jose gonzalo rangel ucsd capri workshop may 25, 2006

Jose Gonzalo Rangel

UCSD

Capri Workshop

May 25, 2006

Macroeconomic Announcements, Price

Discovery, and Order Flow Effects in the

Stock Market: Evidence from Incomplete

Data and Multiple Financial Markets

Motivation

Asset prices are affected by revisions in expectations driven by news about changing economic conditions (e.g. output, employment and inflation shocks).

The ultimate objectives of monetary policy are expressed in terms of same macroeconomic variables (Bernanke and Kuttner, 2005).

The stock market response to macroeconomic news is linked to market assessments (investor’s beliefs) of future states of the economy and/or Fed actions.

However, the mechanism through which these beliefs enter equity prices remains an intriguing empirical question.

Main Approach

Announcements are pure symmetric information events. Beliefs are homogeneous. The transmission mechanism involves a nearly instantaneous price adjustment (jumps, little trading activity involved)

Andersen et al.(2003, 2005), Boyd, Hu, and Jagannathan(2005), Bernanke and Kuttner(2005)

Problem: under “asymmetric information” the market needs to aggregate heterogeneous beliefs. Transmission mechanism involves a learning process. Learning occurs through trades. Fundamental price is affected by the order flow (sum of signed trades).

Important effects on price dynamic behavior (price discovery), liquidity, and volatility.

Evans and Lyons (2004), Brandt and Kavajecz (2004)

Price Effects of Trading Activity on Announcement Days

Data Evidence: Amihud (2002) illiquidity ratio

Day Amihud a t-stat b Amihud a t-stat b

Non_announcement 3.21E-06 0.00029Unemployment/NFP 3.67E-06 -3.16 0.00032 -2.42

Retail Sales 3.47E-06 -2.16 0.00028 0.93Construction Spending 3.23E-06 -0.17 0.00034 -3.49

NAPM 3.26E-06 -0.36 0.00034 -3.90CPI 3.21E-06 -0.03 0.00028 0.95

Durable Goods Orders 3.12E-06 0.69 0.00030 -1.21New Home Sales 3.18E-06 0.21 0.00029 -0.16

a)

b) Ho: Illiquidity(Non-announcement days)=Illiquidity (Ann. days)Bold (blue): significant at 5% level

Liquidity Measures Based on Daily Data (1992-2003)

S&P500 Futures S&P500

t

t

volume

returnmean

||

Order Flow’s Role Graphically

Symmetric Information Approach

Hybrid

Microstructure Approach

Public info Price

Private info

Price

Order flow

Information

Price

Order flow

Main Empirical Results

• Significant instantaneous news impacts of news related to real activity, investment, inflation, and monetary policy.

• Significant order flow and/or asymmetric information effects on employment days due to:

– Uncertainty on the implications of employment news for stock prices

– Increases in the volatility of fundamental prices

• Asymmetric Information effects come from the interest rate component of equity prices.

• Evidence of excess sensitivity of long term interest rates to employment shocks

– Private agents revise expectations about future Fed policies and/or long run states of the economy (Gurkaynak, Sack, and Swanson, 2005)

– Revisions are not homogeneous

The Microstructure View

Observed transaction price:

Random walk representation of the unobserved fundamental (log) price mt

qt=trade direction (1 if buy, -1 if sell)

Under symmetric information:

where ut accounts for arrival of public information over (t-1,t]

,1 ttt umm

ttt cqmp

Under asymmetric information (Hasbrouck, 2004, 2005):

where

1(or -1) if transaction k was initiated by the buyer (or seller). Nt = number of trades over (t-1,t].

Similar results if Qt is proxied by signed volume, Vtqt, where Vt=f(volume), and qt represents sign(Qt).

tttt uQmm ~1

tNk

kt qQ ~

kq~

The Microstructure View

A Simple Asymmetric Information Model (SAIM)

Theoretical basis: Kyle (1985), and Glosten and Milgrom(1985)

Suppose Kyle’s framework in a one period model:

P0 Announcement Release

v

Demands Signals

Informed &

Liquidity Traders

Informed Traders

J. Gonzalo Rangel

Everybody observes the release, but only the informed agents process this information in an elaborated way to get a signal.I extended the original Kyle's model by allowing for more than one informed agents and noise signals.

