Explains What the Stock Markets Reaction to
Federal Reserve Policy?
Ben S. Bernanke
Kenneth N. Kuttner
March 2004
Abstract
This paper analyzes the impact of changes in monetary policy on equity prices,
with the objectives both of measuring the average reaction of the stock market and
also of understanding the economic sources of that reaction. We nd that, on average,
a hypothetical unanticipated 25-basis-point cut in the federal funds rate target is associated with about a one percent increase in broad stock indexes. Adapting a methodology due to Campbell (1991) and Campbell and Ammer (1993), we nd that the effects
of unanticipated monetary policy actions on expected excess returns account for the
largest part of the response of stock prices. JEL codes: E44, G12.
Board
of Governors of the Federal Reserve System and Princeton University (Bernanke) and Oberlin
College and NBER (Kuttner). Correspondence to Ken Kuttner, Economics Department, Rice Hall, 10 North
Professor Street, Oberlin, OH 44074, e-mail [e-mail]. Thanks to John Campbell for his
advice; to Jon Faust, Refet G rkaynak, Martin Lettau, Sydney Ludvigson, Athanasios Orphanides, Glenn
u
Rudebusch, Brian Sack, Chris Sims, Eric Swanson, an anonymous referee and the associate editor of the
Journal of Finance for their comments; and to Peter Bondarenko for research assistance. The views expressed
here are solely those of the authors, and not necessarily those of the Federal Reserve System.
1 Introduction
The ultimate objectives of monetary policy are expressed in terms of macroeconomic variables such as output, employment, and ination. However, the inuence of monetary policy
instruments on these variables is at best indirect. The most direct and immediate effects of
monetary policy actions, such as changes in the federal funds rate, are on the nancial markets; by affecting asset prices and returns, policymakers try to modify economic behavior
in ways that will help to achieve their ultimate objectives. Understanding the links between
monetary policy and asset prices is thus crucially important for understanding the policy
transmission mechanism.
This paper is an empirical study of the relationship between monetary policy and one
of the most important nancial markets, the market for equities. According to the conventional wisdom, changes in monetary policy are transmitted through the stock market
via changes in the values of private portfolios (the wealth effect), changes in the cost of
capital, and by other mechanisms as well. Some observers also view the stock market as
an independent source of macroeconomic volatility, to which policymakers may wish to
respond. For these reasons, it will be useful to obtain quantitative estimates of the links
between monetary policy changes and stock prices. In this paper we have two principal
objectives. First, we measure and analyze in some detail the stock markets response to
monetary policy actions, both in the aggregate and at the level of industry portfolios. Second, we try to gain some insights into the reasons for the stock markets response.
Estimating the response of equity prices to monetary policy actions is complicated by
the fact that the market is unlikely to respond to policy actions that were already anticipated.
Distinguishing between expected and unexpected policy actions is therefore essential for
discerning their effects. A natural way to do this is to use the technique proposed by
Kuttner (2001), which uses federal funds futures data to construct a measure of surprise
rate changes.1 To explain the economic reasons for the observed market response to policy
1 Cochrane
and Piazzesi (2002) proposed using the change in term eurodollar rates to measure policy
surprises, while Rigobon and Sack (2002) utilized the eurodollar futures rate. While these measures provide
informative gauges of interest rate expectations over a slightly longer horizon, G rkaynak et al. (2002)
u
1
surprises requires an assessment of how those policy surprises affect expectations of future
interest rates, dividends, and excess returns. To do this, we adapt the procedure developed
by Campbell (1991) and Campbell and Ammer (1993), which uses a vector autoregression
(VAR) to calculate revisions in expectations of these key variables.
The results presented in section 2 of the paper show that the market reacts fairly strongly
to surprise funds rate changes. Specically, for a sample consisting of the union of days
with a change in the target funds rate target and days of meetings of the Federal Open
Market Committee (FOMC), we estimate that the CRSP value-weighted index registers a
one-day gain of roughly one percent in response to a hypothetical surprise 25-basis-point
easing. The market reacts little, if at all, to the component of funds rate changes that are
anticipated by futures market participants. A comparable reaction is observed at a monthly
unit of observation.
These results are broadly consistent with those of other studies which have looked at
the link between monetary policy and the stock market. Thorbecke (1997), for example,
documented a response of stock prices to shocks from an identied vector autoregression
(VAR); in a similar vein, Jensen et al. (1996) and Jensen and Mercer (1998), examined the
markets response to discount rate changes. This paper improves on these earlier efforts by
using a measure of monetary policy based on futures data, which more cleanly isolates the
unanticipated element of policy actions. In that sense, this paper resembles the more recent
work of Rigobon and Sack (2002), who reported a signicant response of the stock market
to interest rate surprises derived from eurodollar futures. That papers main innovation was
the use of a novel, heteroskedasticity-based estimator to correct for possible simultaneity
bias, an approach subsequently extended by Craine and Martin (2003). The analysis in this
paper takes a more conventional event-study approach, while controlling directly for certain
kinds of information jointly affecting monetary policy and stock prices. Section 2 also
includes an assessment of the results sensitivity to potential outliers, and an exploration
of certain kinds of asymmetries in the markets response. Additional analysis distinguishes
showed that federal funds futures are the best predictors of target funds rate changes one to ve months
ahead.
2
between policy actions that affect the expected level of future interest rates, versus those
that affect only the timing of rate changes.
Section 3 takes up the question of what explains equity prices response, an issue not
addressed by any of the papers cited above. The approach taken here is an adaptation of the
VAR method proposed by Campbell (1991) and Campbell and Ammer (1993). The main
nding is that policys impact on equity prices comes predominantly through its effect on
expected future excess equity returns. Specically, we nd that while an unanticipated rate
cut (for example) generates an immediate rise in equity prices, it tends to be associated
with an extended period of lower-than-normal excess returns. Some effect of policy on
equity returns can be traced to revisions in cash ow forecasts, but very little is directly
attributable to changes in expected real interest rates. One interpretation of this result is
that monetary policy surprises are associated with changes in the equity premium, a point
we discuss further below. But in the absence of a fully-developed asset pricing model, it is
impossible to distinguish this interpretation from a simple market overreaction.
Relatively few papers to date have attempted to provide an explanation for the markets
reaction to monetary policy. One effort along these lines is that of Patelis (1997), who
also used the Campbell-Ammer framework to perform a decomposition similar to ours.
Goto and Valkanov (2000) used a somewhat different VAR-based method to focus on the
covariance between ination and stock returns. Both relied on policy shocks derived from
identied VARs, however, rather than the futures-based surprise used in our analysis. Boyd
et al. (2001) also considered the linkage between policy and stock prices. Their analysis
focused on the markets response to employment news, rather than to monetary policy
directly, however.
3
2 The reaction of equity prices to changes in the target
federal funds rate
This section focuses on the immediate impact of monetary policy on equity prices, both for
broad stock market indices and for industry portfolios. As noted in the introduction, however, one difculty inherent in measuring policys effects is that asset markets are forward
looking and hence tend to incorporate any information about anticipated policy changes.
Some effort is therefore required to isolate the unexpected policy change which might plausibly generate a market response. This does not say that asset prices respond to monetary
policy only when the Fed surprises the markets, of course. Naturally, asset prices will also
respond to revisions in expectations about future policy, which in turn may be driven by
news about changing economic conditions. Our focus on unexpected policy actions allows
us to circumvent difcult issues of endogeneity and simultaneity, and discern more clearly
the stock market reaction to monetary policy.
One convenient, market-based way to identify unexpected funds rate changes relies on
the price of federal funds futures contracts, which embody expectations of the effective
federal funds rate, averaged over the settlement month.2 Krueger and Kuttner (1996) found
that the federal funds futures rates yielded efcient forecasts of funds rate changes. Kuttner
(2001) subsequently used these futures data to estimate the response of the term structure
to monetary policy. The analysis in this section employs a similar method to gauge the
response of equity prices to unanticipated changes in the federal funds rate from 1989
through 2002.
2.1 Measuring the surprise element of policy actions
A measure of the surprise element of any specic change in the federal funds target can be
derived from the change in the futures contracts price relative to the day prior to the policy
2 The
contracts, ofcially referred to as 30 Day Federal Funds Futures, are traded on the Chicago Board
of Trade. The implied futures rate is 100 minus the contract price.
4
action. For an event taking place on day d of month m, the unexpected, or surprise target
funds rate change can be calculated from the change in the rate implied by the currentmonth futures contract. But because the contracts settlement price is based on the monthly
average federal funds rate, the change in the implied futures rate must be scaled up by a
factor related to the number of days in the month affected by the change,
i u =
D
Dd
0
0
fm,d fm,d 1
,
(1)
0
where iu is the unexpected target rate change, f m,d is the current-month futures rate and D
is the number of days in the month. 3 The expected component of the rate change is dened
as the actual change minus the surprise, or
i e = i i u .
