High Frequency Trading: Economics, Empirics & Politics

Bruno Biais (Toulouse School of Economics, FBF IDEI Chair on Investment Banking and Financial Markets)

Presentation prepared for the Banque de France Conference on Algorithmic and High-Frequency Trading

November 2013

Lots of information in financial markets

Difficult and costly to process

Slower than the others = too late

NASDAQ

ARCA

NYSE

BATS

EDGX

EDGA

NASDAQ BX

Fragmented markets

Due to regulatory push for competition & technology

Lots of quotes in different markets: Disappear quickly

Hard to identify best trading opportunities before vanish

Financial institutions’ response: Investment in market technology

Fast connection (fiber-optic, colocation, throughput)

Clever terse code

Computers that read Bloomberg faster than eye blinks

Minimize latency = delay between info event & trade execution

This talkDiscussion based in particular on the following papers:

Biais & Foucault (2013) survey forthcoming in Bankers Investors & Markets

Baron, Brogaard, Kirilenko (2012) [CME data]

Biais, Foucault, Moinas (2012) [theory paper]

Hendershott & Riordan (2013) forthcoming JFQA

1)Consequences of HFT, for given fast trading technology

2)Equilibrium investment in fast trading technology

3)Policy and politics

1) Consequences of HFT, for given fast trading technology

2) Equilibrium investment in fast trading technology

3) Policy and politics

Info. advantage for fast marketable orders, adverse selection cost for slow limit orders

Limit orders

Ask = 100,01Midquote: 100Bid = 99,99

New info:value = 100.02

HFT observes info quickly:Buy at 100,01

Slow observesinfo, wants to cancel bid: too late

t

Info eventually observed by all, midquote rises to 100,02

Fast Profit Slow loss

Implication

Ask = 100,01

M = 100

Bid = 99,99

HFT buys

t

Ask = 100,03

M = 100.2

Bid = 100,01

HFT marketable buy order followed by increase in midquote

Hendershott & Riordan (2013): Algorithmic trades’ impulse response

Ask = 100,01

Bid = 99.99

New info:value = 100.02

1st HFT observes info quickly: cancels limit order to sell at 100.01

Slowobserves Info: wants to cancel ask, too late

t

2nd HFT observes info quickly: buys at 100,01

Lower adverse selection cost for fast limit

Fast No Loss Fast Profit Slow loss

Baron, Brogaard, Kirilenko (2012)

CME E.mini S&P 500 futures

HFTs average profits: $ 0.77 per contract

Marketable orders: $ 0.93 per contract

(at the expense of institutional traders)

Limit orders: $ 0.33 per contract

(losing money versus HFT and market makers)

Teaser Biais, Declerck, Moinas (in a few minutes)

Industrial Organization of Liquidity Supply

Fast limit less exposed to adverse selection than slow limit

Two countervailing effects for liquidity supply:

1)Lower cost for fast liquidity suppliers: makes liquidity cheaper

2)Slow limit orders, exposed to winner’s curse, exit market => Fast face less competition from slow, better able to extract oligopoly rents (// Biais, Martimort, Rochet, Econometrica 2000): makes liquidity more expensive

Which effect will dominate? Ambiguous.

Benefits of HFT for society

Reduces adverse selection cost for liquidity supplier:

tends to increase quantity of liquidity supplied

=> larger depth at quotes

and to reduce cost of liquidity supply

=> tighter spread

Facilitates arbitrage across markets & helps linking fragmented markets

Costs of HFT for society

Fast marketable orders

=> adverse selection costs for limit orders

Deters placement of limit orders by slow traders

=> reduces competition to supply liquidity

Tends to reduce quantity of liquidity supplied

=> lower depth at quotes

and to increase cost of liquidity supply

=> larger spread

1) Consequences of HFT, for given investment in fast trading technology

2) Equilibrium investment in fast trading technology

3) Policy implications

Social costs and private gains

Some of the social benefits of HFT are aligned with private gains of high frequency traders:

reduced cost of liquidity supply

better alignment of markets

But some of the social costs of HFT also are aligned with private gains of high frequency traders

adverse selection costs of orders picked off by fast traders

are mirror image of trading profits of fast traders

An example of the divorce between private and social efficiency

Project Express: fiber-optic cable across Atlantic

Reduces data roundtripNY-London: from 64.98to 59.6 milliseconds

Handful of trading firms Subscribed

Cost = $ 300 million

Profitable for subscribers (otherwise stay out), not for society: cost of socially useless investment passed to slow traders

“foreknowledge: whatever does actually occur will, in due time, be evident to all”

“the distributive aspect of access to superior information … provides a motivation for the acquisition of private information that is quite apart from any social usefulness of that information.”

