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Stock Market Reaction to Good and Bad Inflation News

Johan KnifSwedish School of Economics and Business Administration (Hanken), Finland

James Kolari*

Texas A&M University, USA

Seppo PynnnenUniversity of Vaasa, Finland

Current draft: January 11, 2006

*Correspondence: Professor James Kolari, Texas A&M University, Mays Business School,

Finance Department, College Station, TX 77843-4218.

Email address:[email protected]

Office phone: 979-845-4803 Fax: 979-845-3884

The authors gratefully acknowledge financial support from the Center for International Business

Studies, Mays Business School, Texas A&M University, Hanken Swedish School of Economics

and Business Administration, and Academy of Finland. Helpful comments were received from

seminar participants at the 2003 European Finance Association conference in Helsinki and 2003

Financial Management Association conference in Denver. Also, we have benefited from

comments by Tarun Chordia, Avinidhar Subrahmanyam, David Chapman, Robert Dittmar, Sorin

Sorescu, Eric Kelley, Jaap Bos, Schmuel Baruch, Rune Hglund, Markus Jntti, Gunnar

Rosenqvist, and Paulo Renato Soares Terra.
mailto:[email protected]:[email protected]

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Abstract

This paper shows that differentiating between good and bad inflation news is important to

understanding how inflation impacts stock market returns. Summing positive and negative inflation

shocks as in previous studies tends to wash out or mute the effects of inflation news on stock

returns. We also find that, depending on the economic state, positive and negative inflation shocks

can produce a variety of stock market reactions. We conclude that inflations effect on stock

returns is conditional on whether investors perceive inflation shocks as good or bad news in

different economic states.

JEL Classification: E31, G00, G14

Key Words: Inflation; Stock Market; Event Study

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I. Introduction

Hess and Lee (1999) show that the relation between stock returns and inflation in the

United States, United Kingdom, Japan, and Germany depends on whether inflation arises from

supply or demand shocks. Using quarterly data and time series decomposition empirical

methods, they find that demand disturbances associated with monetary shocks increase inflation

and stock returns. Conversely, supply disturbances due to real output shocks lower inflation and

raise stock returns. Thus, positive versus negative stock-return/inflation relationships are

explained by positive and negative inflation shocks, respectively.

Related work by McQueen and Roley (1993) finds that stronger relationships between

stock prices and macroeconomic news occur at different stages of the business cycle. For

example, defining economic output as high, medium, or low, aggregate U.S. stock returns

significantly respond negatively to consumer price index (CPI) shocks in the medium state and

to producer price index (PPI) shocks in the high state. Relevant to the present study, the authors

posit that positive surprises in industrial production would be perceived by stock investors as

good news during the Great Depression but bad news in 1969 when output and employment

were high. However, their empirical analyses do not directly test for good news versus bad

news effects of positive or negative macroeconomic surprises on stock returns.

This paper seeks to extend the aforementioned studies by using event study methods to

further investigate the impact of positive (demand driven) and negative (supply driven) inflation

shocks on U.S. aggregate stock returns. We contribute new empirical evidence on the stock-

return/inflation relation by differentiating between good and bad news associated with positive

and negative inflation shocks. Following McQueen and Roleys logic, a positive inflation shock

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can have different meaning to investors depending on the state of the economy. If the economy

is in a recession, and news of higher than expected inflation is released, we hypothesize that

stock investors would interpret this as good news (e.g., higher prices due to increasing consumer

confidence and demand for goods and services). By contrast, a positive inflation shock in an

expansionary economy is hypothesized to be perceived as bad news by investors (e.g., an

increased likelihood of potential central bank intervention to increase interest rates, decrease

inflation rates, and slow the economy). Precedent for differential market responses to good and

bad macroeconomic news can be found in Boyd, Hu, and Jagannathan (2003), who observe that

rising unemployment is perceived as good news by stock investors during economic expansions

but bad news during economic contractions. Also, theoretical work by Veronesi (1999) argues

that investors react differently to bad news in good times as opposed to good news in bad times.

Thus, theory and evidence supports that notion of differential market responses to good and bad

news.

Using the consumer price index (CPI) and producer price index (PPI), we examine in this

study the impact of good and bad inflation news announcements on aggregate U.S. stock returns.

In general, our results indicate that differentiating between good and bad news with respect to

inflation shocks is important to more fully understanding how inflation affects stock returns.

For example, we find that positive and negative inflation shocks can have fairly strong cumulative

effects on aggregate stock returns in the weeks surrounding inflation news events. However,

summing positive and negative inflation shocks as in previous studies tends to wash out or mute the

effects of inflation news on stock returns. Also, given the economic state, positive and negative

inflation shocks can produce a variety of stock market reactions due to good and bad news

effects. For example, similar to Hess and Lee, we find that inflation shocks and stock returns are

not always inversely related as previously believed. We conclude that, consistent with our

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research hypotheses for the most part, inflations effect on stock returns is conditional on whether

investors perceive positive or negative inflation shocks as good or bad news in different economic

states.

Section II overviews related literature. Section III gives definitions of inflation news and

economic states. Section IV provides details of our event study methodology. Section V reports

the empirical results. Section VI contains conclusions and implications.

II. Previous Event Studies of Inflation News

Despite being the subject of intensive empirical and theoretical research for many years,

the relationship between inflation news and stock market returns remains a controversial issue

(Flannery and Protopapadakis (2002)). Many regression studies1 conclude that there is a

negative short-run relationship between inflation and common stock returns. Because these

results contradict the well-known Fisher (1930) effect, a variety of theoretical arguments2 have

been proposed to explain this anomalous result.

Early event studies designed to more closely examine the issue of stock price reactions to

inflation news generally found that the negative relationship is so weak as to be economically

insignificant. For example, Castanias (1979) reports that actual inflation data series did not

affect the S&P 500 index around announcement days. Also, Schwert (1981) examines the S&P

500s daily reaction to a measure of unexpected inflation and finds only weak market responses.

Additionally, he finds that aggregate stock market returns appear to anticipate inflation news

announcements and that the post-event market reaction is relatively slow requiring numerous

days to fully respond. In an attempt to explain weak market response to inflation, he cites

Famas (1981) argument that the high correlation between inflation and other macroeconomic

variables obscures empirical efforts to discern the relationship between stock returns and

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inflation. Pearce and Roley (1985) use survey data to estimate market expectations but again

find little or no evidence of daily stock market reaction to either anticipated or unanticipated

inflation news.

By contrast, later event studies have found significant relationships between stock returns

and inflation news. For example, extending event studies to hourly stock returns, Jain (1988)

finds a significant stock market response to CPI news but not PPI news. He attributes the former

finding to the lower variance of hourly data compared to daily data and comments that most of

the information contained in inflation news is fully captured within a one-hour timeframe.

Another study by McQueen and Roley (1993) finds that, consistent with Famas proxy

argument, while the S&P 500 Index generally did not respond to a variety of macroeconomic

information, a larger conditional response occurs with respect to different economic states [see

also Boudoukh, Richardson, and Whitelaw (1994)]. As mentioned above, their study reveals

significant, negative one-day stock market responses to consumer price (CPI) inflation shocks in

medium economic states but not high and low economic states. One-day producer price (PPI)

inflation shocks are significant in the high economic state but not other states. These results

suggest that inflation shocks have differential effects in different economic states.