Results and Comparative Statics of (SAIM)

Proposition A1: There is a unique linear equilibrium in which

Comparative statics

– λ↑ when fundamental price volatility ↑ (σ2 ↑)

– λ ↑ when volatility of liquidity demands ↓ (σu2

↓ )

– λ↑ when precision of the signal ↓

(σε2 ↑, for M sufficiently large)

– λ↓ when number of informed traders ↑

(M ↑, for M sufficiently large)

Qm

Empirical Specification (SAIM)

Estimation of a structural microstructure model based on:

– Hasbrouck (2004) extension of Glosten and Harris (1988), including price impacts of trades on fundamental prices.

– Incomplete data, daily frequency

Additional extensions:

– News effects on the efficient price

– Average incremental effects of order flow on announcement regimes vs the non-announcement regime.


Observed and efficient prices:

where

– Ik,t= type k announcement Indicator

– Vt= f(trading volume) = (1, Vt)’

–

– qt= trade direction:

Yk,t=Realization of type k macro variable (Yhat=forecast)

k

tktktk

YYS

,,

,

ˆ

ttt cqmp

,)~

( ,,2,,1,1 ttkktkktttkktt SSqVIamm

1010 ,,,~

aaa

0

0

0

||

0

0

0 ,

,,,

,

,,,

tk

tktktk

tk

tktktk S

S

if

ifSS

S

S

if

ifSS

)2/1(~1,1 Bernoulliqt

Remarks:

– Model keeps the same form under time aggregation

– Parameters of interest: λ, , and βs

– Observed variables: prices, volume, news variables

– Unobserved (latent) variables: qt and mt

– 2T possible paths of qt (#Qt=2T, T=Sample size, e.g. 3,120 days)

Likelihood:

Estimation Issues and Econometric Approach (SAIM)

a

TQq

T

ttttt qqpPpf

21 ,,,|),|(

ttktttkttt SqVaIqqcp ,,1 )~

()(

How to estimate?

• Simulated Maximum Likelihood (SMLE)

• Bayesian Markov Chain Monte Carlo (MCMC)

– Hasbrouck (2004, 2005)

MCMC advantages

• Computationally convenient

• Parameter uncertainty

– Uncertainty about news effects

– Uncertainty about asymmetric information

Estimation Issues and Econometric Approach (SAIM)

The MCMC Algorithm

Desired posterior:

Given the set of parameters

and q(0):

Step1: Draw Θ(1) from

Step2: Draw q(1) from

Step3: Continue in this fashion until

generate a sequence

whose limit is the desired posterior F

2,,,~

, ac

|, qF

IGMVNqP /~,| )0(

,| )1(qP

K

kkk q 1

)()( ,

Data

S&P500 (daily) data on closing prices and volume from CRSP. Sample period: 1992-2003.

Futures (daily) data on closing prices and volume for S&P500, bonds (5Y, 10Y) and exchange rates (US/YEN) from Datastream

19 Macroeconomic announcements and forecasts from MMS regarding:– Real Activity: IP, RS, NFP, UMP, CU, PINC,

and CCR– Consumption: NHS, PCE– Investment: DGO, CS, and BI– Trade: GSTB– Price Level: CPI, PPI– Forward Looking: LI, NAPM, and HS– Monetary Policy: FOMC/FFR

Hist

Announcement

λ ak

REAL ACTIVITY β1, k β2, kAverageEffect*

Average Effect*

Nonfarm Payroll -0.271 -0.005 0.360 0.351(0.123) (0.115) (0.064) (0.111)

Unemployment 0.035 0.020 0.340 0.301(0.129) (0.097) (0.065) (0.110)

Retail Sales -0.059 0.317 0.324 -0.263(0.119) (0.131) (0.070) (0.414)

INVESTMENT

Construction Spending -0.012 0.217 0.326 0.109(0.099) (0.109) (0.076) (0.124)

PRICES

CPI -0.461 0.11972 0.336 -0.314(0.134) (0.093) (0.074) (0.337)

MONETARY POLICY

FFR -0.100 0.179 0.315 0.122(0.183) (0.096) (0.074) (0.234)

