informed, then the price impacts for single vs. multi-market orders shouldn't look this different.

Multi-Market

Single Market

vs.

Is it Size?

$ value of trades per bucket

$ value of all trades

$ value of multi-mkt trades per bucket

$ value of all multi-mkt trades

Size distribution

Conclusion: multi-mkt orders are larger

Single Market

Multi-Market

Plotting: price impact multi-mkt minus single-mkt

Conclusion: even for similar size, price impact of multi-market orders is larger.

Could price impact be larger because of the HFT reaction?

Plotting: price impact with HFT reaction minus price impact without HFT reaction

Reaction= trades

Observation: HFT makes your trade look fat

Multi-Market

Single Market

Bottom Line

• multi-mkt trades are larger but need not be smarter
  • using multi-mkt is/can be regulation requirement
  • possibly exchange fee considerations
  • retail (brokers) use them regularly
• multi-mkt have larger price impact
  • even for retail
  • for same size
• multi-mkt with HFT cancellations/aggressive submissions
  • have larger price impacts.
  • look "bigger"

"Trade clusters"

• "Merge" all transactions that occur within a short (10ms) time interval into a "trade cluster", instead of restricting these to be by a single trader
  • Average cluster lasts for 3.4ms 
• Compare trade clusters :
  • with single vs. multiple traders (i.e., where the original trade was followed by others)
  • that occur on a single vs. multiple markets

Price Impact of Trade clusters

• Assign all trade clusters into one of the four categories:

• Estimate a regression over all trades to assess whether following by other other traders & multi-market trading matters for the price impact, controlling for the size

Price Impact in Trade Clusters

• Effects on the price impact 5ms after the trade (in basis points), controlling for the size of the first trade in a cluster or for the aggregate volume of the trade cluster (2 separate regressions):

Who follows the trades and why?

• Over 60% of follow-up trades stem from 14 fast traders
• As a group, these:
  • Earn $1.25M on in-cluster trades
  • Lose $0.28M on out-of-cluster (active) trades
• Compute return per trade, relative to the trade's VWAP and assuming that positions are closed at end-day prices:

• Estimate:

Who follows the trades and why?

Bottom Line

• Price Impacts are higher for
  • Trades that execute on multiple markets -- even after controlling for trade size
    • Possible explanations: liquidity effects: the choice of whether to go to multiple markets is endogenous
  • Trades that are followed by other traders -- again, after controlling for trade size
    • This latter effect is even more pronounced for trades that are followed by one of the 14 fast traders
• Remains unresolved whether followers:
  • increase the price impact by making trades look "bigger"
  • or follow the trades that are informed ("back running")

The Big Question

• Is the reaction:
  • reacting to information/price discovery
    • HFT push prices to the "right" level
  • or noise
    • HFT reaction obfuscates learning 

How to identify?

• Idea: if latency between venues disappears:
  • non-HFT order flow should remain similar
    • if price discovery => HFT can create same level of it.
  • "Following" is harder to implement:
    • if noise => lower price impacts

How do you make physical latency disappear?

Market A and B move to the same data centre: April 29, 2013

What would we expect?

• if you post on both A and B, you cannot avoid being "hit" on both, i.e. no more outrunning
  • depth should decline
  • spreads may increase
  • fewer post-trade cancellations
  • impact on post-trade aggressive trading is unclear 
• Notable differences between A and B:
  • Market A: lowest take fee/make rebate
  • Market B: "big", highest take fee/make rebate

Empirical Strategy

Examine changes in market quality and trader behavior

• before/after (March 1-May 31) and also by market;

• DV dependent variable of interest
• Dummies for the event and the market
• Controls include VIX for volatility & an ETF for the S&P500 GSCI commodity index for the price level
• Security and mkt fixed effects 
• Std errors double-clustered by date and security

Changes in Liquidity and fast trading activities

Changes in Responses to Trades

(no)

Untabulated: no significant changes in size or usage of multi-mkt trades

Changes in Price Impacts

Bottom line: price impacts of multi-market orders decline

Difference in differences of multi- vs. single-mkt orders before vs after