Finally, Adams, McQueen, and Wood (2004) extend Jains daily analyses by using high

frequency daily data and find that both PPI and CPI news significantly affect stock returns,

especially those of large firms. For example, a 1 percentage point PPI (CPI) surprise is

associated with a 0.70 percent (-1.29 percent) stock return response among large firms within

15 minutes of the markets opening after morning inflation announcements. They find that most

of the stock market reaction takes place within 10-20 minutes. Also, stronger stock market

responses occur in the high economic state (as opposed to normal and low states) and with

respect to positive (as opposed to negative) inflation shocks.3 In the high economic state, for

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example, a 1 percent CPI surprise is associated with a 3.52 percent market reaction within 15

minutes of the markets open among large firms.

This paper contributes to the inflation/stock return literature in two ways. First,

we show that positive and negative inflation news can have different good and bad news effects

on stock market returns. Second, we show that market responses to positive and negative

inflation news can differ across different economic states. When positive and negative shocks

are pooled for any particular economic state, their effects on aggregate stock returns are washed

out or muted and become insignificant for the most part. This result helps to explain the

disparity between regression studies (see footnote 1) finding an inverse stock-return/inflation

relation and event studies reporting a weak or no significant relationship.

III. Inflation News and Economic States

Ederington and Lee (1993) point out that inflation news is closely watched and regularly

forecasted by market participants. Inflation news released in financial media regularly captures

headlines due to its important implications fornumerous other macroeconomic variables, including

monetary policy actions with respect to interest rates and money supply, economic growth, cost of

debt and equity capital, etc. Private forecasts of macroeconomic variables are produced by virtually

every firm investing funds in the stock market e.g., securities firms typically hire professional

forecasters to provide in-house future estimates of key economic indicators, many firms sell

econometric modeling services, and most large financial organizations employ economists to

predict economic developments. In this regard, due to the large number of private inflation

forecasters, some organizations develop and publish consensus forecast information (e.g., the

Federal Reserve Bank of Philadelphias Survey of Professional Forecasters and Livingston Survey,

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Council of Economic Advisors, Congressional Budget Office, Blue Chip Economic Indicators,

etc.).

We measure monthly unexpected inflation news (or shocks) as the difference between

median predicted forecasts of both CPI(U) and PPI published in MMS Internationals (a

subsidiary of Standard & Poor's) Weekly Economic Survey and actual CPI(U) and PPI compiled

by the U.S. Bureau of Labor Statistics.4 Survey forecasts are normally released four or five days

before the release of actual data by the BLS. It is based on the forecast of the upcoming

monthly inflation rate by business economists at brokerage houses, commercial banks, and some

private consulting firms and major business schools in the U.S. Actual PPI is announced in the

middle of each month and actual CPI is announced about one week later. Since the forecast

series starts in January 1980, our sample period begins in this month and ends in September

2004.McQueen and Roley (1993) classify economic states in terms of quartile bands around

the trend of the (log) level of industrial production to delineate low, medium, and high periods

of economic activity. They also employ manufacturing capacity utilization and unemployment

to classify economic states. In the present paper we define economic states in two ways. First,

similar to McQueen and Roley, we use the level of U.S. manufacturing capacity utilization to

define economic states. However, we do not use industrial production as in their study because

it is an I(1) series with drift, whereas manufacturing capacity utilization is bounded between 0

and 100. Regarding industrial production, this implies that bounds around the trend will be

sensitive to the specific sample period. For manufacturing capacity utilization the bounds will

be more stable because the series is mean reverting.5 Second, extending previous work, we also

classify the economy as rising, stable, or slowing. In this way we capture the direction of the

economy and discern between an economy that is (for example) entering versus exiting a recession.

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Changes (levels) in manufacturing capacity utilization are used to define dynamic (static) economic

states. The rationale for using both approaches is that investors may not only be concerned about

the overall level of economic conditions but changes in economic conditions too.

Figure 1 shows U.S. seasonally adjusted monthly manufacturing capacity utilization for

the period January 1980 to September 2004. We use changes (levels) between high and low

quartile bands to represent stable (normal) economic activity, above the upper quartile bank to

define rising (high) economic activity, and below the lower quartile to define slowing (low)

economic activity. The shaded areas are periods classified as recession by the National Bureau

of Economic Research (NBER). Figure 1 also displays changes in U.S. manufacturing capacity

utilization over the sample period. As we will see, the CPI results are virtually unchanged when

economic states are defined in terms of either levels or changes in manufacturing capacity

utilization. That is, investors interpret good and bad inflation news in the same way for the most

part in both static and dynamic economic states. However, the PPI results are more significant

for changes than for levels of economic states. To conserve space we focus primarily on the

results for changing economic states and discuss results for levels of economic states as

appropriate. Complete results for levels of economic states are available upon request from the

authors.

Table 1 reports sample statistics for S&P 500 monthly rates of return for the entire

sample period as well as rising, stable, and slowing economic states. The October crash month

of 1987 when the S&P 500 dropped 24.5 percent is excluded. The overall, rising, stable, and

slowing economy average monthly market stock returns (and standard deviations) are 0.82

percent (4.41 percent), 0.23 percent (4.79 percent), 0.84 percent (3.96 percent), and 1.42 percent

(4.87 percent), respectively. Notice that monthly volatility is about the same in all economic

states and that the kurtosis statistics are not significant. Skewness measures indicate that the

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distributions are fairly symmetric. Average monthly inflation rates, both actual and predicted,

are almost the same for all economic states and overall at around 0.31 (0.20) percent monthly for

CPI (PPI). Also, during our sample period, the estimated correlation coefficient between CPI

and PPI shocks was only 0.044. Thus, we infer that CPI and PPI shocks contain different

inflation information and should be separately examined.

IV. Methodology

We follow Schwerts (1981) regression event study approach but modify it to control for

other macroeconomic news announcements when measuring the cumulative effects of inflation

shocks on stock market returns. Importantly, this methodology allows us to measure inflation

effects on stock returns conditional on other macroeconomic events. By taking into account

other contemporaneously correlated macro news events, we mitigate the proxy problem

observed by Fama and recognized by later researchers.

As discussed above, unexpected inflation is defined as the difference between the actual

and the median predictions of CPI(U) and PPI changes as published by MMS Internationals

weekly Economic Survey. The October 23, 1987 CPI announcement was excluded due to the

strong outlier effect of the market crash. Table 2 gives the numbers of positive, zero, and

negative inflation shock in terms of CPI(U) and PPI during rising, stable, and slowing economic

states. Using Schwerts approach, we estimate the regression

t

k

n

tk

n

k

k

p

tk

p

k

p

j

tjjt uuXR

++++=

= == ==

3

1

10

10

,,

3

1

10

10

,,

1

, )()( , (1)

where )ln(ln100 1= itt IIR , tI = the S&P 500 value on day t , tjX , are control

variables including daily dummies (if needed) and a dummy variable for zero inflation shock

(i.e., when there is an announcement but no shock), )0,max()(,pred

t

actual

t

p

tk CPICPIu ++ = is

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thepositive inflation shock in economic state k(k=1 rising economy, k=2 stable economy, and

k=3 slowing economy), and )0,min()(,pred

t

actual

t

n

tk CPICPIu ++ = is the negative inflation

shock in economic state k.

The regression coefficients measure the marginal effect of an inflation shock such that

a 1 percent shock has a magnitude of percent change in the stock return. Analogous to

traditional event study Abnormal Returns (AR), s can be interpreted to measure the Abnormal

Marginal Return (AMR). As in traditional event studies, summing upAMRs gives Cumulative

Abnormal Marginal Returns (CAMR) that measure the cumulative return effect for a 1 percent

inflation shock.

