Ronald Akke
ECON410
Project 1: Announcement Effect Study
The Effect of Initial Unemployment Claims on the S&P500
Introduction
“Unemployment claims came in above 400,000, signaling a continued lackluster economy.”
Statements such as these grace financial headlines each Thursday morning. Reported at 8:30AM by the
U.S. Department of Labor, new initial unemployment claims is a key indicator of the U.S. economy’s
health. Being such, it is important for investors to know the effect of surprise announcements upon
equity prices. Furthermore, traders would like to know whether they could benefit by trading on this
announcement. This paper will answer three questions: how do these unexpected changes in claims
affect the equities market? Do markets react during overnight trading or is there a lagged reaction?
Would the ability to predict unemployment claims lead to abnormal financial returns?
Academics believe that markets are efficient. This paper assumes the same, thus the paper
hypothesizes that all market reaction to initial unemployment claims will take place during after-hours
trading between the close of the S&P 500 on Wednesdays and the open on Thursday. Being such, a
statistically significant relationship should exist between unexpected unemployment claims and
abnormal S&P 500 returns during this time period. Furthermore, assuming market efficiency, a
statistically significant relationship should not exist between unexpected unemployment claims and
abnormal S&P 500 returns between 4PM Wednesday and the market’s close one trading day later. This
should hold true for returns between 4PM Wednesday and 4PM two trading days later. That is:
Ho: β 1 ≠ 0, β2 = 0, β3 = 0
Ha: β1 = 0, β2 ≠ 0, β3 ≠0
This will be tested using 3 separate regressions:
𝑃𝑂𝑝𝑒𝑛 𝑡
(i)
[ln (𝑃
(ii)
𝑃
[ln (𝑃 𝐶𝑙𝑜𝑠𝑒 𝑡 ) −
𝐶𝑙𝑜𝑠𝑒 𝑡−1
(iii)
𝑃
[ln (𝑃 𝐶𝑙𝑜𝑠𝑒 𝑡 ) −
𝐶𝑙𝑜𝑠𝑒 𝑡−1
𝐶𝑙𝑜𝑠𝑒 𝑡−1
)−
POpen i
)
PClose i−1
∑322
𝑖=1 ln(
322
] = 𝛽̂0 + 𝛽̂1 [𝐴𝑛𝑛𝑜𝑢𝑛𝑐𝑒𝑑 − 4 𝑊𝑒𝑒𝑘 𝑀𝑜𝑣𝑖𝑛𝑔 𝐴𝑣𝑒𝑟𝑎𝑔𝑒]
PClose i
)
PClose i−1
∑322
𝑖=1 ln(
322
] = 𝛽̂0 + 𝛽̂1 [𝐴𝑛𝑛𝑜𝑢𝑛𝑐𝑒𝑑 − 4 𝑊𝑒𝑒𝑘 𝑀𝑜𝑣𝑖𝑛𝑔 𝐴𝑣𝑒𝑟𝑎𝑔𝑒]
PClose i+1
∑322
)
𝑖=1 ln(
PClose i−1
322
] = 𝛽̂0 + 𝛽̂1 [𝐴𝑛𝑛𝑜𝑢𝑛𝑐𝑒𝑑 − 4 𝑊𝑒𝑒𝑘 𝑀𝑜𝑣𝑖𝑛𝑔 𝐴𝑣𝑒𝑟𝑎𝑔𝑒]
Where: P: Price of S&P 500, t is the day of announcement, i is a trading day during this period,
Announced: announced amount of seasonally adjusted unemployment
4 Week Moving Average: Revised 4 week moving average based upon previous 4 weeks of data
Data
Data was obtained and manipulated using various sources. S&P pricing data were obtained
using Bloomberg and initial unemployment claims data were obtained using historical announcements
on the U.S. Department of Labor website. These data were then transferred into Microsoft Excel where
they were manipulated and finally transferred to Stata where regressions were run. No outliers were
discarded because all data points are important to the paper’s economic significance argument.
First, new initial unemployment claims data was obtained from the historic announcements
from the U.S. Department of Labor for the period spanning July 1, 2010 until October 6, 2011. Each
datum point includes the date of the announcement, the announced level of claims, and the previous
four weeks’ revised moving average (i.e. not including the date of the announcement). The moving
average was used as an objective proxy to measure expected unemployment claims. It works well
because it is free from subjectivity and can be easily utilized by any market participant. The expected
unemployment was subtracted from new initial unemployment claims in order to obtain the unexpected
news event (“news”). “News,” in turn, acted as the independent variable for all three regressions.
The first regression’s methodology is best explained using the example of Thursday, February
10 , 2011. 383,000 new claims were announced that day. The previous four announcement days (Feb
3rd, Jan 27th, Jan 20th and Jan 13th) had, after revision, averaged 431,500 claims. The “news” event was
thus -48,500 claims (383,000 – 431,500). Next, the S&P 500’s opening price at 9:30am, February 10th
was 1318.13. The S&P 500’s closing price at 4:00pm on February 9th was 1320.88. The after-hours rate of
return (Wednesday afternoon until Thursday morning) was thus calculated ln(1318.13/1320.88) to yield
a return of -0.21%. But this needed to be converted into an abnormal return. Thus, the average afterhours return for every day of the week from July 1st, 2010 until October 7, 2011 (N=322) was computed
to be 0.00%. The abnormal return (“Ab_OpT) was thus -0.21% (-0.21%-0.00%). This abnormal return
acted as the dependent variable. A regression was then run to compare “news” effect on abnormal after
hours returns.
th
The second and third regressions were tested in a similar manner. Drawing from the previous
example, the second regression merely replaced the opening price on February 10th with the closing
price on February 10th, and replaced the average-after hours return with the average one day closing
price return. The third regression replaced the opening price with the closing price on February 11th, and
replaced the average after-hours return with the average two day return. These regressions were used
to test for the existence of asset pricing lag.