Standard errors in parentheses *)Bold (Blue): 5% SignificantItalics (Red): 10% Significant

ESTIMATION RESULTS FOR S&P500

Asymm. Info. Coeff.News Effects

)(

)(

10

10

volumeAvgaa

volumeAvg

,)~


Announcement

λ ak

REAL ACTIVITY β1, k β2, kAverageEffect*

Average Effect*

Nonfarm Payroll -0.311 0.008 0.535 0.261(0.119) (0.106) (0.040) (0.097)

Unemployment -0.025 -0.040 0.528 0.233(0.122) (0.091) (0.042) (0.108)

Retail Sales -0.023 0.289 0.528 -0.309(0.106) (0.131) (0.042) (0.311)

INVESTMENT

Construction Spending 0.201 0.296 0.545 0.109(0.136) (0.154) (0.041) (0.100)

PRICES

CPI -0.487 0.104 0.549 -0.219(0.145) (0.093) (0.041) (0.189)

MONETARY POLICY

FFR -0.173 0.174 0.524 0.246(0.207) (0.085) (0.042) (0.103)

Standard errors in parentheses *)Bold (Blue): 5% SignificantItalics (Red): 10% Significant

ESTIMATION RESULTS FOR FUTURES S&P500

Assym. Info Coeff.News Effects

)(

)(

10

10

volumeAvgaa

volumeAvg

,)~


Slide lambda

Group Obs Mean Std. Dev.All Days 3026 706.65 445.05

Non-Employment Days 2885 706.54 445.28Employment Days 141 708.96 441.91

Descriptive Statistics for S&P500 Trading Volume

Variance Ratio TestHo: sd(Non-Employment) = sd(Employment) Ha: sd(Non-Employment) > sd(Employment)

F Stat = 1.015P-Value = 0.4655

Comparative statics

– λ↑ when fundamental price volatility ↑

– λ ↑ when volatility of liquidity demands ↓ (σu

2 ↓ )

– λ↑ precision of the signal ↓ (σε

2 ↑, for M sufficiently large)

– λ↓ when number of informed traders ↑ (M ↑, for M sufficiently large)

Back

λ ak

Type of Release β1,k β2,kAverageEffect*

Average Effect*

Nonfarm Payroll -0.2403 0.1818 0.0518 0.1047(0.0380) (0.0527) (0.0131) (0.0245)

Unemp 0.1493 -0.0713 0.0475 0.1214(0.0439) (0.0314) (0.0129) (0.0240)

Nonfarm Payroll -0.3839 0.1743 0.0719 0.1465(0.0466) (0.0522) (0.0197) (0.0341)

Unemp 0.1739 -0.1389 0.0637 0.1840(0.0645) (0.0474) (0.0201) (0.0344)

Standard errors in parentheses *)Bold (Blue): 5% Significant

Employment Effects on Bond Futures Markets

10Y Notes

News Effects Asymm. Info. Coef.

5Y Notes

)(

)(

10

10

volumeAvgaa

volumeAvg

,)~


Robustness: Evidence from Bond Markets

Announcement

λ ak

REAL ACTIVITY β1,k β2,kAverageEffect*

Average Effect*

Nonfarm Payroll -0.1794 0.0360 0.2494 -0.2167(0.1073) (0.0748) (0.0270) (0.2836)

Unemployment 0.0985 -0.1183 0.2634 -0.2076(0.0945) (0.0700) (0.0273) (0.2661)

Standard errors in parenthesesBold (Blue): 5% Significant (*)Italics (Red): 10% Significant

News Effects Asymm. Info. Coeff.

ESTIMATION RESULTS FOR FUTURES FX US/YEN

)(

)(

10

10

volumeAvgaa

volumeAvg

,)~


Evidence from Exchange Rates

Summary of Empirical Results

For the stock market

– Instantaneous fundamental news impacts (asymmetric)

– Order flow effects on employment days

For long term bond markets

– Fundamental news effects as predicted by the asset pricing view

– Strong order flow effects on employment days

For exchange rates

– Just fundamental news effects on employment days

Results consistent with recent literature

Pasquariello and Vega (2005): Day-to-day bond yield changes and order flow are most sensitive to Nonfarm Payroll Employment announcements (based on intradaily data) .

