Potential Pilot Problems

Charles M. JonesColumbia Business SchoolDecember 2014

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The popular view about equity markets

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Trading certainly looks different today…

20th century 21st century

Automation has driven out costs.Is it increasing liquidity and helping firms raise capital? 3

Two liquidity measures defined Effective bid-ask spreads

ESit = | Pit – Mit | Distance from prevailing midpoint Mit to trade price Pit

Actually a half-spread or one-way cost Defined for a single (child) transaction

Implementation shortfall More relevant for a parent order (e.g., buy 1mm shares

of IBM) For buys, ISit = – Mit

Distance (usually in bps) from decision-time price Mit to average trade price

Captures effect of driving prices up with sequences of buy orders

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Source: spliced ITG research reports5

2005 2006 2007 2008 2009 2010 2011 2012 201320

40

60

80

0

10

20

30

40

50

60

US large-cap trading costs have trended down

IS Costs Commissions Average VIX

Co

sts

in b

ps

VIX

Source: Jun 2014 ITG Global Cost Re-view

What caused the improvements? There is a straightforward Econ 101 story

More competition within and across exchanges Scalable technology drives down costs

But we can’t be sure: correlation is not causality!

Many other things have changed over the past 20 years Various regulatory changes Perhaps less private information now

Can use market structure changes as instruments: Example: Hendershott, Jones and Menkveld (2010 JF)

But the gold standard for determining causal effects is randomized controlled trials

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An example: 2007 repeal of short sale uptick rule Before 2005, NYSE short sales could only

happen: On an uptick (at a price higher than the last sale price) Or on a zero-plus tick (at the same price as the previous

transaction if the most recent price change was positive)

Regulation SHO: Adopted by the SEC in 2005. Initiated a pilot program suspending the NYSE’s uptick

rule and the Nasdaq’s analogous bid test.

All Russell 3000 stocks ranked by market value; every third stock assigned to the pilot.

Pilot continued into 2007.

SEC decided to repeal all price tests Announced June 13, 2007 Effective July 6, 2007

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Empirical design for studying the 2007 repeal Takes advantage of virtually random assignment

Econometric approach: look before and after final repeal

Initial approach: treatment vs. control Treatment group (non-pilot stocks) experiences the

repeal Control group (pilot stocks) free of the uptick rule

throughout

Implemented via a differences-in-differences regression:Yit = β0 + β1Ti + β2At + β3TiAt + εit

whereYit is the outcome variable for stock i at time t,Ti = 1 if stock i is in the treatment group, Ti = 0

otherwiseAt = 1 if date t is after treatment (after repeal), else

At = 0

The interaction term β3 measures the average treatment effect.

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More shorting since tick test repealed

Jan Feb Mar Apr May Jun Jul Aug0%

10%

20%

30%

40%

50%

60%

Shorting prevalence during 2007 in NYSE stocks

non-pilot (treatment) pilot (control)

Sh

ort

ing

as

a f

racti

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of

trad

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Tick test repealed

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Short-sale orders become more aggressiveShort order characteristics in NYSE stocks during 2007

0%

10%

20%

30%

40%

50%

Jan Feb Mar Apr May Jun Jul Aug

Frac

tion

of sh

ort s

ales

non-pilot marketable non-pilot passive pilot marketable pilot passive

Tick test repealed

Passive short-sale orders are those placed at or above the prevailing ask price. 10

The problem with this empirical design Doesn’t work if there are treatment spillover effects.

Spillovers mean control stocks are affected by the treatment too.

Controls aren’t actually controls.

Not clear what the difference-in-difference approach measures.

Seminal paper in econ: “Worms” (Miguel and Kremer, 2004) Study randomized deworming treatments on Kenyan village

children But children in the control group also benefit via less

transmission So can’t do simple treatment vs. control

These spillovers are called interference in the statistics literature.

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What’s the problem with uptick repeal? Many short sale strategies are portfolio strategies

Example: index arbitrage. If the index is cheap: Buy futures or an index ETF Simultaneously short all of the underlying stocks

During the Reg SHO pilot, this strategy was hard to execute: Only about 1/3 of S&P500 stocks exempt from the uptick rule For all the rest, can’t short without complying with the uptick

rule Introduced substantial risk into this strategy.

After repeal, could short all stocks without this constraint Would expect more shorting of lists of stocks More shorting of pilot (control) stocks Voila! Treatment spillover.

Same is true for any list-based strategy (e.g., factors)12

Revisiting the evidence

Jan Feb Mar Apr May Jun Jul Aug0%

10%

20%

30%

40%

50%

60%

Shorting prevalence during 2007 in NYSE stocks

non-pilot (treatment) pilot (control)

Sh

ort

ing

as

a f

racti

on

of

trad

ing

vo

lum

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Tick test repealed

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More shorting of both treatment AND control stocks

Spillover effect could be quite large for effective bid-ask spreads

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0.06%

0.08%

0.10%

0.12%

0.14%

0.16%

0.18%

non pilot mean res pilot mean res

Tick test repealed

Pilot (control) stocks see big changes: treatment spillover or unrelated?

This is not always a problem: no evidence of spillovers during 2008 shorting ban

Cross-sectional mean of short sales as a percentage of trading volume (RELSS) for stocks on the original Sep 2008 SEC ban list with matched non-banned stocks.