(2)
Getting the timing right is, of course, crucial for event-study analysis. Before 1994,
when the Fed instituted its current policy of announcing changes in the funds rate target, market participants generally became aware of policy actions on the day after the
FOMCs decision, when it was implemented by the Open Market Desk. Following Rudebusch (1995) and Hilton (1994), we assign most pre-1994 rate changes to the date of the
Desks implementation. As documented in Kuttner (2003), however, the sample contains
several minor deviations from this pattern. Six of these correspond to days on which the
Desk allowed the funds rate to drift downward in advance (and presumably in anticipation)
of the FOMCs decision, with the full awareness that its inaction would be interpreted as
an easing of policy. A seventh exception occurred on December 18, 1990, when the Board
of Governors made an unusual late-afternoon announcement of a cut in the discount rate,
3
Because the monthly average of the effective federal funds rate on which the contract is based is very
close to the average target rate, this method generally provides a good gauge of the surprise change in the
target federal funds rate. In order to minimize the effect of any month-end noise in the effective funds rate,
however, the unscaled change in the one-month futures rate is used to calculate the funds rate surprise when
the change falls on one of the last three days of the month. Also, when the rate change occurs on the rst day
1
0
of the month, f m1,D is be used instead of f m,d 1 . See Kuttner (2001) for details.
5
from which market observers (correctly) inferred a 25-basis-point rate cut.
The policy of announcing target rate changes, which began in February 1994, eliminates
virtually all of the timing ambiguity associated with rate changes in the earlier part of the
sample. Moreover, because the change in the target rate is usually announced prior to the
close of the futures market, the closing futures price generally incorporates the days news
about monetary policy. The only exception is October 15, 1998, when a 25-basis-point
rate cut was announced after the close of the futures markets. In this case, the difference
between the opening rate on the 16th and the closing rate on the 15th is used to calculate
the surprise.
2.2 Baseline event study results
One approach to measuring the impact of Federal Reserve policy on the stock market is to
calculate the markets reaction to funds rate changes on the day of the change. The market
may of course also react to the lack of a change in the funds rate target, if a change had
been anticipated. Because this approach involves looking at the response to specic events,
it might be described as an event-study style of analysis. For the purpose of this paper,
the relevant sample of events is dened as the union of all days when the funds rate target
was changed, and days corresponding to FOMC meetings. The rst event in the sample
is the June 1989 25-basis-point rate cut, and the last corresponds to the FOMC meeting
in December 2002. The 17 September 2001 observation is excluded from the analysis, as
that days rate cut occurred on the rst day of trading following the September 11 terrorist
attacks. Altogether, the sample contains 131 observations.
Table 1 presents a selection of descriptive statistics on the policy surprises and stock
returns in our sample. The statistics are reported both for the pre-1994 period, when
changes in the funds rate target were generally unannounced and frequently occurred between scheduled FOMC meetings, and the post-1994 period when all rate changes were
announced, and most coincided with FOMC meetings. As measured by the standard deviation, the typical funds rate surprise in both periods is roughly 10 basis points; by contrast,
6
equity prices are half again as volatile post-1994 as pre-1994. In both subsamples, equity
returns are roughly ten percent more volatile on the monetary policy event days than on
non-event days, consistent with policy actions inducing a market reaction of some kind.
Baseline estimates of the reaction of equity prices to monetary policy appear in Table 2.
The results in column (a) of the table are based on a regression of the CRSP value-weighted
return on the raw change in the federal funds rate target,
Ht = a + bit + t ,
(3)
making no distinction between surprise and expected changes; Ht represents the stock return, and it is the funds rate target. The regression used for the results in column (b)
Ht = a + be ite + bu itu + t ,
(4)
distinguishes between expected and unexpected funds rate changes, ite and itu , using the
decomposition described above in section 2.1.
In both specications, the error term t represents factors other than monetary policy
that affect stock prices on event days. These factors are assumed to be orthogonal to the
changes in the federal funds rate appearing on the right-hand side of the regression. Section
2.3 below discusses the validity of this assumption in some detail, and section 2.4 presents
results that control directly for one observable source of endogeneity.
Although it has the expected negative sign, the response to the raw target rate change
reported in column (a) of Table 2.2 is small and insignicant. When the target rate change
is broken down into its expected and surprise components, however, the estimated stock
market response to the latter is negative and highly signicant: the results reported in column (b) imply a 4.68% one-day return in response to a one percentage point surprise rate
cut.4 The R2 indicates that 17% of the variance in equity prices on these event days is
associated with news about monetary policy. While Fed policy accounts for a nontrivial
4 Very
similar results are obtained using the S&P 500 in place of the CRSP value-weighted return.
7
portion of the variance of stock returns on event days, clearly it is far from the only piece
of new information affecting stock returns.
The negative relationship between funds rate surprises and stock returns is readily visible in Figure 1. Also apparent, however, are a number of observations characterized by
very large changes in equity prices some exceeding three standard deviations in magnitude. This naturally raises the question of whether the results reported in the rst two
columns of Table 2 are sensitive to the inclusion of these observations.
To determine which observations might have an unduly large effect on the regression
results, we computed inuence statistics for each observation in the sample. These statis
tics are calculated from the quadratic form bt 1 bt , where bt is change in the vector
of regression coefcients resulting from dropping observation t , and is the estimated covariance matrix of the coefcients. The distribution of these statistics, plotted in Figure 2,
conrms that six observations, all with statistics in excess of 0.3, exert an unusually large
inuence on the estimates; the comparable statistics for the remaining observations are all
well below 0.2 and most are less than 0.05. The six observations associated with the large
inuence statistics are labeled in Figure 1: 8 August 1991, 2 July 1992, 15 October 1998, 3
January 2001, 20 March 2001, and 18 April 2001. The rst two of these are associated with
events other than monetary policy actions, while the most recent four arguably represent
unusual reactions to monetary policy actions. Each is in its own way is revealing.
All three of the candidate outliers occurring during the easing cycle that began in 2001
are classied as such because of their abnormally large reactions to the funds rate surprises.
The unexpected 50-basis-point intermeeting rate reductions on 3 January and 18 April were
both greeted euphorically, with one-day returns of 5.3% and 4.0% respectively. The 50basis-point rate cut on 20 March was received less enthusiastically, however. Even though
the cut was more or less what the futures market had been anticipating, nancial press
reported that many equity market participants were disappointed the rate cut hadnt been
an even larger 75 basis point action. Consequently, the market lost more than 2%.
Another unusually vehement reaction to a Fed action is associated with the 25-basis-
8
point intermeeting rate cut on 15 October 1998, which was taken in response to unsettled
conditions in the nancial markets specically, the deteriorating situations in Asia and
Russia. For whatever reason, the unexpected intermeeting cut lifted equities over 4%.
The stock market fell less than 0.3% on 2 July 1992. What makes this reaction unusual,
however, is the fact that it came on a day when the Fed unexpectedly cut the funds rate target
by 50 basis points. The decision to cut was no doubt inuenced by that days unusually
bleak employment report, in which reported payroll employment fell by 117 thousand. This
raises the issue that some of the surprise rate changes in the sample may in fact represent
endogenous responses to economic news, such as the employment report. This possibility
is investigated in greater detail below in section 2.4.
The nal candidate outlier is 21 August 1991, when the CRSP value-weighted index
rose 2.7% on a day associated with an FOMC meeting. The futures market had apparently
priced in some possibility of a rate cut on that day, but the FOMCs decision to leave rates
unchanged generated a small, positive surprise. The nancial press reported that the stock
market jump was a response to the resolution of the attempted coup in Russia clearly an
event with no direct relation to that days FOMC decision.
Two additional observations are highlighted in Figure 1: 17 May and 16 August 1994.
While their relatively low inuence statistics (0.05 and 0.04) do not qualify them as outliers, they stand out as unusual instances in which equities rose in spite of signicant,
positive funds rate surprises. As noted in Kuttner (2001), a similarly anomalous response
is observed in the response of bond yields on those dates. The reason seems to be that both
of these larger-than-expected 50-basis-point rate hikes were accompanied by statements by
the FOMC suggesting that further rate increases were not imminent. This interpretation is
consistent with the results reported below in section 2.6 indicating that the three-monthahead futures rates fell on the dates in question.
Columns (c) and (d) of Table 2 show the effect of dropping the six candidate outliers
identied above. (The two observations from 1994 are retained.) The estimated response
to funds rate surprises is still negative and signicant, but smaller in magnitude: 2.55, as
9
opposed to 4.68. The response to the expected component is smaller (and now no longer
signicant at the 0.05 level), as is the response to the raw funds rate change in column (c).
Excluding the six outliers also decreases the R-squared from 0.17 to 0.05.
2.3 Orthogonality revisited
As noted above, the event-study results reported in section 2.2 rely on the assumption
that the error term is orthogonal to funds rate changes. One reason for a violation of this
condition would be a contemporaneous response of monetary policy to the stock market.
There are, however, no clear examples of instances in which a drop in equity prices led the
FOMC to cut rates, or the inverse. Even in monthly data, evidence for such a systematic
reaction is elusive.5 Moreover, to the extent the FOMC did respond in this way, it would
tend to reduce the size of the estimated response to the funds rate surprise.
The orthogonality condition would also fail to hold if monetary policy and the stock
market both responded jointly (and contemporaneously) to new information. For example, the release of data indicating weaker-than-expected economic growth would plausibly
cause the stock market to decline, and make a cut in the funds rate target more likely. 6 As
in the case of a direct policy response to the stock market, the resulting tendency for rate
cuts to be associated with stock market declines would lead to a downward bias in the size
of policys estimated market impact. A similarly attenuated reaction would be observed
if surprise policy actions were thought to reveal private information about the state of the
economy.7
Instances of direct, same-day policy responses to economic news are rare in our sample
at least in recent years, when the FOMC meeting schedule dictated the timing of most
policy actions. During the pre-1994 subsample, however, it was not uncommon for the
5 See,
for example, Bernanke and Gertler (1999) and Fuhrer and Tootell (2003). Some evidence to the
contrary was obtained by Rigobon and Sack (2003), however.
