Detecting Liquidity Traders

By

Avner Kalay (Tel Aviv U. and U. of Utah)

Avi Wohl (Tel Aviv U.)

Main Questions:

A. Are there indeed “liquidity traders”?

•A common assumption in microstructure models: “liquidity traders” that are willing to buy / sell in any price.

•  287 papers in ECONLIT contain in their abstract “liquidity trad*” or “noise trad*”.

• In continuous trading environment it is hard to detect willingness to buy or sell in any price.

B. The information content of the demand and supply schedules of stocks in call auctions

Example of the idea:

Three strategic investors and liquidity traders.

Investor 1 excess demand: q1 = 10 – p

Investor 2 excess demand: q2 = 12 – p 

Investor 3 excess demand: q3 = 14 – p 

We break the excess demand of strategic investors to demand and supply.

Liquidity traders’ net supply: Z = Zs - Zd

9

10

11

12

13

14

15

0 1 2 3 4 5 6 7 8 9

Quantity

Price D

S

No liquidity traders: Zs = Zd = 0

8

9

10

11

12

13

14

15

16

0 1 2 3 4 5 6 7 8 9

quantity

Price D

S

Zd = 2 , Zs = 1

• 0 M(P) 1 is the proportion of strategic traders

whose valuation exceeds the price (they are on the buy

side)

slopescurvedemandslopescurveply

slopescurvedemandpM

'/1_'_sup/1

'/1)(

Our new measure of the presence of strategic traders in the market is

•In our case,

M(12.333) = [1/1]/[(1/0.5) +(1/1)] = 1/3

BP = 1 - 2M is a measure for the liquidity buying pressure :

BP > 0 (liquidity buying pressure)

BP < 0 (liquidity selling pressure)

In our case BP = 1-2*0.333 = 0.3333

BP is negatively correlated with future price change

Buying Pressure (BP)

Modeling Call Auction

•N strategic. Each has linear excess demand function

Qj = aj(uj – p)

•Inelastic liquidity net supply Z =Zs - Zd

•Result:

•Compatible with NREE (Noisy Rational Expectations Equilibrium) models of Hellwig(1980) or Kyle(1989) or with inventory models.

•Our restating: separating demand and supply BP

n

jj

j

n

jn

ii

j

a

Zu

a

ap

1

1

1

*

Numerical example based on Hellwig 1980

8

9

10

11

12

0 0.2 0.4 0.6 0.8

quantity

pric

e

2,1,1,10,10,6 sn x yj: 8, 9, 10.5, 11.5, 12.5, 14

The two most related papers are:

First- Kandel, Sarig and Wohl (1999) Examine Israeli auctioned IPOs

Positive correlation between flatness of demand curve (revealed after the auction) and subsequent price change .

Interpretation: Flat demand curve is “good news” about information accuracy and/or future liquidity.

Second- Madhavan and Panchapagesan (2000)

•NYSE openings

•Market orders probably distort the price

•Specialist intervention, according to market order imbalance and previous price ,improves price efficiency

This Paper

•Empirical implications of models with liquidity traders.

•Empirical evidence using opening sessions in Tel Aviv Stock Exchange (TASE)

Trading Mechanism in the TASE• In 97-98 TASE moved to a computerized limit order book system (as in Paris and many other exchanges). No market makers.

•The day begins with a call auction.•The pre-opening is partially transparent (investors observe projected price and volume and segments of the demand and supply schedules).

•No cancellations in the last 15 minutes. •The equilibrium price: intersection of demand and supply schedules.

TEVA (629014) – JAN-3-2002 one minute before the openingPrevious’ day closing – 277.70

projected: price –276.00 volume - 12884

Buy Buy Sell Sell Quantity Limit Limit Quantity

1 5 351.00 263.80 19 2 114 350.00 269.40 170 3 13 319.40 270.00 160

Actual: price –276.00 volume -14244

Data

•All orders and transactions in TASE opening sessions and all closing prices.

•The sample period: 25.1.98 – 28.9.98 (167 trading days).

•105 stocks . •Average # orders per session – 31

Return Predictability

We look at open-to-close returns and try to explain them by variables from the opening session

Explanatory Variables

•10% limit on price change (from previous close)

eliminates pure market orders

•We classify an order as a “market order” if the

limit is different in absolute value by more than

9.5% from the previous close.

•Zd (Zs) – is the proportion of buy (sell) “market orders” out of the trading volume

•We find mean Zd= 0.123 and mean Zs= 0.232 The difference is significant.

BP is proxied by

BP=1- 2*Dif_D /(Dif_D + Dif_S)

Where: Dif_D = the difference between the demanded

quantity ½% below the equilibrium and the demanded

quantity ½% above the equilibrium. Dif_S = …

 

Mean BP = -0.112

Extreme values: 1 (-1) in 12.0% (16.7%) of cases

Example Price Demand Supply

422 10,000 12,500

420 12,000 12,000

418 13,000 11,500

M=3,000/(3,000+1,000)=0.75

BP=1-2*0.75=-0.5

Explanatory Variables (contd.)

