1. Market Microstructure Bid-Ask Spreads and PIN Daniel Sungyeon Kim

2. Bid-Ask spread The bid-ask spread can be decomposed
into what two components?
Adverse selection component
Transaction cost component

3. Four-way Decomposition of the spread
Adverse selection component
Transaction cost component can be further decomposed into three pieces:
Inventory risk component – compensates dealers for bearing price risk their inventory
Order processing cost component – compensates dealers for their normal cost of doing business
Monopoly profits component – extra profits that can be extracted by monopoly power

4. Four-way Decomposition of the spread
Recent estimate by Henker and Martens:
Adverse selection = 15% of spread = 1.0 cents
Inventory risk = 17% of spread = 1.2 cents
Order processing = 65% of spread = 4.5 cents
Monopoly profits = 3% of spread = 0.2 cents
Total spread = 6.9 cents

5. Explanations for Adverse Selection Comp.
What are the two explanations for adverse selection component?
Information perspective
Accounting perspective

7. Figure 14-1 Ask0 = V0 + (1/2) Adv Selection + (1/2) Transaction Cost
Bid0 = V0 – (1/2) Adv Selection – (1/2) Transaction Cost
Figure directly shows information perspective:
If buy trade, value increases from
If sell trade, value decreases from

9. Figure 14-2 Suppose there is a buy trade, value increases from
This becomes the new midpoint:
Ask1 = V1 + (1/2) Adv Selection + (1/2) Transaction Cost
Bid1 = V1 – (1/2) Adv Selection – (1/2) Transaction Cost
If next trade = buy, value increases from
If next trade = sell, value decreases from
Change in value = V1 –V0 = (1/2) Adv Selection
Adverse Selection = Permanent Component of the spread
Vs. Transaction Cost=Transitory Component of the spread
That is, Ask went away  did not become part of change in value
If the market is Efficient = reflects all available info,
then value changes are unpredictable = Random Walk

10. “Most Important Lesson in this Book for Most Readers”
Uninformed traders lose to informed trader regardless of whether they use a limit order or market order
Example: Ask = 30.10, Bid = 30.00, informed have good news that stock is worth 35.00
(1) Uninformed submits limit sell at 30.10
 informed buys at 30.10, price rises to 35.00
 uninformed sold at & missed price rise
(2) Uninformed submits limit buy at 30.00
informed submits limit buy at (or buys at 30.10)
price rises to 35.00
uninformed limit buy doesn’t execute & misses price rise
(3) Uninformed submits market buy or market sell
 pays the spread, which is wider due to adv select comp

11. Easley, Keifer, O’Hara, and Paperman
Develop a simple, classic model of adverse selection
Can estimate how much informed trading in any stock
 PIN = Probability of INformed trade
= % of traders that are informed

13. PIN Computation Chance of Informed Sell = Chance of Informed Buy =
Chance of Informed Trade =
Chance of Uninformed Trade=
PIN = % of traders that are informed

14. A PIN example Day Buys Sells 1 53 49 2 74 51 3 48 78 4 50
Best guess of α? Two events out of four trading days = 0.50.
Best guess of δ? One good event, one bad event; 0.50
Best guess of ε? 50
Best guess of μ? 25
Best guess of PIN? .5x50 / (.5x50 + 2x50) = 25 / 125 = 20%

15. PIN Model Dynamics Some traders are informed and others are uninformed
Trading is anonymous
 Market makers don’t know which
trader is informed vs. uninformed
 Every trade causes a price reaction
Every sell causes a lower bid and ask
Every buy causes a higher bid and ask

16. = PIN

17. PIN Sampler e m
Vega
PIN parameters
year-by-year a d
PIN

18. PIN Sampler Agudelo In Indonesia, who is more informed: foreigners
or locals
PIN(foreigners)
vs. PIN(locals)

20. PIN Sampler Easley, Engle, O’Hara, Wu Dynamic PIN model PIN estimates
for 16 stocks

22. PIN Sampler Easley, De Prado, O’Hara Modified Version of PIN = VPIN
during for
the 2010
Flash
Crash