6
The 17 September 2001 is an extreme example of just such a joint response: the Feds 50 basis point rate
cut and the stock markets sharp drop were both clearly spurred by the previous weeks terrorist attacks.
7 Romer and Romer (2000) suggested that such an information advantage could account for the bond
markets response to monetary policy. However Faust et al. (2003) found little evidence to support this view.
10
FOMC to cut rates on the heels of weaker-than-expected employment data. In fact, ten
of the 23 rate cuts from June 1989 through July 1992 coincided with the release of the
employment report. The analysis in section 2.4 below addresses this issue directly by
allowing for a different market response on employment release days.
Recent studies have proposed two generic solutions to the endogeneity and jointresponse issues. One is to use intraday data in a relatively narrow event window surrounding the FOMCs announcement, thus distinguishing the impact of the policy change
from the effects of news arriving earlier or later in the day. Applying this approach,
G rkaynak et al. (2004) reported, in work subsequent to ours, an equity price response
u
that was virtually identical to that obtained from daily data. (Using intraday data did, however, result in a considerable improvement in the R 2 .) Also in subsequent work, DAmico
and Farka (2003) uncovered a similar reaction using an approach incorporating intraday
data in a VAR specication.
The other generic solution is more statistical in nature. Rigobon and Sack (2002),
for example, used an estimator which, by exploiting the heteroskedasticity introduced by
exogenous monetary policy actions, yields consistent estimates of the markets response.
In a related approach, Craine and Martin (2003) developed a multivariate factor model that
allows all asset prices to respond to common, unobserved information shocks. In the end,
however, both studies report results that are very close to those obtained from event-study
methods.
A correlation between the error term and the regressors in (4) could also arise if the
regressors were measured with error. This possibility was explored by Poole et al. (2002)
in the context of Treasury yields response to monetary policy. They assumed the measurement error in the funds rate surprise was uncorrelated with other factors affecting yields,
turning it into a classical errors-in-variables problem. To gauge the size of the measurement
error, Poole et al. calculated the variance of the futures rate on days when the actual funds
rate change was in line with the consensus market expectations reported by the Wall Street
Journal; using this estimate, they found the attenuation in the bond markets response was
11
typically on the order of ve to ten percent.
Overall, the alternative econometric methods that have been used to correct for mismeasurement of the funds rate surprises uniformly yield results similar to those relying on the
event-study methodology used in section 2.2. Moreover, to the extent that the event-study
results are biased, that bias tends to understate the true response to monetary policy. Thus,
it seems safe to proceed using the event-study approach, bearing in mind that it may yield
slightly conservative estimates of the stock markets reaction to monetary policy.
2.4 Employment releases and subsample stability
This section investigates the robustness of the results reported in section 2.2 along two dimensions. One issue has to do with the joint response of monetary policy and the stock
market to economic news. As noted above, ten funds rate cuts in the pre-1994 part of the
sample occurred on the same day as the employment report. After 1994, with rate changes
more or less dictated by the exogenous scheduling of FOMC meetings, this becomes less of
an issue. As is evident in Figure 1, these observations are characterized by little, if any, correlation between the funds rate surprise and the stock return. In these instances, the good
news for the stock market represented by the Feds actions seems to have been almost
exactly offset by the bad news about economic activity contained in the employment
report.
Another issue concerns the stability of the estimated relationship. The avoidance of
a same-day response to employment reports (and other economic news) is one possible
reason the relationship might have changed in the early 1990s. The FOMCs practice of
explicitly announcing rate changes, which began in February 1994, may also have altered
the stock markets response to monetary policy.
To explore the possibility of different responses either post- 1994, or on days associated
with employment releases, we interact the surprise rate change with dummy variables: one
equal to 1 starting with the 4 February 1994 observation, and another equal to 1 on the
days of pre-1994 employment releases. Table 3 reports the response of the CRSP value12
weighted index to surprise rate changes in the presence of these interactive dummies. Like
Table 2, columns (a) and (b) give the results for the full sample, and columns (c) and (d)
give the results for the sample excluding the six candidate outliers identied above.
At rst glance, the results in column (a) appear to show that the entire equity price
response can be traced to the post-1994 period. The coefcient on the surprise itself is
only 1.25 and insignicant; that on the surprise interacted with the post-1994 dummy is a
highly signicant 6.87. This conclusion would be premature, however, as this regression
neglects the possibility of endogeneity in the policy response prior to 1994. Including the
surprise interacted with the employment release dummy as in column (b), increases the
magnitude of the surprise response, and the positive interaction term implies a near-zero
response to policy when it coincides with an employment release. These coefcients are
statistically signicant at only the 0.10 level however, and the post-1994 interaction term
remains large and highly signicant.
The signicance of the post-1994 term is heavily inuenced by the six outliers identied above, however. With those observations excluded, as in column (c), post-1994 rate
surprises have only a slightly larger effect, and the difference is not statistically signicant.
The coefcient on the surprise itself 2.29, and signicant at the 0.05 level. But when
the employment interaction term is included, the surprise coefcient grows to 3.57. This
effect is almost exactly offset for employment release days by the 3.33 coefcient on the
interaction term. Both are now highly signicant, and the post-1994 dummy remains insignicant. Thus, if the six candidate outliers are discarded as unrepresentative, there is no
evidence of a break in 1994. Furthermore, the results conrm that the endogeneity problem
discussed above reduces the OLS estimates of the markets response to policy surprises.
2.5 Asymmetries
Another set of questions concerns asymmetries, broadly dened: the possibility that the
equity price response to monetary policy depends on the direction of the action, or on the
context in which it occurred. As above in section 2.4, interactive dummy variables are used
13
in the regression to investigate these questions.
One possibility is that the magnitude of the markets response depends the sign of the
surprise. To allow for this, a dummy variable was set to 1 for those 37 observations with
positive surprises. An interaction term involving this dummy and the surprise rate change
was then included in the regression. The interactive term involving the employment release
is also included, in order to pick up the smaller impact of funds rate surprises on employment release days. As above, the regressions are run with and without the six candidate
outliers identied earlier. The results reported in Table 4 provide weak support at best for
this form of asymmetry. For the full sample, in column (a) of the table, the coefcient on
the interaction term indicates a smaller effect of positive surprises, but the difference is not
statistically signicant. There is virtually no difference for the no-outlier sample, shown in
column (d).
A related kind of asymmetry can be modeled by including interactive dummies for rate
changes associated with increases in the funds rate, and with surprises associated with no
change in the funds rate. The full sample contains 14 observations of the former, and 76 of
the latter. The results of this exercise appear in columns (b) and (e) of the table for the full
and no-outlier samples. Again, the statistically insignicant coefcient in the rate increase
interaction variable suggests the direction of movement is not an important determinant
of the markets reaction. The positive and statistically signicant estimated coefcient on
the no change interaction variable does, however, indicate that the market responds very
little, if at all, to policy inactions. This presumably means that the failure to move at any
specic FOMC meeting may be viewed largely as postponing the inevitable.
A third sort of asymmetry has to do with the context of the rate decision: whether it
was taken at an FOMC meeting (109 observations), or represented a change in the direction of short-term interest rates (ve observations). Interaction terms involving suitablyconstructed dummy variables are again used to capture possible differences in the markets
response. The sign of the FOMC interaction term is unclear a priori. Decisions taken at
FOMC meetings may be less subject to the sort of endogeneity issues discussed above,
14
which would tend to increase the impact of rate changes on these days. On the other hand,
intermeeting changes (at least those not associated with employment reports) may convey
an urgency on the part of the FOMC which would tend to increase the size of the response.
To the extent that interest rate reversals have a larger impact on expected future interest rates
than other rate changes, these changes in the target federal funds rate would be expected to
elicit a larger stock market response.
Columns (c) and (f) of Table 4 show the results from a regression that includes the
FOMC and reversal interaction terms, along with employment report and post-1994 regressors. The coefcient on the surprise term remains an economically and statistically
signicant 3.97 for the full sample, and 3.67 for the no-outlier sample. In the full sample, the measured response is smaller on FOMC days; this difference disappears, however,
when the candidate outliers are excluded. Reversals seem to have a large additional impact
on the stock market: 6.33 for the full sample, and 17.62 for the no-outlier sample. In
the latter case, the implausibly large estimate is driven almost entirely by the single observation in the southeast corner of Figure 1, corresponding to the rst rate increase in 1994.
Clearly, reversals in the direction of rate changes have occasionally been met with extreme
market reactions, which accounts for the exaggerated response. With only ve observations
in the sample, however, inference on the additional stock market impact of reversals is hazardous at best. Dummying out these observations at least provides further conrmation
that the baseline results are not dependent on the inclusion of these events.
Taken together, the results presented above conrm the existence of a strong one-day
reaction of the stock market to unanticipated changes in federal funds rate. Just how strong
this response is depends on whether the handful of potentially anomalous observations are
viewed as representative, or discarded as outliers. The estimated response is stable over
time, once the tendency for the FOMC to react to employment news in the early part of the
sample is controlled for. The estimated reaction does, however, appear to be smaller (or
nonexistent) for policy surprises associated with no change in the funds rate target.