“There is an incentive for individuals to expend resources in a socially wasteful way in the generation of such information.”

Hirshleifer (AER, 1971)

Investment in fast trading technology iff

Private gain > private cost

Since private gain includes profits from picked off limit orders, we can have

Private gain > social gain

Since private cost does not reflect adverse selection cost borne by slow traders, we can have

Social cost > private cost

Hence we can have

Equilibrium investment > socially optimal investment

Excess equilibrium investment

To the extent that investment in fast trading technology motivated by desire to earn rents

Informational rents mirror image of adverse selection costs

Market power rents if slow liquidity suppliers exit

Opportunity cost of resources allocated to HFT

“The existence of an opportunity to obtain monopoly profits will attract resources into efforts to obtain monopolies, and the opportunity costs of those resources are social costs of monopoly too.”

Posner (NBER WP, 1974)

Privately optimal to be fast if

– C >

Equilibrium investment in fast trading techno(Biais, Foucault, Moinas, 2012)

Trading gain | fast

Investment cost

Trading gain | slow

Privately optimal to be fast if

– > C

My investment depends on the fraction of others’ that are fast ()

Trading gain | fast

Both decrease with :My marketable orders face larger spreadMy limit orders are more picked off

Which one decreases faster with ? or ?

Determines whether – increasing or decreasing

Trading gain | slow

– in Biais, Foucault, Moinas (2012)

0 1

Large : slow bear larger adverse selection costs until evicted

Small : fast suffer more from higher spread because trade more

C large => equilibrium investment = 0

C

0 1

.

For all advantage of being fast < cost

Small C => equilibrium investment =1

C .For all advantage of being fast > cost

Intermediate C: multiple equilibria

C

. . .

Interior equ: s.t. advantage of being fast = cost

For advantage of being fast > cost

Strategic complementarity

If increase in hurts slow more than fast

increasing in

Relative profitabiliy of investment increases with

Investment decisions are strategic complements

(Local as well as global complementarities)

Complementarity => equilibrium multiplicity

(but stable not **)

Contagion

If I expect all the others to be fast (I expect )

I must also be fast, lest I should be evicted

We all do the same: as expected

=> investment waves in HFT

1) Consequences of HFT, for given investment in fast trading technology

2) Equilibrium investment in fast trading technology

3) Politics & policy

If there is too much HFT, should we tax it?

Theoretical misgivings: If we tax high message traffic, maybe we’ll deter the “good” kind of HFT (limit orders need to be modified or cancelled often to avoid adverse selection) without affecting the “bad” kind (adversely hitting limit orders)

Practical awkwardness: French tax (August 1st 2012) only for French firms, not foreign ones …

Teaser J.E. Colliard this afternoon

Market response

HFT free platforms

Or give slow traders option to execute only against slow orders

If slow traders find execution against fast costly, they will choose this option

Concerns

If it is expected that there will be no liquidity on HFT free platform, nobody will go there, confirming expectation: “bad equilibrium”

If HFT can influence exchanges’ policy, they could prevent exchanges from offering option to place HFT free orders (i.e., orders precluding execution against fast)

An interesting experience

Baron, Brogaard, Kirilenko (2012) quoted above

Results pretty damning for HFT

CFTC study, based on CME data

CME sued by HFT firms: data supposed to be used for monitoring and regulation, not research !

Baron, Brogaard & Kirilenko had to withraw their study ! Officially, does not exist any more (but I still have it ;-)

Suggests significant lobbying power of HFT firms

Systemic risk concerns

HFT firms have very little capital

HFT transactions occur at much higher frequency (many per second) than clearing (daily)

HFT trade a lot with one another, often in same direction, or transferring hot potatoes

If one HFT, with little capital, takes big loss => could go bust

This could propagate to other HFTs => lots of uncleared trades, mess

Mitigating systemic risk

Capital requirements for HFT firms

Capital reduces risk of going bust

Also increases skin in the game or fund manager, reducing temptation to gamble

Stress tests, at level of each trading firm, to evaluate impact of shocks

Conduct pilot experiments

Similar to pilot introduction of TRACE in US

For example, instead of introducing two taxes on August 1st, 2012 (one on daily trades, the other on HFT)

It would have been a good idea to introduce the two taxes at different point in time (so that the effect of each one could have been evaluated independantly) and for a randomly selected pilot sample (to conduct diff in diff evaluation)