Morris and Chin (2002): Overreaction to employment news. Bond yields are most reactive to the types of news emphasized by the press.

Does employment convey more information about future growth? No evidence

Does employment convey more information about future inflation? More likely

Contribution: Why is this distinction interesting?

Relevant for practitioners and policy makers. – Provides new methods for measuring impacts

of output and inflation shocks in financial markets.

– Provides new measures on how homogeneous is the market evaluation of future Fed reactions to these shocks.

Provides an explanation for observed patterns in different price “characteristics”, such as volatility and liquidity.

Contributes to a better understanding of link between macroeconomic information and the price discovery process (one of the main functions of financial markets).

“Assets trade in markets, markets provide liquidity and price discovery, and asset prices are influenced by the transaction costs of liquidity and the risk of price discovery” (O’Hara, 2003)

Concluding Remarks

Evidence of incremental asymmetric information costs on employment days.

Changes in the asymmetric information coefficient on employment days due to:– Uncertainty on the implications of

employment news for asset prices– Increases in volatility of fundamental

prices.

Bond markets point to asymmetric information on the interest rate component of stock prices.– Consistent with the excess sensitivity of

long-term interest ratesNot only investors change their long run expectations of the state of the economy and long-run Fed policies, but also they have heterogeneous beliefs.

Future Research

Analysis with “complete” information

– More flexible specification for conditional volatility

Time varying news effects

Time varying order flow effects

Explore correlations in the trade direction (or order flow)

Analysis of individual stocks

Include earnings announcements

Conditional Posterior for the latent trade direction:

Where,

Back

*2

,,,|2

*1

ttt MqqP

2

1,11,11

2

1,11,11

2

1,11,11

4

~~exp

4

~~exp

4

~~exp

tktttkttttktttkttt

tktttktttt

SqVSVmmSqVSVmm

SqVSqVmm

1,111,1* ~~

2

1 tkttttktttt SqVmSqVmM

Observed Price:

Returns:

ttt cqmp

ttktttktt SqVaIqcp ,, )(

Posterior Distributions for News Effects and Asymmetric Information Parameters

0 0.25 0.5 0.75

200

400

600

800

1000

1200Lamda (All Days)

-0.75 -0.5 -0.25 0 0.25

200

400

600

800

1000B1,k (Positive Surprises, NFP)

-0.5 -0.25 0 0.25 0.5

200

400

600

800

1000B2,k (Negative Surprises, NFP)

0 0.25 0.5 0.75

200

400

600

800

1000

1200Incremental Lamda (NFP/Unemp Days)

200 400 600 800 1000 1200 1400 1600 1800 2000 2200-0.5

0

0.5

1

1.5

2Incremental Effect on Employment Announcement Days

200 400 600 800 1000 1200 1400 1600 1800 2000 2200-0.5

0

0.5

1

1.5

2

Daily Volume

Total Effect

200 400 600 800 1000 1200 1400 1600 1800 2000 2200-0.5

0

0.5

1

1.5

2All Days

All Days

Employment Days

Total Effect

95% ConfidenceInterval


Cu

mu

lati

ve Im

pac

t (B

asis

Po

ints

)Asymmetric Information Effect

S&P500

Asymmetric Information Effect (Futures S&P500)

Cu

mu

lati

ve Im

pac

t (B

asis

Po

ints

)

50 100 150 200 250 300 350 400-1

0.5

2

3.5

5

Incremental Effect on Employment Announcement Days

50 100 150 200 250 300 350 400-1

0.5

2

3.5

5

6.5

Daily Volume

Total Effect

50 100 150 200 250 300 350 400-1

0.5

2

3.5

5

All Days

All Day s

Employ ment Day s

Total Ef f ect



M

iixuQQvEm

1

,|

Post announcement “true” value,

M informed traders get noisy signals about the “true” price impact of a particular news event,

Informed agent i demands xi units of the asset

Noise traders demand

A market maker sets prices after observing aggregated order flow. Fundamental post announcement price satisfies market efficiency

),0(~ 2uNu

Assumptions

20 ,~ PNv

,mm vs ),0(~ 2 iidNm

jose gonzalo rangel ucsd capri workshop may 25, 2006

Documents