15
2.6 Timing versus level surprises
While the results presented above are consistent with a strong response of equity returns to
funds rate surprises, that response is anything but uniform. In some cases, the reaction is
muted, while in others the reaction seems out of proportion with the size of the measured
surprise. One explanation for the lack of uniformity is that funds rate surprises differ in
their impact on expected future short-term interest rates. Many of the surprises in the
sample may have been interpreted as an advancement or postponement of a more-or-less
inevitable change in policy, while others were viewed as altering the expected path of the
funds rate for months to come. Surprises with a more durable on policy expectations would
naturally tend to have a larger effect on equity prices than those which merely altered the
timing of policy actions.
One way to gauge policy surprises impact on expected future short-term rates is to
examine the relationship between the surprises and the change in the fed funds futures
rates in subsequent months. This relationship is depicted in Figure 3, which plots the
change in the three-month-ahead futures rate against the funds rate surprise for the 131
observations in our June 1989 through December 2002 sample. The 45 degree line in the
gure corresponds to a one-for-one response of the three-month futures rate to the current
month funds rate surprise. Observations lying along a shallower line (i.e., those below the
45 degree line in the northeast quadrant and above in the southwest quadrant) are therefore
those associated with a less than one-for-one effect on three-month-ahead expectations;
those lying along a steeper line had a greater-than one-for-one effect. As noted above, the
announcements accompanying the two rate hikes in May and August 1994 actually lowered
three-month-ahead interest rate expectations, and as a result those two observations fall in
the southeast quadrant.
Regressing the change in the three-month-ahead futures rate on the policy surprise
yields an estimated slope coefcient of 0.65, as shown in column (a) of Table 5. This
suggests the impact of policy surprises on expectations is typically much less than one-forone; the difference is signicant at the 0.01 level. A plausible interpretation of this result
16
is that many of the unexpected funds rate changes in the sample are to a large extent surprises only with regard to the timing of policy actions. As shown in columns (b) through
(d), FOMC meetings and no change surprises tend to be associated with an even smaller
response of expectations.
In order to determine the extent to which differences in policy surprises impact on expectations can help explain the stock markets response, our approach is to dene a variable
reecting the difference between the surprises effects on current and three-month-ahead
interest rate expectations, and include this term in the equity return regressions. Specically, our timing surprise variable is dened as the difference between the change in the
three-month-ahead futures rate and the current funds rate surprise, i.e., the vertical distance
from each observation to the 45 degree line in Figure 3. The timing surprise for an action
with equal effects on current and expected future interest rates would thus be zero; those
with a smaller effect on expected future interest rates would be negative. Results from the
stock return regressions that include the timing surprise term appear in Table 6.
For comparison purposes, columns (a) and (c) of the table simply reproduce the baseline
results reported earlier in Table 2, with and without the six candidate outliers. Columns
(b) and (d) report the regression results when the timing surprise term is added to the
regression. The inclusion of this term increases the magnitude of the coefcient on the
current-month surprise, which goes from 4.68 to 6.20 for the full sample. Because
this coefcient can now be interpreted as the impact of a funds rate surprise that changes
expectations by the same amount (i.e., with the timing surprise equal to zero), this implies
a larger stock price response to those policy surprises that affect the level of interest rates
expected to prevail three months hence.
Similarly, the statistically signicant, negative coefcient on the timing surprise term
says that surprises with a less-than one-for-one impact on expectations (i.e., those for which
the change in the three-month futures rate is smaller than the current-month surprise) have
a correspondingly smaller effect on stock prices. In the extreme case of a pure timing
surprise with no effect on the expected level of rates, the response is given by the difference
17
between the two coefcients: 1.91 for the full and 0.09 for the no-outlier sample. (Neither
is statistically signicant at even the 0.10 level.) The results therefore show that policy
actions affect stock returns only to the extent that they alter the expected level of rates in
the months ahead.
2.7 Results based on monthly data
An alternative way to dene the policy surprise is to focus on the expected change in
policy at a regular, monthly horizon. Unlike the event study approach, the regular timing
is amenable to the time series analysis employed below in section 3 to assess the causes of
the markets response. It is worth noting that in this approach, any month could potentially
contain a surprise policy action, and that a failure to change the funds rate target in any
month could represent a policy surprise. Consequently, the monthly time-series approach
is less susceptible to any sample selection issues that might arise in the context of the
event-study methodology.
The use of monthly data calls for a slightly different gauge of unanticipated policy
actions. Since the price of the federal funds futures contract is based on the monthly average
federal funds rate, a natural denition of the month-t surprise would be
1D
itu it ,d ft1 1,D ,
D d =1
(5)
where it ,d is the funds rate target on day d of month t , and f t1 1,D is the rate corresponding
to the one-month futures contract on the last (Dth) day of month t 1. 8 The expected funds
rate change is dened analogously as
ite ft1 1,D it 1,D .
(6)
The sum of the two is the average funds rate target in month t minus the target on the last
8 The
settlement price of the federal funds futures contract is determined by the average over the calendar
month, carrying the prior business days rate over to weekends and holidays.
18
day of month t 1. (The notation is used to distinguish this from the conventionallydened rst difference operator.)
This denition of the funds rate surprise raises a time aggregation issue. Measuring
the surprise in terms of the average funds rate will tend to attenuate the size of the policy
surprises, as discussed in detail in Evans and Kuttner (1998). Unfortunately, without making specic assumptions about the days of possible rate changes, there is no clean way to
correct for this problem.9 Consequently, some caution is required when interpreting the
magnitude of the surprises measured in this way. It is also important to note that the endogeneity issue discussed above in section 2.4 is almost certainly going to be more relevant
to monthly funds rate surprises than it was for the day-ahead surprises. Rate changes that
were unanticipated as of the end of the prior month may well include a systematic response
to economic news, such as employment, output and ination.
The results shown in Table 7 support the view that the month-ahead surprises incorporate an endogenous reaction to economic developments. The table reports the parameter
estimates and R2 from a regression of the monthly policy surprises on the surprise element
of key economic reports, calculated as the difference between the number released and the
consensus expectation for that number, compiled by Money Market Services. 10 Over the
full May 1989 through December 2002 sample, there appears to be a signicant withinmonth impact of several data releases on the funds rate target: nonfarm payrolls, industrial
production, retail sales, and core PPI, although these latter two have the wrong (i.e.,
negative) sign.
This relationship seems to be much stronger in the early part of the sample, however.
The second column of the table shows the results for the same regression estimated from
May 1989 through September 1992 (the date of the last rate cut associated with an employment report). The Feds reaction to bad payroll employment news is now particularly
pronounced. Moreover, the regression accounts for nearly half of the variance of the funds
9 One solution would have been to assume that post-1994 rate changes were always expected to occur
at scheduled FOMC meetings. The three intermeeting rate cuts in 2001 have made this assumption less
plausible, however.
10 We are indebted to Eric Swanson for his assistance with these data.
19
rate surprises. By contrast, in the more recent February 1994 through December 2002
subsample, there is very little evidence of a within-month reaction to economic news, as
shown in the third column of the table. Only retail sales is signicant, and the regression
now accounts for a much smaller share of the variance of funds rate surprises.11
Table 8 reports the results from a regression of the monthly CRSP value-weighted return
the expected and unexpected components of monthly funds rate changes,
Ht = a + be ite + bu itu + t .
(7)
Column (a) reports the estimates for the full sample, consisting of all 164 months from
May 1989 through December 2002. As in the earlier results, there is a strong, statistically
signicant negative response to unanticipated rate increases, and little or no response to the
anticipated actions. The R2 indicates that nearly 7% of the monthly stock return variance
can be traced to unanticipated policy actions.
It is interesting to note that the magnitude of the response, 11.43, is about twice that
found in the event-study analysis. This difference in magnitudes is readily explained by
the time aggregation issue alluded to earlier. In fact, if funds rate changes on average take
place in the middle of the month (for example, if rate changes were distributed uniformly
over the days of the month), then the magnitude of the estimated monthly surprises will be
attenuated by one-half, which would explain the doubling of the estimated response of the
stock price.
The negative relationship between policy surprises and stock returns is also evident in
the scatterplot of the data in Figure 4. As in the daily data, a number of observations stand
out as potential outliers, again raising the question of whether the results are sensitive to
their inclusion. As above, inuence statistics were calculated for each observation in the
sample; those with statistics in excess of 1.5 are agged as outliers in the plot. (The most
11 Again,
retail sales is signicant with the wrong sign. But this result is due entirely to an anomalous
7% jump in retail sales in November 2001, which happened to occur in a month in which the Fed also cut the
funds rate target.
20
conspicuous of these is the data point deep in the southwest quadrant, which corresponds
to September 2001.) Dropping these ten observations makes little difference to the results,
however. In fact, as shown in column (b) of Table 8, the estimated coefcient of 14.26 is
somewhat larger than it is for the full sample, and the R2 rises to 0.096.