It is well known that stock returns exhibit modest autocorrelation and (asymmetric)

conditional heteroscedasticity (for which an asymmetric GARCH(1,1) process has proven to be a

useful description).6 Since utilizing the conditional heteroscedasticity in the estimation increases

the accuracy of the estimates over traditional least squares, we model the residual term in

equation (1) as:

[ ] 112

12

2

111

1

Var

,

+++==

+=

ttttttt

ttt

hdwwwh

w

, (2)

where, conditional on information up to time t-1, ),0(~ tt hNw with th the GARCH(1,1)

variance, and 11 =td if 01

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In stable or medium economic times we expect that the results would parallel those for good

economic times, as normalcy itself is likely to be viewed by investors as more closely associated

with good than bad economic times. While peripheral to the main purpose of this paper, our

empirical results may provide some insight into Veronesis (1999) theoretical hypothesis that

investors overreact to bad news in good times (i.e., positive inflation shocks in high or rising

economic states) but underreact to good news in bad times (i.e., positive inflation shocks in low

or declining economic states).

V. Empirical Results

A. CPI Inflation News

Tables 35 report the daily CPI event study results using a 21-day window for rising,

stable, and slowing economic change periods, respectively. The CAMR results in each economic

state are broken down by positive and negative inflation shocks as discussed in the previous

section. Pooled results for both positive and negative inflation shocks are reported also. Figure

2 summarizes the information in Tables 35 for the changing economic states in a series of

CAMR graphs. Figure 3 shows the results using high, normal, and low economic states are

almost identical to those using changing economic states. We infer that investors respond

similarly to good and bad inflation news across economic states defined in terms of both levels

and changes in manufacturing activity.

Table 6 provides the results for the macroeconomic control variables. Here we see that

only day 0 PPI shocks are significant at the five percent level or higher.

Rising and High Economic States. Table 3 shows that, in periods of rising economic

activity, positive economic shocks significantly affect aggregate stock returns but not negative

inflation shocks. Positive economic shocks have a negative and significant (at the 10 percent

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level) CAMR equal to 6.08 on day 0. This result means that a 1.0 percent unexpected increase

in inflation is associated with a 6.08 percent decline in stock market returns. After the event day

CAMRs are significant (at the 5 and 10 percent levels) for days +1 to +10, with a maximum of

10.11 on day +8. Panel A of Figure 2 illustrates these findings. Note that the results shown in

panel A of Figure 3 for the high economic state measured in levels (e.g., the CAMR equals -8.91

on day +10) are virtually the same as those for the rising economic state (e.g., the CAMR equals

-9.16 on day +10). Thus, our results for both high and rising economic states are almost

identical. Importantly, the results strongly confirms our hypothesis that positive inflation shocks

are bad news for stocks in good economic times.

With respect to negative inflation shocks, notice that none of the results reported in Table

3 are significant. Figures 2 and 3 visually confirm this general finding. Thus, these findings do

not support our research hypothesis that negative inflation shocks are perceived by investors as

good news in good economic times.

It is worthwhile to evaluate our results in the context of previous CPI/stock returns event

studies. Schwert reports a CAMR for days -9 to +5 of 2.38. He concludes that the reaction of

stock returns to unexpected inflation news is weak and, as a partial explanation, cites Famas

argument that unexpected inflation shocks are contemporaneously correlated in real

macroeconomic shocks (e.g., capital expenditures and gross national product). For positive

inflation shocks we obtain a CAMR for days -9 to +5 of -8.23, which is considerably larger than

Schwert. McQueen and Roley measure one-day stock market responses to inflation news events

and find a 1.0 percent shock in inflation is associated with a decline in stock prices of about 0.78

percent. This result is much smaller in magnitude than Schwerts and therefore suggests little or

no relationship between stock returns and inflation shocks. By comparison, for positive inflation

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shocks we find an aggregate stock market response on day 0 of -1.70, which is larger than

McQueen and Roley.

One possible reason for our finding of relatively larger market responses to inflation

shocks (besides a different sample period) is that our methodology controls for other

macroeconomic news events and therefore diminishes the proxy problem observed by Fama,

which tends to obscure the relationship between inflation news and stock returns. However, our

results were little changed when we re-ran the analyses by dropping the other macroeconomic

news shocks from our regression model (see Table 6). Little or no change in results was found

in other economic states also.

Another potential explanation for the larger market response to inflation shocks is that

we distinguish between positive and negative shocks, rather than lumping them together as in

previous studies. As shown in Table 3, when we ran pooled positive and negative inflation

shocks in the rising economic state, no significant stock market response measured in CAMRs is

found. Hence, negative stock market responses to positive inflation shocks are washed out by

combining them with negative inflation shocks. Although not reported, the results for the high-

level economic state are muted also namely, summing results for positive and negative shocks

yields a negative and significant CAMR of -2.27 for day 0 but other event window CAMRs are

insignificant. Results for sub-windows at the bottom of Table 3 also reveal that pooling positive

and negative inflation shocks mutes the relatively strong impact of positive inflation shocks on

stock returns. In general, we infer that these results help to explain the relatively mild or

absence of stock market responses to inflation shocks reported in early event studies.

Lastly, casual inspection of panel A of Figure 2 as well as panel A of Figure 3 confirm

Schwerts finding of market anticipation and post-event market reaction to inflation news. It is

obvious that market reaction to inflation news occurs over a number of days, rather than only on

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day 0 or +1. In the event windows shown at the bottom of Table 3 for the rising economy,

significant (at the five percent level) negative CAMRs occur in the -5 to -1 event window and the

1 to +1 event window. Although further significant market reaction is not found in post-event

windows +1 to +5 and +6 to +10, aggregate stock returns drift slowly lower during this 10-day

period. We infer that, rather than focusing on the traditional narrow event windows of day 0 or

days 0, +1, which is popular in many event studies of financial announcements, the cumulative

market response more fully captures the effect of inflation shocks on aggregate stock returns. In

this respect, our results in Table 3 show that aggregate stock returns can cumulatively fall as

much as 10 percent on days -5 to +10 in response to a 1 percent positive inflation shock.

Clearly, positive inflation shocks in economic expansions are perceived to be bad news by

investors.

Stable and Normal Economic States. Table 4 reports the results for positive and

negative inflation shocks in the stable economic state. Here we see that positive shocks

negatively impact stock returns, with a few statistically significant (at the 10 percent level)

cumulative shock effects on days +3 and +9. For negative shocks, significant (at the 5 and 10

percent levels) CAMRs are found for days +4 to +10, with a maximum CAMR of 6.34 on day

+8. Results for the sub-windows at the bottom of Table 4 confirm a negative market response to

both positive and negative inflation shocks. Panel B of Figure 2 shows that positive and

negative inflation shocks have almost identical effects on aggregate stock returns in the stable

economic state. Thus, as expected, positive inflation shocks are viewed as bad news by

investors in times of economic normalcy, which is similar to the results for rising and high

economic states. However, contrary to our research hypotheses, negative inflation shocks in

stable and normal economic states are perceived by investors as bad news for stocks, as opposed

to good news.

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An interesting finding here is that inflation shocks are not necessarily inversely related to

stock returns (i.e., negative inflation shocks are associated with a decline in stock market

returns), which is contrary to the common finding of an inverse relationship between stock

returns and inflation shocks reported in regression studies (see footnote 1). Similar results are

obtained for the normal-level economic state in panel B of Figure 3. Apparently, in the stable or

normal economic state, any CPI surprise may be viewed as bad news by investors (e.g., investors

interpret higher than expected inflation may trigger central bank initiatives to increase interest

rates and slow inflation, while lower than expected inflation may be interpreted as a signal that

consumer spending is falling which will slow the economy). Upon re-running the analyses by

pooling positive and negative inflation shocks, Table 4 shows that none of the CAMRs are

significant. Although not reported (but available upon request), this finding is confirmed for

normal-level economic states also.