0%

5%

10%

15%

20%

25%

30%

Quartile 1 (Small-cap)

Banned stocks Non-banned match

Ban period

0%

5%

10%

15%

20%

25%

30%

35%

Quartile 2

Banned stocks Non-banned match

Ban period

0%

10%

20%

30%

40%

50%

60%

Quartile 3

Banned stocks Non-banned match

Ban period

0%5%

10%15%20%25%30%35%40%45%

Quartile 4 (Large-cap)

Banned stocks Non-banned match

Ban period

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Tackling spillovers methodologically

Using notation from causal effects literature, Yi(zi,ψ) is the potential outcome for firm i given: its own treatment zi = {0, 1} ψ is the fraction of firms treated at random We only observe one of these outcomes; the other is the

unobserved counterfactual

Overall treatment effect (TE) moving from treatment strategy ψ to strategy ϕ:

TE(ψ, ϕ) = Σ E[Yi(1, ψ) – Yi(0, ϕ)] This can be rewritten as:

TE(ψ, ϕ) = Σ E[Yi(1, ψ) – Yi(0, ψ) + Yi(0, ψ) – Yi(0, ϕ)]

direct treatment effect indirect treatment effect

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Tackling spillovers (cont’d.)

A treatment strategy ψ is often compared to no treatment (ϕ = 0). corresponds to the beginning of a regulatory pilot

program.

If the pilot is extended to all firms, treatment strategy changes from the original pilot fraction ϕ to ψ = 1.

In biostatistics, other fractions make sense: Vaccinating 75% vs. 50% of the population

Statistical inference is easier if you have many different groups with only within-group spillovers. Most stats papers discuss this case. Example: “Worms” studies randomized trials in many

villages.

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But most regulatory pilots are one village One solution: identify off of differences-in-differences

regression with controls:Yit = β0 + β1Ti + β2At + β3TiAt + γXit + εit

whereYit is the outcome variable for stock i at time t,Ti = 1 if stock i is in the treatment group, Ti = 0

otherwiseAt = 1 if date t is after treatment (after repeal), else

At = 0Xit is a vector of control variables

The interaction term β3 measures the direct treatment effect.

β2 measures the indirect treatment effect (the average change in control firm outcome from moving to new treatment strategy).

Controls become quite important here.

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Indirect effect non-trivial for uptick repeal

Jan Feb Mar Apr May Jun Jul Aug0%

10%

20%

30%

40%

50%

60%

Shorting prevalence during 2007 in NYSE stocks

non-pilot (treatment) pilot (control)

Sh

ort

ing

as

a f

racti

on

of

trad

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vo

lum

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Tick test repealedDirect treatment effect: +5.8%Indirect treatment effect: +6.0%Total treatment effect: +11.8%

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What’s HFT got to do with all this?

Pilot designers need to think about potential spillovers.

Currently in the U.S.: concern that current market structure is not ideal for small-cap firms.

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2005 2006 2007 2008 2009 2010 2011 2012 201320

40

60

80

100

120

140

160

0

10

20

30

40

50

60

But small-cap trading costs remain high

IS Costs Commissions Average VIX

Co

sts

in b

ps

VIX

Source: Jun 2014 ITG Global Cost Re-view

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SEC plans a new pilot program for smaller-caps To be a pilot stock, must satisfy all of the following:

Market cap of $5 billion or less Average daily volume (ADV) of 1 million shares or less Share price of $2 or more.

Pilot design: 1 control group and 3 test groups Approximately 300 securities in each of the four buckets

Test group 1: Quoted in nickels ($0.05), no other restrictions

Test group 2: Quoted & traded in nickels OR at the mid-point of the NBBO. Retail orders internalized only with price improvement of at

least $0.005. No price improvement required for trades off-exchange (e.g.,

dark pool).

Test group 3 same as group 2 plus: “Trade-at” requirement: off-exchange trades require

significant price or size improvement. Otherwise, must first execute against the full size of on-

exchange, protected quotations at the NBBO before executing off-exchange.

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Overall conclusions

Equity market liquidity in large caps is clearly better than it was 10 years ago. Competition and cost reduction are probably the cause

Regulatory experiments have the potential to clearly identify causal effects. Would be great if Europe could start to do them Must think carefully about spillovers Must design the experiment carefully to maximize info

gained

My predictions and pleas: Due to the nature of information about small firms,

small cap liquidity will always be lousy regardless of market structure

Tick size and trade-at will have close to zero effect Trade-at should dramatically increase liquidity in large-

cap stocks; let’s try the pilot there! 23

For further reading

This talk incorporates elements from the following papers:

Ekkehart Boehmer, Charles M. Jones, and Xiaoyan Zhang (2013), “Shackling short sellers: the 2008 shorting ban,” Review of Financial Studies, 26:1363-1400.

Ekkehart Boehmer, Charles M. Jones, and Xiaoyan Zhang (2014), “Unshackling short sellers: the repeal of the uptick rule,” SSRN working paper.

Terrence Hendershott, Charles M. Jones, and Albert Menkveld (2010), “Does algorithmic trading improve liquidity?” Journal of Finance.

Terrence Hendershott, Charles M. Jones, and Albert Menkveld (2013), “Implementation shortfall and high-frequency price dynamics,” Chapter 9 of High Frequency Trading (edited by Maureen O’Hara, Marcos López de Prado and David Easley), Risk Books.

Charles M. Jones (2013), “What do we know about high-frequency trading?” SSRN working paper.

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