Macroeconomic News Announcement Effects on Stocks

Allison Keane

Motivation

Determine if there exists a relationship between news announcements and stock returns

News announcements occur before market opens – need appropriate measure of returnNeed appropriate measure of standardization

Equations

Returns R1000 = log(P1000,t+1) - log(Pclose,t )

Announcements (data taken from Yahoo Finance)

Skt = (Akt – Ekt ) / σk

Have to standardize because of units Realized Variance

RV = Σr2 (calculated using five minute log returns)

DRVt = √ RVt-1

WRVt = √((1/5)*(RVt-1 + RVt-2 + RVt-3 + RVt-4 +RVt-5))

Equations

Standarizing RStandardize R since S is standardizedRt/DRVt

Rt/WRVt

Rt/MRVt

RegressionsRt/WRVt = βkSk,t + εt

Different standardizations of R

Stocks

Focused on four stock from S&P100 Begin with different industries

Procter & Gamble (PG) Kraft (KFT) American International Group (AIG) Ford (F)

Later expand to see if similarities among industries Avon Product Inc.(AVP) Hartford Financial Group(HIG) Allstate Corp. (ALL) Colgate-Palmolive(CL)

Data sets include one minute price data from 2002 - 2007

Announcements Produce Price Index (PPI) Consumer Price Index (CPI) Durable Goods (D) Industrial Production (I) Retail Sales (R) Average Work Week (AWW) Unemployment Rate (UR) Hourly Earnings (HE) Nonfarm Payrolls (NP) Capacity Utilization (CU) Business Inventories (BI) Personal Income (PI)

All announcements occur before market opens

Any days announcements did not occur or data was not available are disregarded

Regression I: Test Different Standardizations Attempt to determine which standardization

value for R was best None, DRV, WRV, MRV

Took the 10:00 return from four primary stocks standardized four different ways Regressed each standardized R against each

announcement individually R1000/ DRVt = βkSk,t + εt

192 different regressions

Regression I Results

None Day Week Month# of significant days 6 3 2 1

AIG PG KFT F# of significant days 1 6 2 2

none Day Week MonthAIG 0.013308 0.008358 0.010758 0.010933KFT 0.012631 0.012569 0.012615 0.013785F 0.016385 0.011692 0.009192 0.009146PG 0.022531 0.018977 0.019331 0.016569total ave 0.016214 0.012899 0.012974 0.012608

Average R2 values

•Found P-values very different for different combinations

•Could have small p-values for PG and KFT, but AIG and F would have high values

•Not very many significant coefficients for any standardization

•Examined highest R2 values but no consistent pattern

•Looked at averages and used the best standardization based on the average

Significant Betas

Regression II: Test Different Return Values Question: Which return should be used as overnight

return measure? Want to account more market adjustment

Assume market will adjust quickly Test 9:35, 9:40, 9:45, 9:50; 9:55, 10:00, 10:10, 10:20, 10:30, 10:40,

10:50, 11:00, 11:10, 11:20, 11:30, 12:30, 3:00 Use later times, 12:30 and 3:00 to show the announcement has had an

effect by then Standardize each return by WRV based on previous regression results

R1000 = log(P1000,t+1) - log(Pclose,t ) R1000/WRV = βkSk,t + εt

Do this regression for each return measure for PG only against each announcement individually

214 regressions

Regression II ResultsRopenpg R940pg R945pg R950pg R955pg R1000pg R1010pg R1020pg

average Rsq 0.016917 0.016583 0.016067 0.01435 0.017908 0.021058 0.017142 0.015017

R1030pg R1040pg R1050pg R1100pg R1110pg R1120pg R1130pg R1230pg R300pgaverage Rsq 0.013592 0.012717 0.013775 0.014342 0.012058 0.012767 0.012325 0.013125 0.01655

•P-values varied depending on the announcement- Some announcements had very high p-values for all returns, some had smaller values- General trend – smaller p-values in morning relative to those in the afternoon-There was not one consistent return with the lowest P-value-Most lowest p-values occurred between 9:35 and 10:00 and only one past 11:00-Focused on returns between 9:35 and 10:00 and used 10:00 because had lowest average R2

•Difficulty: the coefficients would change sign- When regressed against HE, the first two returns had pos coefficients and the rest were negative- Occasionally, just one coefficient would change sign

Regression III: Multivariate Regressions Real Activity Rt = βk(NP) t + βk(R) t + βk(I) t + βk(CU) t + βk(PI) t + εt

PricesRt = βk(CPI) t + βk(PPI) t + εt

InvestmentRt = βk(BI) t + βk(D) t + εt

EmploymentRt = βk(NP) t + βk(HE) t + βk(AWW) t + βk(UR) t + εt

Regression III: Results

PG KFT AIG F HIG ALL AVP CLprice regressionreal activityemploymenyinvestment 0.113 0.0163all 0.0857

PG KFT AIG F HIG ALL AVP CLprice regressionreal activity 0.0307employmentinvestment 0.0941 0.0534 0.0323all 0.002

Regress (F-stats)

Newey - West

•Was hoping to see PG, AVP, CL to have similar significant regressions and AIG, HIG, ALL have similarities

•F-test for all announcement on PG becomes insignificant if take out BI

Extensions

Perform regressions on individual stocks for ALL, AVP, CL, HIG

Add more stocks from similar industries and S&P500 data

In process of determining if response varies with sign

Βk = β0+ β1k,t Sk,t if S<0

= β2+ β3k,t Sk,t if S>0