Slowing or Low Economic States. Table 5 gives the results for the slowing economic

state. None of the CAMRs are significant in this economic state. Negative shocks appear to be

good news for the stock market, as CAMRs change from negative prior to day 0 to positive

thereafter (see panel C of Figure 2). For negative shocks a maximum CAMR of 5.59 is obtained

on day +10 but it is not statistically significant. However, in the sub-window for days -1, +1, the

CAMR is 3.64, which is significant at the five percent level. The results for the low-level

economic state also yield a positive stock market response to negative shocks, with a significant

CAMR on day 0 equal to 1.34. Thus, contrary to our research hypothesis, negative inflation

shocks in bad economic times are perceived by investors as good news. One possible

interpretation is that lower goods and services prices may help to stimulate consumer demand

and, subsequently, stimulate economic recovery. We infer that mild market responses to

inflation shocks occur in slowing or low economic states.

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B. PPI Inflation News

Aggregate stock return reactions to PPI inflation shocks were all insignificant when the

levels of economic states were used. For changes in economic states, we did find some

significant results for PPI shocks. Thus, to conserve space we report the results for changing

economic states. Figure 4 illustrates the CAMR results in different economic states and for

positive and negative inflation shocks for changes in economic states. Tables 7-9 report CAMRs

and test statistics for rising, stable, and slowing economic states, respectively. Table 10 reports

the results for the macroeconomic control variables. Similar to Table 6s results for CPI

inflation news, only day 0 CPI shocks are significant at the five percent level or higher.

Summarizing the results in Tables 7-9, neither positive nor negative inflation shocks are

significant in the rising economic state. By contrast, recall that CPI positive shocks in the rising

economic state were associated with large, negative stock return responses. In the stable

economic state, positive PPI shocks appear to be anticipated as good news, rather than bad news

as in the case of positive CIP shocks. Table 8 shows that CAMRs are significant for days -7 to

+1, as well as on days +3 and +5. It is obvious that PPI and CPI shocks have different inflation

information content in terms of their effects on stock returns. Like our finding of a negative

market response to negative CPI shocks in stable economic states, the positive market response

to positive PPI shocks in the stable economic state contradicts the common notion that inflation

shocks are inversely related to stock returns. Finally, in the slowing economic state negative PPI

shocks are good news, as Table 9 shows that CAMRs are positive and significant on days -1 to

+10, which is similar to the market reaction to negative CPI shocks. Overall, the PPI results are

normally different from those for CPI and tend to be somewhat less pervasive (i.e., compared to

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the CPI results, we find no significant PPI results for positive shocks in the rising economic state

and negative shocks in the stable economic state).

Thus, even though PPI data is announced one week before CPI data, investors appear to

gain different information from wholesale versus retail price news. And, investors appear to be

somewhat more concerned about changes in the final prices of goods and services rather than

producer input prices. Similarly, Adams, McQueen, and Wood find smaller (but more

significant) stock market reactions to PPI than CPI shocks using intra-day data.

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C. Information Uncertainty and Inflation Shocks

Slow, large market reactions to news events have been the subject of considerable

research in behavioral finance (e.g., see Chan, Jegadeesh, and Lakonishok (1996), Barberis,

Shleifer, and Vishny *(1998), Daniel, Hirshleifer, and Subrahmanyam (1998), and others).

Theoretical work by Veronesi (1999) suggests that investor beliefs change slowly over time in

response to new information that can increase uncertainty about the true economic state. In this

regard, recent empirical work by Zhang (2006) finds that price continuation following the

release of new public information increases with information uncertainty. He interprets these

findings to mean that the degree of incomplete market reaction to new public information is

positively associated with the level of information uncertainty.

In the present context it is possible that inflation shocks are associated with increased

investor uncertainty about the posterior probability of the economic state. For example, news of

higher than expected CPI in a rising economic state (i.e., bad news in good times) decreased

stock returns considerably over a two-week period of time, due in all likelihood to increased

investor concern about a potential economic slowdown (as the central bank could be expected to

raise interest rates to control rising inflation). Does this uncertainty manifest itself in the slow

market responses as well as differential responses depending on the economic state or state,

direction of the inflation shock, and consumer versus producer price inflation shock? In this

regard, we find that negative CPI and PPI shocks are associated with higher stock returns (i.e.,

good news in bad times). Interestingly, this evidence can be tentatively interpreted as consistent

with Veronesis hypothesis of overreaction to bad news in good times and underreaction to good

news in bad times that is, we find a stronger market reaction to bad news in good times than

good news in bad times. This potential explanation for our findings is beyond the scope of the

present work but is recommended for further study in future research.

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VI. Conclusions and Implications

Consistent with Hess and Lee (1999), this study finds that distinguishing between positive

and negative inflation shocks is important to understanding their impact on stock returns.

Extending their empirical work as well as research by McQueen and Roley (1993) to good and bad

inflation news, we find that inflation shocks can have relatively large cumulative effects on

aggregate stock returns depending both on the economic state and on whether inflation

announcements are viewed as good or bad news by investors. For example, in the rising economic

state a 1 percent positive (negative) CPI shock in the rising economic state is associated with about

a 10 percent decline (no significant decline) in aggregate stock returns within a two-week event

window. Like Schwert, we find that CPI shocks are anticipated by investors and have slow post-

event effects on stock returns that continue for a week or more. Importantly, when positive and

negative shocks are pooled across economic states, their effects on aggregate stock returns are

washed out or muted and become insignificant. This result helps to explain the disparity

between regression studies that typically found an inverse stock-return/inflation relation and

event studies reporting a weak or no significant relationship.

Similar to Hess and Lee, we find that inflation shocks can have a variety of effects on

aggregate stock returns, rather than only an inverse relation as previously believed. For example,

we find that positive (or negative) inflation shocks can be either positively or negatively related to

stock returns depending on whether they are perceived as good or bad news by investors. In stable

or normal economic states, negative CPI shocks are associated with a decrease (not increase) in

stock market returns, and positive PPI shocks are associated with an increase (not decrease) in stock

market returns. Thus, we conclude that, consistent with our research hypotheses for the most part,

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inflations effect on stock returns is conditional on whether investors perceive positive and negative

inflation shocks as good or bad news in different economic states.

While beyond the scope of the present study, we find evidence that appears to support

Veronesis theoretical hypothesis that investors overreact to bad news in good times and underreact

to good news in bad times. Specifically, we find a strong, negative market reaction over a two-

week period to positive CPI shocks in rising and high economic states (i.e., bad news in good

times), and a significant but less pronounced positive market response to negative CPI and PPI

shocks in declining and low economic states (i.e., good news in bad times). Further research in this

behavioral area of asset pricing is recommended.

Lastly, an important implication of our findings is that the weak effects of other

macroeconomic variables on stock returns reported in previous studies (e.g., see Chen, Roll, and

Ross (1986)) could be due to not distinguishing between good and bad news in different economic

states. Further work is recommended on the impact of other macroeconomic good and bad news

on stock returns, such as Boyd, Hu, and Jagannathans (2003) study of unemployment news.

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Footnotes

1. For example, see Oudet (1973), Branch (1974), Cagan (1974), Lintner (1975), Bodie (1976),

Fama and Schwert (1977), Jaffe and Mandelker (1976), Nelson (1976), Cohn and Lessard

(1981), Schwert (1981, 1990), Geske and Roll (1983), Hardouvelis (1987), Boudoukh and

Richardson (1993), Boudoukh, Richardson, and Whitelaw (1994), Ely and Robinson (1997),

Sharpe (2000), Anari and Kolari (2001), and others.