The monthly data contain very little evidence for the sorts of asymmetries uncovered in
the daily data. As shown in columns (c) and (d), there is no indication that the stock price
response depends on the sign of the surprise, or on the direction of the rate change. Nor is
there any evidence of a different response to policy reversals, or to the MMS employment
surprises.12
We have so far focused on the responses of broad equity indexes, but of course it is also
possible to examine the responses of more disaggregated indexes. Table 9 reports estimates
of (7) for the ten industry portfolios constructed from CRSP returns as in Fama and French
(1988).13 The most responsive industries are high tech and telecommunications, with coefcients half again as large that for the overall value-weighted index. On the other end of
the spectrum, energy and utilities are only half as responsive as the overall market, and the
relevant coefcients are statistically insignicant. 14 The low R2 s indicate that very little
of those industries variance is associated with unexpected policy actions. The estimates
precision is, however, not sufcient to reject the hypothesis of an equal reaction for all 10
industries.
A natural question is the degree to which the pattern of responses of industry portfolios
is consistent with the implications of the CAPM that is, whether the observed responses
are proportional to the industries market betas. A straightforward way to address this
question is to obtain industry betas from a regression of the excess return in industry i, y i,t ,
12 Interestingly, the employment surprise is negative and signicant in a univariate regression (not reported),
but becomes insignicant once the federal funds surprise is included. This is consistent with the ndings of
Boyd et al. (2001), and corroborates their conjecture that the policy response accounts for equities perverse
response.
13 The Fama-French portfolio data are available from mba.tuck.dartmouth.edu/pages/faculty/ken.french.
14 Using methods similar ours, to Guo (2002) found that the impact of monetary policy on stock prices does
not seem to depend on rm capitalization.
21
on the market excess return, yM ,t ,
yi,t = + i yM ,t + t
(8)
estimated on the same May 1989 to December 2002 sample used to estimate the response
to monetary policy.15 The industry response implied by the CAPM can then be expressed
as:
i
bu = i bu
(9)
where bu is the estimated response of the CRSP value-weighted excess return to funds rate
surprises.
Figure 5 plots these tted responses to monetary policy against the estimated responses,
bu , reported in Table 9, along with the 80% condence intervals associated with those estii
mates. Also plotted is the 45-degree line that the points would lie on if the CAPM perfectly
accounted for variation across industries. Although the t is not perfect, the points line up
reasonably well along the 45-degree line, suggesting that the one-factor CAPM does a good
job of explaining the observed industry variation. High-techs measured sensitivity to monetary policy is in fact somewhat less than its beta would imply, while telecommunications
is somewhat greater. On the other end of the spectrum, the utilities and energy industries
low market betas for the most part account for their muted response to monetary policy.
The CAPM-implied response represented by the 45-degree lies within the condence intervals associated with the estimated responses, although given the imprecision of those
estimates, this is clearly not a powerful test.
15
Using betas based on the sum of contemporaneous and lagged market covariances, as in Campbell and
Vuolteenaho (2003), makes virtually no difference to the results. Campbell and Vulteenhahos proposed
two-factor decomposition also yields very similar results to those reported in the text.
22
3 Policy, fundamentals and stock prices
Having documented the reaction of equity returns to Federal Reserve policy in section 2
above, we now turn to the more difcult question of what explains the observed reaction.
There are three broad reasons why an unexpected funds rate increase may lead to a decline
in stock prices: it may be associated with a decrease in expected future dividends, a rise in
the future expected real interest rates used to discount those dividends, or an increase in the
expected excess returns (i.e., the equity premiums) associated with holding stocks. Simple
regressions of equity returns on surprise changes in the federal funds rate are silent on the
question, so a more structured approach is required to disentangle the various effects.
The approach of this paper is an adaptation of the method used by Campbell (1991),
and Campbell and Ammer (1993). In brief, their method uses a log-linear approximation to
decompose excess equity returns into components attributable to news about real interest
rates, dividends, and future excess returns, then employs a VAR methodology to obtain
proxies for the relevant expectations.16 We take the Campbell-Ammer framework one
step further, however, by relating the proxies for expectations to the news about the path
of monetary policy embodied in the surprises derived from federal funds futures. This
allows us to estimate the impact of federal funds surprises on expected future dividends,
real interest rates, and expected future excess returns. It turns out that the largest effects
come from revisions to expectations of future excess returns, and to expectations of future
dividends. Real interest rates have a very small direct impact.
The object of this analysis is the (log) excess return on equities, denoted y t +1 . This
is dened as the total return on equities (price change plus dividends), minus the risk-free
rate (the one-month Treasury bill yield). The return dated t + 1 is measured over period
y
t , i.e., from the beginning of period t to the beginning of period t + 1. Let e t +1 represent
the unexpected (relative to expectations formed at the beginning of period t ) excess return
during period t , i.e., yt +1 Et yt +1 .
16 Because
VARs require periodic time series data, the subsequent analysis will use the monthly measure
of the funds rate surprises.
23
Using the linearization developed by Campbell and Shiller (1988), we can express the
period t unexpected excess return on equity in terms of the revision the expectation of
discounted future dividends, the real interest rate, and future excess returns. (A sketch of
the derivation can be found in the appendix.) The decomposition can be written as:
y
y
et +1 = etd+1 etr+1 et +1
(10)
where the es represent the revision in expectations between periods t and t + 1, and the tilde
denotes a discounted sum, so that
etd+1 = (Et +1 Et ) j dt +1+ j
j=0
etr+1 = (Et +1 Et ) j rt +1+ j
(11)
j=0
et +1 = (Et +1 Et ) j yt +1+ j .
y
j=1
The discount factor , which comes out of the linearization, represents the steady-state ratio
of the equity price to the price plus dividend; following Campbell and Ammer (1993), this
is set to 0.9962. As emphasized by Campbell (1991), (10) is really nothing more than a
dynamic accounting identity relating the current excess return to revisions in expectations.
As such, it contains no real economic content, much less any specic asset pricing model;
such a model would be required to provide a link between the conditional expectations of
future returns and economic variables (e.g., consumption).
Implementing this decomposition requires empirical proxies for the expectations appearing in (10). The approach of Campbell (1991) and Campbell and Ammer (1993) is to
model expectations using a Vector Autoregression (VAR) involving the variables of interest (excess returns and the real interest rate) along with any other indicators that might be
helpful in forecasting those variables. Calculating the discounted sum of the revisions in
expectations is straightforward; to do so involves writing the n variable, p lag VAR as a
24
rst-order system,
zt +1 = Azt + wt +1 ,
(12)
where zt +1 is an appropriately stacked np 1 vector containing the excess equity return,
the real interest rate, and any additional indicators. With the VAR expressed in this form,
the ingredients of (10) are given by
y
et +1 = sy wt +1 ,
y
et +1 = sy A(1 A)1wt +1 ,
etr+1 = sr (1 A)1 wt +1 and
y
(13)
y
etd+1 = et +1 + et +1 etr+1 ,
where sy and sr are appropriate 1 np selection matrices.
Two features of the Campbell-Ammer method deserve further comment. One is its
parametric approach to constructing long-horizon expectations of stock returns: one has
to assume that the dynamics of equity returns many years in the future are adequately
captured by a parsimonious VAR model. To a large extent, this parametric approach is
forced upon us, as the relatively short experience with federal funds futures is not sufcient
to directly estimate the long-horizon impact on stock asset returns, particularly in light of
the questionable small-sample properties of long-horizon regressions (see Nelson and Kim
(1993)). But as discussed below, the use of the VAR does allow us to estimate the dynamics
of stock returns over a longer sample than the period for which futures data are available.
A second important feature of the approach is that dividends are not included explicitly
y
y
as a variable to be forecast; given et +1 , et +1 and etr+1 , etd+1 is backed out from (10). In
principle, it would be possible to forecast dividends directly in the VAR, and instead back
y
out an implied et +1 . In practice, however, this is complicated by a strong seasonal pattern,
and a root near unity in the dividend process. It is important to note that to the extent that the
VAR understates the predictability of excess returns, treating dividends as a residual means
25
that the method will end up attributing too much of the return volatility to dividends. 17
3.1 The forecasting VAR
The rst step is to set up a VAR to capture the dynamic correlations between the excess
equity return and the real interest rate (calculated as the one-month bill yield minus the log
difference in the non-seasonally-adjusted CPI). The VAR must therefore include these two
variables at a minimum, plus whatever other variables that might be useful in forecasting
them. (One important constraint, of course, is that these variables are available in real
time.) We follow Campbell and Ammer (1993) in using a six-variable one-lag system
that included, besides the real rate and equity return: the relative bill rate (dened as the
three-month bill rate minus its 12-month lagged moving average), the change in the bill
rate, the (smoothed) dividend price ratio, and the spread between the 10-year and onemonth Treasury yields. For comparability with the Campbell-Ammer (1993) results, we
use January 1973 as the starting date for estimation.
3.2 A variance decomposition of equity returns
Equation (10) expresses the current months excess equity returns into three components,
which may be correlated with one another. The variance of the current excess return can
therefore be broken down into the sum of the three variances, plus (or minus) the relevant
three covariances,
y
y
Var(et +1 ) = Var(etd+1 ) + Var(etr+1 ) + Var(et +1 )
y
y
2Cov(etd+1 , etr+1 ) 2Cov(etd+1 , et +1 ) + 2Cov(et +1 , etr+1 ) ,
(14)
giving a sense of the relative contributions of news about real interest rates, dividends, and
expected future excess returns to uctuations in the current excess return. The results of this
17 A
useful check on the Campbell-Ammer procedure would be to compare its implied dividend forecasts
with the observed behavior of dividends. Such a comparison is beyond the scope of the present paper,
however.