Results
The regression results are found on the accompanying Stata sheet. Key results are as follows:


Regression one showed a statistically significant result such that news had a negative effect
upon market returns. With P=0.005, this value is statistically significant at a 5% cut-off value.
Further, R2 = 11.59%.
Regressions two and three did not show statistically significant results at a 5% cut-off value.
The result from regression one is logically sound. A numerically positive “news” event means
initial unemployment claims were announced above their expected value. This is a poor signal for
the economy, so markets sell off. There are two important caveats to this. First, the variation in
news only explains 11.6% of the variation in abnormal returns. Thus, there are much larger forces at
work for which this model does not account. The second important result is that β1= -0.0000000316.
That is, every time initial claims comes in at 10,000 claims greater than expected, investors should
expect an abnormal return of -0.03%. Because of this small return and the low value of R2, although
these results are statistically significant, they are not economically significant. Trading costs would
destroy any potential profit from a trading strategy that could predict initial claims.
The results from regressions two and three are also as expected. At a cut-off value of 5%, there
is not a statistically significant relationship between unexpected initial claims and lagged, abnormal
stock market returns. Thus, all of this paper’s initial hypotheses hold and the author fails to reject
the null hypothesis.
It is important to remember that this paper is only as good as the data it contains. These results
can only be generalized over economic time spans similar to the past seventeen months. Further,
with such a low R2, systemic underlying events may strongly affect the variation in abnormal returns.
To better generalize these results, many more observations would need to be taken across decades
of data. Furthermore, the use of the four-week moving average as the estimator of initial claims is
questionable. Using a different estimator would obtain different results, and thus could effect the
statistical significance of the paper’s results. Furthermore, a moving average lessens the variability in
the dependent variable, which may cause the R2 to be greater than it should be. As in all research,
these results must be taken with a grain of salt.
This paper set out to answer three important questions. First, the paper found that when there
is an unexpected increase in filed unemployment claims, markets tend to sell off more than usual.
Second, based upon the statistical significant, the paper found that since July 1st, 2010 markets have
fully reacted to initial unemployment “news” events overnight with no lagged reaction. Finally,
although statistically significant, the limits to arbitrage would destroy any profit making ability if one
could predict initial unemployment claims. Thus, the market reacts quickly and efficiently to news
events, leaving no room for arbitrage opportunities. The efficient market hypothesis seems to hold.
log:
opened on:
/home/3170a/usra/00/00213/Desktop/410log3reg.log
9 Oct 2011, 14:53:49
REGRESSION ONE
. reg ab_opt news
Source |
SS
df
MS
-------------+-----------------------------Model | .000025258
1 .000025258
Residual | .000192577
65 2.9627e-06
-------------+-----------------------------Total | .000217835
66 3.3005e-06
Number of obs
F( 1,
65)
Prob > F
R-squared
Adj R-squared
Root MSE
=
=
=
=
=
=
67
8.53
0.0048
0.1159
0.1023
.00172
-----------------------------------------------------------------------------ab_opt |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------news | -3.06e-08
1.05e-08
-2.92
0.005
-5.16e-08
-9.69e-09
_cons | -.0001602
.0002181
-0.73
0.465
-.0005958
.0002754
-----------------------------------------------------------------------------REGRESSION TWO
. reg ab_clt news
Source |
SS
df
MS
-------------+-----------------------------Model |
.00040344
1
.00040344
Residual | .012223977
65 .000188061
-------------+-----------------------------Total | .012627417
66 .000191324
Number of obs
F( 1,
65)
Prob > F
R-squared
Adj R-squared
Root MSE
=
=
=
=
=
=
67
2.15
0.1478
0.0319
0.0171
.01371
-----------------------------------------------------------------------------ab_clt |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------news | -1.22e-07
8.36e-08
-1.46
0.148
-2.90e-07
4.45e-08
_cons | -.0002984
.0017378
-0.17
0.864
-.003769
.0031722
-----------------------------------------------------------------------------REGRESSION THREE
. reg ab_cltp1 news
Source |
SS
df
MS
-------------+-----------------------------Model | .000183384
1 .000183384
Residual | .020432987
65 .000314354
-------------+-----------------------------Total | .020616371
66 .000312369
Number of obs
F( 1,
65)
Prob > F
R-squared
Adj R-squared
Root MSE
=
67
=
0.58
= 0.4478
= 0.0089
= -0.0064
= .01773
-----------------------------------------------------------------------------ab_cltp1 |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------news | -8.26e-08
1.08e-07
-0.76
0.448
-2.99e-07
1.33e-07
_cons | -.0009349
.0022468
-0.42
0.679
-.005422
.0035522
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name: <unnamed>
log: /home/3170a/usra/00/00213/Desktop/410log3reg.log
log type: text
closed on:
9 Oct 2011, 14:56:30
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