2. For example, see Nelson (1976), Modigliani and Cohn (1979), Feldstein (1980), Summers

(1981), Fama and Gibbons (1983), Geske and Roll (1983), James, Koreisha, and Partch

(1984), Kaul (1987), Pindyck (1984), Pearce and Roley (1985), Stulz (1986), Boudoukh,

Richardson, and Whitelaw (1994), Thorbecke (1997), and others.

3. Adams et al. sum positive and negative inflation shocks across economic states using high-

frequency, intra-day data. By contrast, in the present study we examine positive and

negative inflation shocks conditional on the economic state on a daily basis. Previous work

by Glosten, Jagannathan, and Runkle (1993) also shows that stock returns are differentially

affected by negative and positive information. Moreover, studies of debt securities (i.e., Li and

Engle (1998) and Christie-David, Chaudhry, and Lindley (2003)) have reported asymmetric

market responses to negative and positive information.

4. Flannery and Protopapadakis (2002) use the same source and methodology for computing

inflation shocks.

5. Flannery and Protopapadakis have criticized the definition of economic states in

McQueen and Roley on the grounds that alternative measures of the macroeconomy (e.g., the

index of industrial production, unemployment rates, an index of help wanted ads in major

newspapers, and the University of Michigans Survey Research Centers Index of Consumer

Sentiment) yielded different definitions of economic states and, subsequently, different

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results in tests of how a wide variety of macroeconomic shocks affected daily stock returns.

Our approach mitigates this ambiguity to some extent. Using daily data, the index of

industrial production and manufacturing capacity utilization give similar definitions of

economic states when using the change in the direction of economic activity.

6. See Bollerslev, Chou, and Kroner (1992) for a review of this literature.

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FIGURE 1

U.S. Seasonally Adjusted Monthly Manufacturing Capacity Utilization

and Changes in Capacity Utilization with Quartile Bands

Seasonally Adjusted US Manufactu

Utilization

75

80

85

90

Month

Utilization(%

Changes of Seasonally Adjus

Manufacturing Capacity Utili

-1

0

1

2

3

Change(%

)

Source: Federal Reserve Board (www.federalreserve.gov ). The sample period covers January 1980 to September 2004. Theshaded periods, January 1980 to November 1980, July 1981 to November 1982, July 1990 to March 1991, and March 2001 to

November 2001 were declared recessions by the NBER (for further details, seewww.nber.org/cycles.html ).

29
http://www.federalreserve.gov/http://www.nber.org/cycles.htmlhttp://www.nber.org/cycles.htmlhttp://www.nber.org/cycles.htmlhttp://www.federalreserve.gov/http://www.nber.org/cycles.html

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FIGURE 2

Cumulative Abnormal Marginal Returns (CAMRs) with Respect to CPI Inflation Shocks:Different Economic States Defined As Changes in Manufacturing Capacity Utilization

Panel A. Positive manufacturing utilization change periods (rising economy)

-4

-2

0

2

4

6

810

-10 -8 -6 -4 -2 0 2

Positive shock

Negative shock

Panel B. Neutral manufacturing utilization change periods (stable economy)

-4

-2

0

2

4

6

8

10

-10 -8 -6 -4 -2 0 2

Positive shock

Negative shock

Panel C. Negative manufacturing utilization change periods (slowing economy)

-4

-2

0

2

4

6

8

10

-10 -8 -6 -4 -2 0 2

Positive shock

Negative shock

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FIGURE 3

Cumulative Abnormal Marginal Returns (CAMRs) with Respect to CPI Inflation Shocks:

Different Economic States Defined As Levels of Manufacturing Capacity Utilization

A. High manufacturing utilization level periods (high economy)

-4

-2

0

2

4

6

8

10

-10 -8 -6 -4 -2 0 2

Positive shock

Negative shock

B. Normal manufacturing utilization level periods (normal economy)

-4

-2

0

2

4

6

8

10

-10 -8 -6 -4 -2 0 2

Positive shock

Negative shock

C. Low manufacturing utilization level periods (low economy)

-4-2

0

2

4

6

8

10

-10 -8 -6 -4 -2 0 2

Positive shock

Negative shock

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FIGURE 4

Cumulative Abnormal Marginal Returns (CAMRs) with Respect to PPI Inflation Shocks:

Different Economic States Defined As Changes in Manufacturing Capacity Utilization

Panel A. Positive manufacturing utilization change periods (rising economy)

-4

-2

0

2

4

6

8

10

-10 -8 -6 -4 -2 0 2

Positive shock

Negative shock

Panel B. Neutral manufacturing utilization change periods (stable economy)

-4

-2

0

2

4

6

8

10

-10 -8 -6 -4 -2 0 2

Positive shock

Negative shock

Panel C. Negative manufacturing utilization change periods (slowing economy)

-4

-2

0

2

4

6

8

10

-10 -8 -6 -4 -2 0 2

Positive shock

Negative shock

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TABLE 1

Monthly Descriptive Statistics for the Whole Sample Period and Economic States

Defined As Rising, Stable, and Slowing U.S. Manufacturing Capacity Utilization

Monthly Monthly CPI Changes Monthly PPI Changes CPI PPI

Panel A. Overall sample S&P500 Actual Predicted Actual Predicted Shock Shock

Mean (%) 0.82 0.31 0.32 0.20 0.27 -0.01 -0.06

t(mean = 0p-value) 0.00 0.00 0.00 0.00 0.00 0.27 0.00

Standard deviation (%) 4.41 0.28 0.22 0.51 0.30 0.15 0.33

Excess kurtosis 3.50 3.07 4.63 2.23 3.27 1.60 2.36

Skewness -0.87 1.16 1.58 -0.01 0.95 0.08 0.06

Minimum (%) -24.54 -0.40 -0.30 -1.90 -0.65 -0.60 -1.30

Maximum (%) 11.88 1.40 1.40 1.80 1.40 0.50 1.20

Observations (months) 296 296 296 296 296 296 296

Panel B. Rising economy

Mean (%) 0.23 0.30 0.31 0.17 0.28 -0.02 -0.11

t(mean = 0p-value) 0.68 0.00 0.00 0.00 0.00 0.28 0.00


Excess kurtosis 9.00 2.52 8.42 2.18 3.82 -0.03 4.44Skewness -1.90 0.88 2.29 0.28 1.26 -0.19 0.66

Minimum (%) -24.54 -0.20 0.10 -1.00 -0.10 -0.40 -1.10

Maximum (%) 10.68 1.00 1.00 1.60 1.20 0.30 1.20


Panel C. Stable economy

Mean (%) 0.84 0.31 0.31 0.22 0.27 -0.01 -0.05

t(mean = 0p-value) 0.01 0.00 0.00 0.00 0.00 0.54 0.04


Excess kurtosis 0.58 3.52 5.05 1.12 3.90 1.61 1.51

Skewness -0.41 1.44 1.68 0.73 1.31 0.53 0.41

Minimum (%) -11.66 -0.30 -0.20 -0.90 -0.50 -0.40 -0.80

Maximum (%) 10.37 1.40 1.40 1.80 1.40 0.50 1.20


Panel D. Slowing economy

Mean (%) 1.42 0.32 0.33 0.20 0.24 -0.01 -0.04

t(mean = 0p-value) 0.02 0.00 0.00 0.01 0.00 0.74 0.40


Excess kurtosis 0.14 1.65 2.17 2.16 1.54 1.72 2.12

Skewness -0.42 0.74 1.14 -0.84 0.49 -0.22 -0.64

Minimum (%) -12.13 -0.40 -0.30 -1.90 -0.65 -0.60 -1.30

Maximum (%) 11.88 1.40 1.30 1.70 1.40 0.50 0.90


Panel E. Correlation of CPI and PPI shocksCorrelationCoefficient t-value p-value

Rising economy -0.072 -1.24 0.216

Stable economy 0.077 1.34 0.182

Slowing economy 0.074 1.27 0.204

Overall 0.044 0.75 0.454Monthly rates of return for the S&P 500 are calculated as 100 times the log price differences. The sample period for theS&P500 covers January, 1980 to September, 2004. The S&P 500 return for the crash month October, 1987 is discarded as anoutlier from the return sample statistics. Rising, stable, and slowing economic activity periods are defined using quartile

bands around the median percent changes in U.S. monthly manufacturing capacity utilization.