26
decomposition appear in Table 10. For comparison, the table displays results for the full
19732002 sample and for the subsample beginning in May 1989, when the federal funds
futures data became available. The columns labeled total show the total contribution,
and those labeled share expresses that contribution as a percentage of the excess return
y
variance, i.e., normalizing by Var(et +1 ).
The results for the full 19732002 sample are similar to those reported by Campbell
and Ammer (1993) for their 197387 sample. In particular, the variance in expected future
excess returns accounts for the majority of the variance of the current equity return: 76%,
compared with Campbell and Ammers 101%. Dividends make a correspondingly larger
contribution of 24.5%, as opposed to Campbell and Ammers 14%. In both cases, the contribution of the real interest rate is negligible (0.3% and 3% respectively) and statistically
insignicant.
The 19892002 subsample yields somewhat different results, as shown in the right-hand
portion of the table. Considerably less variance is attributed to revisions in expectations of
future excess returns, and the dividend component now plays a somewhat larger role. The
main reason for this seems to be a decline in the forecastability of equity returns in recent
years, consistent with the observed fall in the adjusted R-squared from 0.04 to basically
zero. With returns less forecastable, the Campbell-Ammer methodology by default assigns
more of the excess return variance to dividend news.
3.3 The effects of federal funds surprises
The most straightforward way to analyze the impact of monetary policy within the framework introduced above is to include the federal funds surprises in the VAR as an exogenous
variable
zt +1 = Azt + itu+1 + wt 1
+
(15)
where is an n 1 vector capturing the contemporaneous response of the elements of
zt +1 to the unanticipated rate change period t + 1. The new disturbance term wt 1 is
+
27
by construction orthogonal to the funds rate surprise. This effectively breaks the VARs
one-month-ahead forecast error into a component having to do with news about monetary
policy, itu+1 and a component incorporating information about things other than policy.
Because itu+1 represents a prediction error from a rational forecast made at time t , it
should be orthogonal to zt .18 Consistent estimates of both A and can therefore be obtained
by rst estimating the VARs parameters, and then regressing the VARs one-step-ahead
forecast errors on the funds rate surprises. Normally, there would be no advantage to the
two-step procedure over simply estimating (15) directly. But in our case, using the two-step
procedure allows us to estimate the VAR dynamics (i.e., the coefcients in the A matrix)
over a sample longer than the period for which federal funds futures are available. 19 The
longer sample will of course tend to improve the estimates precision.
3.3.1 The dynamic response to funds rate surprises
Incorporating the federal funds surprises into the VAR in this way allows us to do two
things. First, because it extracts an orthogonal element from the wt forecast error, we can
use it to calculate the dynamic responses of the variables in the VAR to the orthogonal
component. The k-month response to a one-percentage-point surprise increase in the funds
rate can be calculated quite simply as Ak .
An obvious question to arise at this point concerns the relationship between these
futures-based funds rate surprises and the more familiar monetary policy shocks derived
from an identied VAR. The methods used to construct the one-month-ahead funds rate
forecasts differ, of course, with one using the futures markets implicit forecast, and the
other using a reduced-form econometric model. Forecast methodologies aside, however,
the orthogonalization procedure described above is conceptually equivalent to ordering the
federal funds rate rst in a VAR system. Since this precludes any contemporaneous re18 Krueger
and Kuttner (1996) showed that in practice, the federal funds futures prediction errors are generally uncorrelated with lagged information.
19
Faust et al. (2002) used a similar procedure. Specically, they estimate the VAR parameters over the full
sample, but choose an orthogonalization based on the response of interest rates over the post-1989 subsample.
28
action of the funds rate to economic news, the surprises calculated in this way may well
incorporate an endogenous policy response to information arriving within the month. Consequently, the impulse responses may represent the effects of things other than monetary
policy per se.
One way to minimize this problem would be to purge the futures-based funds rate surprises of any contemporaneous response to the economy by projecting them onto the relevant information variables, such as the data news obtained from the MMS survey. Alternatively, since the results above in section 2.7 indicate there has been little, if any, correlation
between the funds rate surprises and data news since 1994, the estimated only on the
post-1994 subsample should be relatively free from this endogeneity problem. This is the
approach taken in the results presented below.
The upper-left-hand panel of Figure 6 displays the dynamic response of excess returns
calculated in this way. The initial decline of 11.6% (not shown, because of the difference
in scale) is followed by another month of negative returns, and then by several months of
near-zero excess returns.20 After six months, equities begin to exhibit small positive excess
returns, peaking at 0.16% per month (1.9% at an annual rate), and continuing for a period
measured in years.
The contractionary funds rate surprise also leads to a sizable increase in the relative bill
rate, which persists several months (essentially by construction). The real T-bill rate rises
sharply at rst, but the increase is relatively short-lived, and all but disappears after four
months. In the near term, the dynamics of equity excess returns are dominated by the effects
of rising interest rates. But as these effects die out, the long-run effect of the dividend-price
ratio, which rises as a result of the fall in equity prices, reasserts itself. This leads to the
highly persistent, positive excess returns visible in the impulse response function.
20 This
11.6% response differs slightly from the results in section 2.7 because the dependent variable is the
forecast error in the log excess return, rather than the raw nominal return.
29
3.3.2 Explaining the stock markets reaction to Fed policy
The second thing this approach allows us to do is calculate the impact of the federal funds
surprises on the discounted sums of expected future excess returns, interest rates, and dividends. And since it is these sums that are related to the current excess return through
(10), this provides a natural way to determine the source (or sources) of the stock markets
reaction to monetary policy.
One way to assess policys effect on these discounted sums is simply to use the VAR to
y
calculate etd+1 , etr+1 , and et +1 , which represent the revisions in expectations of the relevant
present values, and regress these variables in turn on itu+1 . Although this would provide
the answer we are after, the standard errors would be misleading, as they would fail to take
into account the dependence of the es on the estimated parameters of the VAR.
An alternative way to do the same calculation is to write out the es in terms of the VAR
y
coefcients. Taking et +1 as an example:
y
et +1 = sy A(1 A)1wt +1 or
= sy A(1 A)1(itu+1 + wt 1 ) .
+
(16)
The response of the present value of expected future excess returns to the federal fundsrate
surprise is just
sy A(1 A)1 .
(17)
Thus, the response of expected future excess returns depends not only on the vector, but
also on the VAR dynamics represented by A. Similarly, the response of the present value
of current and expected future real returns is
sr (1 A)1 ,
30
(18)
and the implied response of the present value of current and expected future dividends is
sy + sy A(1 A)1 + sr (1 A)1
(19)
(sy + sr )(1 A)1 .
(20)
or alternatively
The standard errors for these responses are calculated using the delta method, as in Campbell and Ammer (1993).
The results of these calculations appear in Table 11. With the VAR estimated over the
entire 19732002 sample, funds rate surprises have a large, marginally signicant impact
on the discounted sum of future excess returns, accounting for just over half of the contemporaneous response excess returns, equal to 11.55. The reason for this large contribution
is readily understood in terms of the impulse responses plotted in Figure 6. Though small,
funds rate shocks are estimated to have a highly persistent positive effect on excess returns.
Discounting these future positive excess returns back using a discount factor near unity
yields a large negative impact on the current excess return. The 4.82 impact of funds
rate surprises on dividends is nearly as large as that of future excess returns, and it too is
signicant at the 0.10 level. The impact on the discounted sum of real rates is very small,
however, accounting for less than one percentage point of the excess return response.
The results are qualitatively similar when the VAR is estimated over the shorter 1989
2000 sample. The only noteworthy difference is the smaller impact on expected future
excess returns, which now account for a statistically insignicant 3.29 percentage points of
the 11.01% response. The reason for this can be traced to the smaller amount of longrun forecastability in excess returns in the post-1989 sample. In fact, the impulse response
functions from this truncated sample (not shown) are nearly identical to those for the full
19732002 sample, shown above. The main difference is that the response of the excess
return is negligible after six months or so, and it is this difference that accounts for the
smaller contribution of future excess returns.
31
4 Conclusions
This study has documented a relatively strong and consistent response of the stock market
to unexpected monetary policy actions, using federal funds futures data to gauge policy
expectations. For broad stock market gauges like the CRSP value-weighted index, an unexpected 25-basis-point rate cut would typically lead to an increase in stock prices on the
order of one percent. The result is robust to the exclusion of outliers and to the choice of
windows for measuring the stock markets response. There is some evidence of a larger
market response to policy changes that are perceived to be relatively more permanent, and
a smaller response to unexpected inaction on the part of the FOMC. We also nd that reactions to monetary policy surprises tend to differ across industry-based portfolios, with
the high-tech and telecommunications sectors exhibiting a response half again as large as
that of the broad market indices. Other sectors, such as energy and utilities, seem not to
be signicantly affected by monetary policy. The industry responses to monetary policy
changes seem broadly consistent with the predictions of the standard CAPM.
Although we have found an effect of monetary policy on the stock market of reasonable
size, we should emphasize that monetary policy surprises are responsible for only a small
portion of the overall variability of stock prices. Our method also does not allow us to
determine the role played by anticipated monetary policy in stock price determination.
Stocks are claims to real assets, so if monetary neutrality holds stock values should be
independent of monetary policy in the very long run. In the medium term, however, real
and nominal volatility induced by the form of the monetary policy rule may well inuence
stock values.
A more difcult question is why stock prices respond as they do to monetary policy.
