1. 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

3. Lots of information in financial markets
Difficult and costly to process
Slower than the others = too late

4. Due to regulatory push for competition & technology
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

5. 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

6. This talk Discussion 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
Consequences of HFT, for given fast trading technology
Equilibrium investment in fast trading technology
Policy and politics

7. Consequences of HFT, for given fast trading technology
Equilibrium investment in fast trading technology
Policy and politics

8. Info. advantage for fast marketable orders, adverse selection cost for slow limit orders
New info:
value =
HFT observes info quickly:
Buy at 100,01
Slow observes
info, wants to cancel bid:
too late
Info eventually observed by all, midquote rises to 100,02
Limit orders
Ask = 100,01
Midquote: 100
Bid = 99,99 t
Fast Profit Slow loss

9. Implication t Ask = 100,03 M = 100.2 Bid = 100,01 Ask = 100,01 M = 100
HFT buys t
HFT marketable buy order followed by increase in midquote

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

11. Lower adverse selection cost for fast limit
New info:
value =
1st HFT observes info quickly: cancels limit order to sell at
2nd HFT observes info quickly: buys at 100,01
Slow
observes
Info: wants to cancel ask, too late
Ask = 100,01
Bid = 99.99 t
Fast No Loss Fast Profit Slow loss

12. 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)

13. Industrial Organization of Liquidity Supply
Fast limit less exposed to adverse selection than slow limit
Two countervailing effects for liquidity supply:
Lower cost for fast liquidity suppliers: makes liquidity cheaper
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.

14. 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

15. 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

16. Consequences of HFT, for given investment in fast trading technology
Equilibrium investment in fast trading technology
Policy implications

17. 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

18. An example of the divorce between private and social efficiency
Project Express: fiber-optic cable across Atlantic
Reduces data roundtrip
NY-London: from 64.98
to 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

19. Hirshleifer (AER, 1971)
“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.”

20. Excess equilibrium investment
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

21. Posner (NBER WP, 1974)
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.”

22. Equilibrium investment in fast trading techno
(Biais, Foucault, Moinas, 2012)
Privately optimal to be fast if
f – C > y
Trading gain | fast
Trading gain | slow
Investment cost

23. My investment depends on the fraction of others’ that are fast (a)
Privately optimal to be fast if
f(a) – y(a) > C
Trading gain | fast
Both decrease with a :
My marketable orders face larger spread
My limit orders are more picked off
Which one decreases faster with a ? f or y ?
Determines whether f(a) – y(a) increasing or decreasing
Trading gain | slow

24. f – y in Biais, Foucault, Moinas (2012)
Small a : fast suffer more from higher spread because
trade more
Large a : slow
bear larger
adverse selection
costs until evicted

25. C large => equilibrium investment a = 0. C
f - y a
For all a advantage of being fast < cost

26. Small C => equilibrium investment a =1
f - y . C a
For all a advantage of being fast > cost

27. Intermediate C: multiple equilibria. . . C
f - y a
a* a**
Interior equ: a s.t. advantage
of being fast = cost
For a =1 advantage
of being fast > cost

28. Strategic complementarity
If increase in a hurts slow more than fast
f - y increasing in a
Relative profitabiliy of investment increases with a
Investment decisions are strategic complements
(Local as well as global complementarities)
Complementarity => equilibrium multiplicity
(but a = 1 stable not a**)

29. Contagion If I expect all the others to be fast (I expect a = 1)
I must also be fast, lest I should be evicted
We all do the same: a = 1 as expected
=> investment waves in HFT

30. Consequences of HFT, for given investment in fast trading technology
Equilibrium investment in fast trading technology
Politics & policy

31. 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

32. 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

33. 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)

34. 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

35. 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

36. 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

37. 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)