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TABLE 2

Number of Inflation Shock Event Months in

Slowing, Stable, and Rising Economic Activity States

Panel A. CPI shocksCPI inflation shocks

Economy Positive ZeroNegativ

e Total

Rising 22 25 28 75Stable 40 50 60 150Slowing 24 21 26 71Total 86 96 114 296

Panel B. PPI shocksPPI inflation shocks

Economy Positive ZeroNegativ

e Total

Rising 17 17 41 75

Stable 53 18 79 150Slowing 27 11 33 71

Total 97 46 153 296

The sample covers the period January 1980 to September 2004. Positive (negative) inflation shocks consist ofactual monthly CPI/PPI changes greater than (less than) zero difference from the median survey forecast. Zeroinflation shock is the case where the prediction equals the actual. The CPI and PPI announcements of October1987 were excluded due to the strong outlier effect of the stock market crash. Rising, stable, and slowingeconomic activity periods are defined using quartile bands around the median percent changes in U.S. monthlymanufacturing capacity utilization.

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TABLE 3

Cumulative Abnormal Marginal Returns (CAMRs) in Response to Different CPI Shocks in

Positive Manufacturing Capacity Utilization Change Periods (Rising Economic State)

Positive inflation shock Negative inflation shockPooled positive and

negative shocks

Day CAMR Std p-value

CAM

R Std p-value CAMR Std p-value-10 -1.03 0.93 0.269 1.65 0.90 0.064 -1.42 0.67 0.034

-9 1.73 1.34 0.199 1.63 1.31 0.214 -0.24 1.00 0.814

-8 1.33 1.92 0.488 0.72 1.52 0.637 0.10 1.24 0.938

-7 1.68 2.18 0.441 -0.04 1.77 0.981 0.68 1.42 0.632

-6 0.72 2.44 0.767 -0.19 1.99 0.925 0.30 1.58 0.851

-5 -1.09 2.70 0.686 -2.05 2.16 0.344 0.60 1.70 0.722

-4 -2.84 3.04 0.350 -2.99 2.40 0.212 0.34 1.87 0.855

-3 -1.58 3.21 0.623 -2.55 2.57 0.321 0.49 2.01 0.808

-2 -2.47 3.38 0.464 -1.52 2.85 0.594 -0.53 2.17 0.808

-1 -4.38 3.50 0.211 -0.35 3.06 0.909 -2.00 2.29 0.381

0 -6.08 3.72 0.102 0.38 3.23 0.906 -3.14 2.40 0.190

1 -7.94 3.84 0.039 -0.44 3.35 0.895 -3.50 2.46 0.155

2 -9.25 3.93 0.019 -0.88 3.54 0.804 -3.79 2.56 0.138

3 -8.06 4.08 0.048 -1.12 3.71 0.763 -3.17 2.69 0.238

4 -9.10 4.20 0.030 -1.56 3.79 0.681 -3.37 2.73 0.217

5 -9.26 4.32 0.032 -1.71 3.95 0.664 -3.36 2.82 0.233

6 -7.75 4.44 0.081 -3.57 4.05 0.379 -1.66 2.87 0.564

7 -8.86 4.60 0.054 -2.41 4.19 0.565 -2.87 2.95 0.331

8 -10.11 4.75 0.033 -2.99 4.23 0.479 -3.08 2.99 0.302

9 -8.45 4.81 0.079 -3.43 4.33 0.428 -2.16 3.05 0.479

10 -9.16 4.91 0.062 -3.07 4.42 0.487 -2.65 3.11 0.395

Sub-windows

-5 -1 -5.10 2.34 0.029 -0.17 2.21 0.940 -2.30 1.64 0.1610 -1.70 1.19 0.154 0.73 0.95 0.441 -1.14 0.75 0.130

-1 +1 -5.46 1.83 0.003 1.08 1.71 0.528 -2.97 1.25 0.017

+1 +5 -3.18 2.16 0.140 -2.10 2.09 0.317 -0.21 1.54 0.890

+6 +10 0.10 2.57 0.970 -1.35 1.80 0.451 0.71 1.56 0.648

The sample covers the period January 1980 to September 2004. Cumulative abnormal marginal returns (CAMR) are sums ofthe regression coefficients in equation (1). We also included additional macro announcements (see Table 6 for the estimationresults). Positive (negative) inflation shocks consist of actual CPI greater than (less than) the median survey forecast. Stddenotes the standard error ofCAMR, where the Bollerslev and Wooldridge (1992) corrections are used.

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TABLE 4

Cumulative Abnormal Marginal Returns (CAMRs) in Response to Different CPI Shocks inNeutral Manufacturing Capacity Utilization Change Periods (Stable Economic State)


negative shocks


CAMR Std p-value

CAMR Std p-value

-10 0.52 0.60 0.385 0.17 0.41 0.676 0.15 0.35 0.668

-9 1.50 0.89 0.091 -0.67 0.90 0.456 1.03 0.65 0.115

-8 1.44 1.01 0.153 -1.18 1.18 0.318 1.26 0.80 0.117

-7 1.41 1.19 0.236 -2.55 1.30 0.050 1.98 0.89 0.026

-6 0.05 1.32 0.972 -1.49 1.54 0.334 0.73 1.04 0.483

-5 0.22 1.56 0.889 -1.05 1.97 0.595 0.63 1.27 0.620

-4 0.60 1.65 0.714 -2.43 1.73 0.160 1.64 1.18 0.167

-3 -0.22 1.80 0.904 -3.57 1.83 0.051 1.85 1.25 0.140

-2 -0.63 1.98 0.749 -3.34 1.91 0.081 1.51 1.33 0.257

-1 -1.43 1.99 0.472 -2.86 2.02 0.157 0.91 1.36 0.502

0 -2.28 2.08 0.272 -2.53 2.09 0.225 0.33 1.40 0.811

1 -3.00 2.31 0.193 -2.50 2.22 0.259 0.06 1.50 0.970

2 -3.58 2.41 0.136 -2.16 2.32 0.353 -0.35 1.56 0.823

3 -4.47 2.50 0.074 -3.14 2.41 0.193 -0.13 1.61 0.935

4 -4.21 2.60 0.106 -5.24 2.47 0.034 1.16 1.65 0.483

5 -3.61 2.73 0.187 -5.18 2.54 0.041 1.34 1.73 0.440

6 -3.64 2.84 0.201 -5.57 2.69 0.038 1.55 1.81 0.392

7 -4.02 2.90 0.166 -6.13 2.94 0.037 1.77 1.87 0.344

8 -4.43 2.98 0.137 -6.34 3.02 0.036 1.73 1.91 0.367

9 -5.38 3.03 0.075 -6.15 3.12 0.049 1.25 1.96 0.521

10 -4.78 3.16 0.130 -6.25 3.18 0.050 1.51 2.01 0.452

Sub-windows-5 -1 -1.48 1.59 0.351 -1.37 1.36 0.314 0.18 1.04 0.860

0 -0.87 0.70 0.213 0.33 0.47 0.487 -0.58 0.40 0.148

-1 +1 -2.37 1.37 0.083 0.84 1.07 0.434 -1.46 0.84 0.084

+1 +5 -1.32 1.58 0.404 -2.64 1.32 0.045 1.00 1.06 0.345

+6 +10 -1.18 1.53 0.443 -1.07 1.53 0.485 0.18 1.04 0.864

The sample covers the period January 1980 to September 2004. Cumulative abnormal marginal returns (CAMR) are sums ofthe regression coefficients in equation (1). We also included additional macro announcements (see Table for the estimationresults). Positive (negative) inflation shocks consist of actual CPI greater than (less than) the median survey forecast. Stddenotes the standard error ofCAMR, where the Bollerslev and Wooldridge (1992) corrections are used.