Additional explanatory variable:

LR – previous close-to-open return

Versions of the regressions:

Separate regression for each stock. Looking at the series of 105 coefficients

For each coefficient we report: mean, t and # positive (out of 105)

ititiitiitiitiiit LRBPZsZdR 4321

  1 2 3 4 5 6

CONSTANT 0.480(11.46)

0.646(-12.11)

0.469(10.78)

0.455(14.04)

0.378(10.22)

0.43612.17)

Zd-0.966(-8.24)

13

------0.222(-2.04)

38

----0.108(1.14)

58

----

Zs 1.569(14.52)

100

-----0.825(7.77)

85

----0.193(2.60)

60

----

BP ----- -1.174(-29.87)

0

-0.989(-27.30)

0

-----0.722

(-19.05)4

-0.747(-20.31)

3

LR----- ----- -----

-0.367(-16.45)

4

-0.275(-13.01)

7

-0.280(-12.74)

8

R2 0.089 0.145 0.181 0.185 0.264 0.250

R2-adj 0.075 0.139 0.162 0.179 0.242 0.239

Table 1

Aggressive orders are like market orders: Orders to buy (sell) in prices between 5%-9.5% above (bellow) previous price also predicts subsequent price decrease (increase) . Table 2

ititSiitsiitDiitdiiit AGGRESSIVEZAGGRESSIVEZR 4321

1 2 intercept 0.480

(11.46) 0.384 (9.55)

Zd -0.966 (-8.24)

13

-0.946 (-8.07)

14 AGGRESIVEd ----- -0.885

(-7.24) 20

Zs 1.569 (14.52)

100

1.645 (14.41)

100 AGGRESIVEs ----- 1.445

(12.57) 95

R2 0.089 0.130 R2-adj 0.075 0.104

• The empirical evidence is consistent with our

predictions.

• Aggressive buy (sell) orders are likely to be

followed by price decrease (increase) most

likely: uninformed orders.

• BP is a good predictor for subsequent price

change.

• Differences between buys and sells.

Part 2Based on the ability to detect (partially) liquidity pressures:

•Commonality of liquidity pressures

•Beginning of the month effect

•Persistence of liquidity pressures

•The contagion effect - the effect of liquidity pressure in one stock on prices of other stocks (in addition to commonality)

Commonality in Liquidity

Chordia, Roll and Subrahmanyam(2000), Hasbrouck & Seppi(2001)

This paper - commonality in the arrival of liquidity traders

Is There Commonality in Liquidity Pressures?

istock

excludingaveragesarePBsZdZWhere

PBM

sZdZZs

sZdZZd

ti

ti

ti

itti

iiit

itti

iti

iiit

itti

iti

iiit

,

1

21

21

,

For each stock we estimate the regressions

ZdiZsiBPi

intercept 0.0610.089 -0.028

Avg(Zd-i)0.659(10.21)

90

-0.137

(-2.00)

47

Avg(Zs-i)-0.087

(-2.24)

39

0.704(14.75)

97Avg(BP-i)0.746

(15.94)97

R2-adj0.0360.0320.034

Table 4

Conclusion: There is commonality in liquidity pressures. Liquidity buys (sells) are positively correlated with liquidity buys (sells) in other stocks and negatively correlated with liquidity sells (buys) in other stocks.

Beginning of Month effect

The first 4 days of the month (23 obs)

The last 26 (or 27) days of the months

(144 obs)

Standard Deviation

t value for difference of means

Zd0.1500.1180.0473.03

Zs0.2340.2320.0620.17

BP-0.046-0.1220.1522.14

In many countries (including Israel): beginning of month effect. Our findings: Beginning of month effect in liquidity buying pressure . Table 5

Is There Persistence in Liquidity Pressures ?

For each stock we estimate the relations between proxies for liquidity pressures and there lags.

Regression Zdi (4.4)

Zsi (4.5)

BPi (4.6)

intercept

0.116

0.226

0.519

)( itZdLag 0.036 (3.88)

66

-0.019 (-1.27)

45

----

)( itZsLag 0 (0) 50

0.042 (3.56)

64

----

)( itBPLag ---- ---- 0.067 (7.24)

78 R2 0.017 0.026 0.013

R2-adj 0.003 0.012 0.006

Table 6

Conclusion: There is persistence in liquidity pressures (stock-specific and market-wide).

The contagion effect of noise

•Admati(1985) - liquidity pressures in one stock affects other stocks’ prices.

•Intuition: substation or information effect

•The contagion effect may explain commonality in liquidity measures.

Findings:

• There is commonality in liquidity pressures. Liquidity buys (sells) are positively correlated with liquidity buys (sells) in other stocks and negatively correlated with liquidity sells (buys) in other stocks.

•Beginning of month effect in liquidity buying pressure

•There is persistence in liquidity pressures (stock-specific and market-wide).

• Contagion effect : liquidity pressures in one stock affects other stocks’ prices (Intuition: substation or information effect)The contagion effect may explain commonality in liquidity measures.

Summary

• TASE opening session data (call auctions).

• “Market” buy (sell) orders are negatively (positively) correlated with subsequent price change.

• A new measure, M, is a proxy for the proportion of strategic traders on the buy side and to the asymmetry between liquidity traders BP : buying pressure

• BP is negatively correlated with subsequent price change.

• A support for the assumption of liquidity traders who do not condition their demand/supply on price.

• Differences between buyers and sellers.

Summary (cont.)

• Commonality of liquidity pressures.

• Beginning of month effect

• Persistence of liquidity pressures

• Contagion effect - liquidity pressure in one stock affects the price of other stocks.

• Issue for further research: the effect of pre-trade transparency