We have tried to make progress on this question by asking whether monetary policy affects stock values through its effects on real interest rates, expected future dividends, or
expected future stock returns. The results presented in this paper showed, perhaps surprisingly, that the reaction of equity prices to monetary policy is, for the most part, not directly
attributable to policys effects on the real interest rate. This nding is the result of the
32
relatively transitory movements in real interest rates induced by surprise policy actions. Instead, the impact of monetary policy surprises on stock prices seems to come either through
its effects on expected future excess returns or on expected future dividends. (The exact
breakdown between these two channels depends somewhat on the choice of sample, which
appears to affect the long-horizon forecastability of excess returns.)
Economically, how should we interpret the result that monetary policy affects stock
prices in signicant part by affecting expected excess returns? Taken literally, this result
suggests that tight money (for example) lowers stock prices by raising the expected equity
premium. This could come about in at least two ways. First, tight money could increase
the riskiness of stocks directly, for example, by raising the interest costs or weakening the
balance sheets of publicly owned rms. Second, tight money could reduce the willingness
of stock investors to bear risk, for example by reducing expected levels of consumption,
as in Campbell and Cochrane (1999), or because of its association with higher ination,
as in Brandt and Wang (2003). These linkages open up the possibility of new ways in
which monetary policy may affect real activity for example, by affecting the level of
precautionary saving.
An alternative interpretation of our results is that the large movements in excess returns
associated with monetary policy changes reect excess sensitivity or overreaction of stock
prices to policy actions. A more tightly structured analysis that encompasses a wider class
of assets may help to differentiate these interpretations. In any case, further exploration of
the link between monetary policy and the excess return on equities is an intriguing topic
for future research.
33
Appendix: deriving equation 10
This appendix provides a brief sketch of the derivation of the log-linearized relationship
between the current excess return, expected future excess returns, dividend growth, and real
interest rates given in (10). The derivation roughly follows Campbell and Shiller (1988) and
Campbell (1991).
The starting point is simply the denition of the stock return, H t +1 :
1 + Ht +1
Pt +1 + Dt
Pt
(1)
where P is the stock price and D is the dividend. Taking logs and letting h t +1 = ln(1 + Ht +1 )
yields:
(2)
ht +1 = ln(Pt +1 + Dt ) ln(Pt ) .
The next step is to derive a log-linear approximation to ln(Pt +1 + Dt ). One way to do this
is to rst-difference, and express the change in the log of the sum as the weighted sum of
the log differences
(3)
ln(Pt +1 + Dt ) pt +1 + (1 )dt
where is the steady-state P/(D + P). Integrating this expression gives
ln(Pt +1 + Dt ) k + pt +1 + (1 )dt ,
(4)
substituting this into the expression for h t +1 , substituting t for dt 1 pt , and combining
terms gives
ht +1 k t +1 + t + dt
k + (1 L1 )t + dt .
(5)
(6)
The next step is to solve forward, giving
t = (1 L1 )1 (ht +1 dt k)
=
i(ht +1+i dt +i ) k/(1 ) .
i=0
34
(7)
(8)
Substituting this, and a similar expression for t +1 , into (5) and collecting terms yields:
i=1
i=0
ht +1 Et ht +1 = i (Et +1 Et )ht +1+i + i (Et +1 Et )dt +1+i
(9)
which corresponds to equation 1 in Campbell (1991).
A breakdown of excess returns can then be derived by expressing the equity return ht +1
as the sum of a risk-free rate and an excess return
ht +1 = rt +1 + yt +1 .
(10)
Because it is assumed that rt +1 is known at time t , the excess return surprise yt +1 Et yt +1
is the same as the overall return surprise ht +1 Et ht +1 . So the risk-free rate can be included
in the two-way breakdown as follows:
i=1
i=0
yt +1 Et yt +1 = i (Et +1 Et )(yt +1+i + rt +1+i ) + i (Et +1 Et )dt +1+i
(11)
or as
yt +1 Et yt +1 = i (Et +1 Et )yt +1+i
i=1
(Et +1 Et )rt +1+i + i(Et +1 Et )dt +1+i
i
i=1
i=0
.
(12)
Again, because Et rt +1 = rt +1 , it doesnt matter whether the summation involving the rs
y
begins at 0 or 1. Finally, letting et +1 represent the excess return surprise and replacing
the summations with the corresponding es yields (10).
35
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38
Table 1
Descriptive statistics
The table reports selected descriptive statistics for federal funds rate surprises and the CRSP
value-weighted equity return over the samples given in the column headings. All statistics
exclude the 17 September 2001 observation.
May 1989
January 1994
February 1994
December 2002
55
76
Standard deviation of
federal funds surprise, basis points
10.4
9.5
Standard deviation of equity return
on event days, %
0.80
1.26
Standard deviation of equity return
on non-event days, %
0.71
1.11
Proportion of rate changes
taking place at FOMC meetings
0.67
0.95
Number of events: rate changes
and FOMC meetings
39
Table 2
The response of equity prices to federal funds rate changes
The table reports the results from regressions of the one-day CRSP value-weighted equity
return on changes in the federal funds rate (columns a and c), and on the surprise and expected components of the funds rate change (columns b and d). All variables are expressed
in percentage terms. The full sample consists of the 55 target rate changes and the 77
FOMC meeting dates over the period from June 1989 through December 2002, excluding
the 17 September 2001 observation, for a total of 131 observations. The outliers excluded
from the regressions in columns c and d correspond to the six observations with inuence
statistics in excess of 0.3, leaving 125 usable observations. Parentheses contain t -statistics,
calculated using heteroskedasticity-consistent estimates of the standard errors.
Full sample
Excluding outliers
Regressor
(a)
(b)
(c)
(d)
Intercept
0.12
(1.35)
...
0.17
(2.14)
0.11
(0.31)
...
0.11
(1.37)
...
Expected change
0.23
(2.58)
0.61
(1.06)
...
Surprise change
...
R2
0.007
Raw funds rate change
1.04
(2.17)
4.68
(3.03)
0.171
40
...
0.007
0.67
(1.62)
2.55
(2.79)
0.049
Table 3
Tests for subsample stability and endogeneity
The table reports the results from regressions of the one-day CRSP value-weighted equity
return on the surprise and expected components of the change in the federal funds rate, all
expressed in percentage terms. The post-1994 dummy is set to 1 for observations beginning
with 4 February 1994. The employment dummy is set to 1 for pre-1994 observations when
a change in the target funds rate coincided with an employment release. The full and nooutlier samples are the same as those used for the results appearing in Table 2. Parentheses
contain t -statistics, calculated using heteroskedasticity-consistent estimates of the standard
errors.
Full sample
Excluding outliers
Regressor
(a)
(b)
(c)
(d)
Intercept
0.16
(1.80)
1.09
(2.26)
1.25
(1.14)
0.16
(1.76)
1.09
(2.24)
2.55
(1.70)
0.12
(1.43)
0.69
(1.68)
2.29
(2.28)
0.12
(1.43)
0.69
(1.67)
3.57
(3.77)
6.87
(3.59)
...
5.58
(2.61)
2.67
(1.82)
0.78
(0.45)
...
0.50
(0.29)
3.33
(2.55)
0.280
0.283
0.042
0.054
Expected change
Surprise change
Surprise change
post-1994
employment report
R2
41
Table 4
Tests for asymmetries
The table reports the results from regressions of the one-day CRSP value-weighted equity
return on the surprise and expected components of the change in the federal funds rate,
all expressed in percentage terms. The positive surprise dummy is set to 1 when the surprise change in the funds rate is greater than zero. The no rate change and positive rate
change dummies equal 1 when the funds rate target is unchanged or increased. The FOMC
meeting dummy is set to 1 for those observations coinciding with FOMC meetings. The
reversal dummy equals 1 for rate changes that reverse the direction of the previous change.
The post-1994 dummy is set to 1 for observations beginning with 4 February 1994. The
employment dummy is set to 1 for pre-1994 observations when a change in the target funds
rate coincided with an employment release. The full and no-outlier samples are the same as
those used for the results appearing in Table 2. Parentheses contain t -statistics, calculated
using heteroskedasticity-consistent estimates of the standard errors.
Full sample
Excluding outliers
Regressor
(a)
(b)
(c)
(d)
(e)
(f)
Intercept
0.02
(0.17)
0.84
(1.58)
7.57
(4.67)
0.12
(1.34)
1.56
(3.24)
8.34
(5.73)
0.14
(1.72)
1.03
(2.24)
3.97
(2.98)
0.12
(1.32)
0.72
(1.67)
3.26
(3.30)
0.12
(1.42)
0.97
(2.00)
4.49
(4.91)
0.13
(1.63)
0.72
(1.76)
3.67
(3.14)
7.05
(4.43)
7.39
(1.59)
...
8.11
(5.26)
...
2.54
(1.47)
...
4.14
(3.19)
...
3.46
(2.35)
...
...
3.05
(2.36)
0.34
(0.10)
...
...
...
...
4.25
(2.75)
6.33
(3.09)
4.61
(2.48)
...
4.00
(2.25)
0.58
(0.15)
...
...
...
...
...
0.053
0.065
Expected change
Surprise change
Surprise change
employment
positive surprise
positive rate change
...
FOMC meeting
...
10.42
(3.81)
3.05
(0.76)
...
reversal
...
...
post-1994
...
...
0.260
0.323
no rate change
R2
42
0.369
...