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TABLE 5

Cumulative Abnormal Marginal Returns (CAMRs) in Response to Different CPI Shocks in

Negative Manufacturing Capacity Utilization Change Periods (Slowing Economic State)

Positive inflation shock Negative inflation shock

Pooled positive and

negative shocks


CAMR Std p-value

CAMR Std p-value

-10 0.88 0.91 0.331 0.29 0.95 0.760 0.22 0.66 0.737

-9 0.27 1.19 0.820 -0.25 1.30 0.845 0.18 0.89 0.842

-8 0.29 1.58 0.853 -0.47 1.54 0.762 0.29 1.11 0.797

-7 0.47 1.95 0.811 -1.45 1.67 0.385 0.89 1.29 0.491

-6 1.83 2.26 0.416 -1.38 1.98 0.487 1.52 1.52 0.317

-5 0.89 2.43 0.714 -0.95 2.09 0.648 0.83 1.61 0.605

-4 2.28 2.72 0.404 -2.11 2.35 0.370 2.06 1.80 0.252

-3 1.07 3.02 0.722 -2.07 2.68 0.441 1.44 2.03 0.477

-2 1.36 3.14 0.664 -0.33 2.86 0.909 0.67 2.13 0.754

-1 0.02 3.22 0.994 -0.63 2.92 0.830 0.21 2.15 0.923

0 -1.38 3.37 0.683 2.15 3.29 0.514 -1.95 2.34 0.404

1 -0.05 3.54 0.988 3.31 3.53 0.348 -1.95 2.47 0.429

2 -0.19 3.67 0.958 4.01 3.82 0.294 -2.44 2.61 0.348

3 -0.27 3.78 0.944 3.28 3.93 0.403 -2.06 2.67 0.441

4 -0.47 4.00 0.906 3.48 4.60 0.449 -2.27 3.01 0.451

5 0.17 4.32 0.969 2.69 4.66 0.564 -1.53 3.11 0.622

6 0.00 4.50 1.000 2.89 4.71 0.540 -1.72 3.18 0.588

7 0.32 4.61 0.945 4.76 4.69 0.309 -2.56 3.20 0.423

8 0.18 4.70 0.970 4.60 4.75 0.333 -2.56 3.24 0.430

9 0.72 4.82 0.880 4.86 4.86 0.317 -2.47 3.32 0.456

10 1.16 4.89 0.812 5.59 4.78 0.242 -2.59 3.29 0.430Sub-windows

-5 -1 -1.81 2.24 0.418 0.75 1.84 0.682 -1.31 1.42 0.357

0 -1.40 1.00 0.160 2.77 0.82 0.001 -2.16 0.63 0.001

-1 +1 -1.42 1.57 0.365 3.64 1.50 0.015 2.62 1.06 0.013

+1 +5 1.55 2.37 0.514 0.54 2.44 0.824 0.42 1.67 0.801

+6 +10 0.99 2.14 0.644 2.90 2.26 0.198 -1.06 1.60 0.508


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TABLE 6

Estimates of the Constant Term (Non-Event Mean), MA, and GARCH Parameters for the

CPI Inflation Event Regression Model with Macroeconomic Control Variables

Coeff std p-value

Constant 0.046 0.016 0.003

CPI zero shock 0.104 0.077 0.175

Leading indicator 0.057 0.134 0.670

PPI(3) -0.106 0.164 0.518

PPI(2) 0.084 0.158 0.596

PPI(1) 0.102 0.163 0.531

PPI(0) -0.573 0.174 0.001

PPI(-1) -0.279 0.151 0.064

PPI(-2) -0.138 0.222 0.534

PPI(-3) -0.170 0.139 0.220

PPI zero shock 0.179 0.122 0.142

Housing starts 0.555 0.600 0.355

Personal income 0.022 0.182 0.905

Industrial production 0.081 0.144 0.577Durable goods: New orders -0.011 0.016 0.505

Retail sales -0.120 0.071 0.091

Trade balance 0.040 0.025 0.104

Unemployment 0.345 0.314 0.272

AR(1) 0.037 0.013 0.005

Variance equation

C 0.013 0.003 0.000

RESID(-1)2 0.023 0.008 0.004

RESID(-1)2*(RESID(-1)

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TABLE 7

Cumulative Abnormal Marginal Returns (CAMRs) in Response to Different PPI Shocks in

Positive Manufacturing Capacity Utilization Change Periods (Rising Economic State)


negative shocks

Day

CAM

R Std p-value

CAM

R Std p-value

CAM

R Std p-value-10 -0.02 0.54 0.972 0.07 0.25 0.772 -0.08 0.24 0.732

-9 0.49 0.75 0.518 0.12 0.45 0.791 -0.03 0.40 0.947

-8 0.50 0.86 0.561 -0.19 0.54 0.727 0.22 0.46 0.632

-7 1.43 1.02 0.160 -0.59 0.67 0.380 0.77 0.57 0.174

-6 1.72 1.10 0.119 -0.56 0.74 0.449 0.83 0.61 0.175

-5 1.42 1.24 0.250 -0.68 0.81 0.402 0.86 0.68 0.203

-4 1.25 1.55 0.421 -0.68 0.87 0.431 0.87 0.75 0.242

-3 0.57 1.63 0.726 -0.94 0.94 0.317 0.87 0.80 0.275

-2 0.55 1.73 0.749 -0.70 1.02 0.492 0.64 0.86 0.458

-1 -0.17 1.93 0.928 -0.72 1.05 0.490 0.47 0.91 0.606

0 -0.78 2.01 0.698 -0.50 1.13 0.660 0.16 0.95 0.870

1 -0.75 2.07 0.718 -0.43 1.18 0.715 0.12 0.99 0.901

2 -1.49 2.10 0.479 -0.20 1.22 0.868 -0.24 1.01 0.811

3 -0.82 2.14 0.700 -0.16 1.27 0.899 -0.08 1.05 0.937

4 -1.15 2.23 0.606 -0.50 1.30 0.699 0.07 1.07 0.950

5 -1.15 2.26 0.611 -0.55 1.35 0.686 0.11 1.11 0.921

6 -1.06 2.40 0.658 -1.26 1.40 0.370 0.63 1.15 0.582

7 -0.88 2.43 0.715 -1.81 1.46 0.214 1.10 1.18 0.352

8 -0.28 2.47 0.911 -1.97 1.50 0.187 1.42 1.22 0.243

9 0.24 2.59 0.925 -1.73 1.54 0.262 1.42 1.26 0.260

10 0.89 2.65 0.738 -1.74 1.56 0.266 1.62 1.29 0.210

Sub-windows

-5 -1 -1.89 1.55 0.223 -0.17 0.75 0.826 -0.37 0.69 0.5990 -0.61 0.54 0.265 0.23 0.36 0.535 -0.31 0.30 0.293

+1 +5 -0.37 1.06 0.728 -0.05 0.66 0.942 -0.05 0.56 0.935

+6 +10 2.04 1.30 0.117 -1.19 0.74 0.109 1.51 0.67 0.024


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TABLE 8


Neutral Manufacturing Capacity Utilization Change Periods (Stable Economic State)


negative shocks

Day CAM

R Std p-value

CAM

R Std p-value

CAM

R Std p-value

-10 0.50 0.25 0.049 -0.02 0.21 0.929 0.17 0.16 0.276

-9 0.92 0.44 0.037 -0.05 0.36 0.894 0.34 0.28 0.221

-8 0.84 0.54 0.123 0.04 0.46 0.922 0.29 0.35 0.419

-7 1.78 0.68 0.009 0.49 0.52 0.349 0.33 0.43 0.452

-6 1.80 0.82 0.029 0.52 0.59 0.375 0.34 0.50 0.503

-5 1.95 0.91 0.032 0.17 0.67 0.796 0.62 0.55 0.263

-4 2.16 0.97 0.026 0.10 0.77 0.897 0.75 0.60 0.212

-3 2.69 1.09 0.013 0.26 0.82 0.756 0.84 0.66 0.205

-2 4.30 1.35 0.001 0.06 0.89 0.946 1.43 0.73 0.051

-1 4.96 1.47 0.001 -0.32 0.93 0.732 1.89 0.78 0.016

0 3.95 1.42 0.005 -0.05 0.97 0.961 1.42 0.79 0.0701 3.05 1.46 0.036 0.20 1.00 0.845 0.98 0.81 0.225

2 2.40 1.46 0.101 0.03 1.06 0.976 0.85 0.81 0.293

3 2.61 1.50 0.082 0.22 1.10 0.840 0.80 0.83 0.337

4 2.37 1.54 0.124 0.51 1.17 0.661 0.56 0.88 0.526

5 3.03 1.60 0.058 0.59 1.20 0.622 0.73 0.91 0.423

6 2.53 1.64 0.123 0.67 1.24 0.587 0.56 0.92 0.542

7 1.86 1.67 0.266 0.57 1.30 0.661 0.47 0.95 0.617

8 1.75 1.74 0.315 0.87 1.34 0.515 0.24 0.97 0.805

9 2.50 1.80 0.164 0.81 1.37 0.554 0.52 1.01 0.607

10 3.08 1.89 0.103 0.63 1.42 0.658 0.85 1.05 0.422

Sub-windows

-5 -1 3.16 1.14 0.006 -0.84 0.67 0.210 1.55 0.59 0.008

0 -1.00 0.61 0.098 0.27 0.24 0.259 -0.47 0.26 0.073

+1 +5 -0.92 1.16 0.425 0.64 0.62 0.301 -0.69 0.58 0.229

+6 +10 0.05 0.92 0.960 0.04 0.65 0.956 0.12 0.53 0.823

The sample covers the period January 1980 to September 2004. Cumulative abnormal marginal returns (CAMR) are sums ofthe regression coefficients in equation (1). We also included additional macro announcements (see Table 6 for the estimationresults. Positive (negative) inflation shocks consist of actual CPI greater than (less than) the median survey forecast. Stddenotes the standard error ofCAMR, where the Bollerslev and Wooldridge (1992) corrections are used.

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TABLE 9


Negative Manufacturing Capacity Utilization Change Periods (Slowing Economic State)


negative shocks

Day CAM

R Std p-value

CAM

R Std p-value

CAM

R Std p-value

-10 -0.12 0.68 0.857 0.01 0.38 0.977 -0.03 0.33 0.932

-9 -0.09 0.81 0.910 0.25 0.51 0.628 -0.16 0.42 0.711

-8 1.04 0.89 0.245 0.36 0.61 0.556 0.07 0.50 0.881

-7 1.63 1.04 0.115 0.52 0.72 0.470 0.15 0.60 0.800

-6 1.48 1.08 0.170 0.47 0.80 0.554 0.16 0.65 0.800

-5 1.36 1.14 0.235 1.10 0.87 0.209 -0.29 0.71 0.683

-4 1.79 1.23 0.146 0.78 0.98 0.427 0.13 0.80 0.874

-3 2.02 1.39 0.147 1.44 1.15 0.213 -0.30 0.96 0.755

-2 1.32 1.47 0.367 1.94 1.25 0.121 -0.86 1.04 0.409

-1 0.80 1.56 0.607 2.30 1.32 0.081 -1.26 1.11 0.257

0 0.03 1.69 0.984 3.45 1.38 0.012 -2.22 1.16 0.0551 0.48 1.74 0.783 3.87 1.43 0.007 -2.40 1.21 0.047

2 0.17 1.81 0.927 3.72 1.48 0.012 -2.43 1.26 0.053

3 0.02 1.91 0.993 4.50 1.54 0.004 -2.98 1.32 0.024

4 0.74 2.00 0.711 5.07 1.68 0.003 -3.12 1.41 0.027

5 1.39 2.04 0.496 4.83 1.69 0.004 -2.69 1.42 0.058

6 2.02 2.14 0.343 5.21 1.86 0.005 -2.79 1.56 0.074

7 1.37 2.20 0.533 5.14 1.90 0.007 -2.95 1.59 0.063

8 1.16 2.23 0.603 6.12 1.96 0.002 -3.65 1.61 0.023

9 -0.06 2.25 0.978 6.35 1.92 0.001 -4.14 1.59 0.009

10 0.74 2.28 0.746 6.06 1.95 0.002 -3.70 1.60 0.021

Sub-windows

-5 -1 -0.67 1.16 0.563 1.83 0.97 0.059 -1.42 0.83 0.086

0 -0.77 0.57 0.179 1.14 0.29 0.000 -0.96 0.27 0.000

+1 +5 1.35 1.15 0.241 1.38 0.85 0.104 -0.47 0.70 0.501

+6 +10 -0.65 1.06 0.540 1.23 0.82 0.135 -1.02 0.67 0.130


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TABLE 10

Estimates of the Constant Term (Non-Event Mean), MA, and GARCH Parameters for the

PPI Inflation Event Regression Model with Macroeconomic Control Variables

Coeff std p-value

Constant 0.017 0.015 0.259

PPI zero shock 0.163 0.126 0.196

Leading indicator 0.081 0.125 0.518

CPI(3) -0.001 0.343 0.997

CPI(2) -0.553 0.373 0.137

CPI(1) -0.779 0.362 0.031

CPI(0) -1.071 0.324 0.001

CPI(-1) -0.280 0.341 0.411

CPI(-2) -0.371 0.313 0.237

CPI(-3) 0.262 0.322 0.415

CPI zero shock 0.133 0.077 0.085

Housing starts 0.637 0.580 0.272

Personal income 0.040 0.183 0.829

Industrial production 0.099 0.145 0.495Durable goods: New orders -0.011 0.016 0.483

Retail sales -0.106 0.069 0.125

Trade balance 0.044 0.025 0.073

Unemployment 0.302 0.323 0.349

AR(1) 0.036 0.013 0.006

Variance equation

C 0.012 0.002 0.000

RESID(-1)2 0.019 0.008 0.019

RESID(-1)2*(RESID(-1)

emp5

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