0.67
(0.39)
17.62
(4.08)
0.80
(0.44)
0.098
Table 5
The response of interest rate expectations to federal funds rate surprises
The table reports the results from regressions of the one-day change in the three-monthahead federal funds futures rate on the surprise and expected components of the change in
the federal funds rate, all expressed in percentage terms. The no rate change and positive
rate change dummies equal 1 when the funds rate target is unchanged or increased. The
FOMC meeting dummy is set to 1 for those observations coinciding with FOMC meetings.
The reversal dummy equals 1 for rate changes that reverse the direction of the previous
change. The full and no-outlier samples are the same as those used for the results appearing
in Table 2. Parentheses contain t -statistics, calculated using heteroskedasticity-consistent
estimates of the standard errors.
Regressor
(a)
(b)
(c)
(d)
Intercept
0.01
(1.46)
0.07
(2.10)
0.65
(13.37)
0.01
(1.55)
0.05
(1.32)
0.70
(14.71)
0.01
(1.34)
0.07
(2.29)
0.73
(14.54)
0.01
(1.40)
0.07
(2.08)
0.66
(12.83)
no rate change
...
...
...
FOMC meeting
...
0.36
(3.24)
...
...
reversal
...
...
0.21
(2.07)
...
0.726
0.745
Expected change
Surprise change
Surprise change
R2
43
0.744
0.12
(2.24)
0.727
Table 6
The stock market response to level versus timing surprises
The table reports the results from regressions of the one-day CRSP value-weighted equity
return on the surprise and expected components of the change in the federal funds rate,
and the timing surprise, all expressed in percentage terms. The timing surprise is dened
as the difference between the change in the three-month-ahead futures rate and the current
months surprise. The full and no-outlier samples are the same as those used for the results
appearing in Table 2. Parentheses contain t -statistics, calculated using heteroskedasticityconsistent estimates of the standard errors.
Full sample
Excluding outliers
Regressor
(a)
(b)
(c)
(d)
Intercept
0.12
(1.35)
1.05
(2.17)
4.68
(3.03)
...
0.09
(1.09)
1.34
(2.92)
6.20
(3.80)
4.29
(2.20)
0.11
(1.37)
0.67
(1.62)
2.55
(2.79)
...
0.09
(1.11)
0.94
(2.46)
4.17
(4.20)
4.27
(3.25)
Effect of pure
timing surprise
...
1.91
(0.91)
...
0.09
(0.08)
R2
0.171
Expected change
Surprise change
Timing surprise
0.192
44
0.049
0.085
Table 7
The impact of economic news on federal funds rate surprises
The table reports the results from regressions of the monthly federal funds rate surprise
on the unexpected components of the data releases listed in the row headings, over the
sample indicated in the column headings. Survey data gathered by Money Market Services
are used to calculate the data surprises. Asterisks denote statistical signicance based on
heteroskedasticity-consistent estimates of the standard errors: *** for the 0.01 level, ** for
the 0.05 level, and * for the 0.01 level.
Data surprise
Headline CPI
Core CPI
Headline PPI
Core PPI
Nonfarm payrolls
Industrial production
Retail sales
Retail sales, x autos
R2
R2
Full sample
Subsample
5/899/92
2/9412/02
0.016
0.058
0.001
0.085
0.203
0.069
0.031
0.023
0.124
0.012
0.027
0.304
0.624
0.136
0.061
0.093
0.010
0.152
0.024
0.022
0.009
0.028
0.035
0.021
0.128
0.082
0.454
0.304
0.087
0.012
45
Table 8
The monthly response of equity prices to federal funds rate surprises
The table reports the results from regressions of the one-month CRSP value-weighted equity return on the surprise and expected components of the one-month change in the federal funds rate, all expressed in percentage terms. The full sample includes 164 monthly
observations spanning May 1989 through December 2002. The no-outlier sample contains 154 observations. Parentheses contain t -statistics, calculated using heteroskedasticityconsistent estimates of the standard errors.
Full
sample
(a)
No
outliers
(b)
(c)
(d)
(e)
0.13
(0.32)
1.11
(0.37)
11.43
(3.95)
0.03
(0.09)
0.96
(0.35)
14.26
(5.43)
0.01
(0.02)
1.07
(0.36)
12.46
(3.69)
0.07
(0.16)
2.72
(0.72)
11.01
(3.46)
0.10
(0.24)
1.09
(0.36)
10.49
(2.53)
positive surprise
...
...
...
...
no rate change
...
...
...
positive rate change
...
...
...
reversal
...
...
...
4.88
(0.75)
6.59
(0.52)
...
post-1994
...
...
...
...
Employment surprise
...
...
...
...
R2
0.065
0.096
0.061
0.056
0.049
Standard error
4.28
3.85
4.30
4.30
4.31
Durbin-Watson statistic
2.02
2.09
2.02
2.02
2.03
Regressor
Intercept
Expected change
Surprise change
Surprise change
46
Tests for asymmetries
6.82
(0.63)
...
3.52
(0.50)
3.77
(0.50)
0.69
(0.10)
Table 9
The response of Fama-French industry portfolios to federal funds rate surprises
The table reports the results from regressions of the one-month returns on the Fama-French
industry portfolios indicated in the row headings on the surprise and expected components
of the one-month change in the federal funds rate, all expressed in percentage terms. The
regressions also include an intercept, whose coefcient is not reported. The full sample
includes 164 monthly observations spanning May 1989 through December 2002. Parentheses contain t -statistics, calculated using heteroskedasticity-consistent estimates of the
standard errors.
Response to federal funds rate changes:
Index
CRSP value weighted
Nondurables
Durables
Manufacturing
Energy
High tech
Telecommunications
Wholesale/retail
Health care
Utilities
Other
anticipated
unanticipated
1.11
(0.37)
0.85
(0.25)
1.47
(0.38)
2.02
(0.61)
0.20
(1.02)
0.06
(0.01)
0.35
(0.60)
4.75
(1.47)
1.04
(0.29)
1.24
(0.48)
1.21
(0.35)
11.43
(3.95)
9.65
(2.88)
12.45
(3.04)
8.82
(2.81)
4.03
(1.24)
14.73
(2.72)
16.10
(3.31)
11.97
(3.64)
8.04
(1.80)
5.42
(1.55)
11.08
(3.61)
47
R2
Market
beta
SE
DW
0.065
4.28
2.02
1
0.046
4.17
2.00
0.60
0.048
5.56
1.97
1.02
0.035
4.26
2.03
0.85
0.003
4.71
2.12
0.55
0.025
8.22
2.00
1.61
0.065
6.16
1.85
1.16
0.056
4.85
1.95
0.90
0.017
4.96
2.15
0.72
0.006
4.21
1.97
0.32
0.051
4.62
2.09
0.92
Table 10
A variance decomposition of excess equity returns
The table reports the decomposition of the variance of the current excess equity returns
into the variances of revisions in expectations of dividends, real interest rates, future excess
returns, and the covariances between these three components. The excess equity return
is the difference between the CRSP value-weighted return and the one-month Treasury
bill rate. A six-variable VAR(1) is used to construct forecasts of future real interest rates
and excess returns. The VAR includes the excess equity return, the real interest rate, the
relative bill rate (dened as the three-month bill rate minus its 12-month lagged moving
average), the change in the three-month bill rate, the smoothed dividend price ratio, and
the spread between the 10-year and one-month Treasury yields. Parentheses contain t statistics, calculated using the delta method.
19732002
Total
Var(excess return)
21.5
Var(dividends)
5.3
Var(real rate)
0.3
Var(future returns)
16.4
2 Cov(dividends, real rate)
0.4
2 Cov(dividends, future excess return)
2 Cov(future excess return, real rate)
Share (%)
0.2
0.2
R2 from excess return equation
Total
Share (%)
19.0
24.5
(6.2)
1.4
(2.4)
76.0
(1.8)
2.1
(0.8)
1.0
(0.0)
0.8
(0.1)
0.040
48
19892002
6.1
0.1
7.2
0.6
7.2
1.0
31.9
(1.8)
0.6
(1.5)
38.0
(1.2)
3.2
(0.7)
37.7
(2.3)
5.1
(1.1)
-0.003
Table 11
The impact of monetary policy on dividends, interest rates, and future returns
The table reports the impact of monetary policy surprises on the current excess equity
return, and the discounted sums of future excess equity returns, current and future real
interest rates, and current and future dividends. The six-variable VAR(1) used to construct
real interest rate and excess equity return forecasts is estimated over the sample indicated
in the column headings, and the contemporaneous response to the funds rate surprises
is estimated on the February 1994 to December 2002 subsample. Parentheses contain t statistics, calculated using the delta method.
Sample used for VAR
1/7312/02
5/8912/02
Current excess return
Future excess returns
Real interest rate
Dividends
11.55
(3.87)
6.10
(1.74)
0.64
(1.03)
4.82
(1.73)
49
11.01
(3.72)
3.29
(1.10)
0.77
(1.87)
6.96
(2.35)
6
1/3/2001
CRSP value-weighted return, %
5
4
4/18/2001
10/15/1998
8/21/1991
3
2
5/17/994
1
8/16/1994
0
-1
-2
-3
-4
-0.50
7/2/1992
FOMC meeting
Intermeeting
Employment report
Reversal
3/20/2001
-0.25
0.00
Federal funds rate surprise, %
0.25
Figure 1. Federal funds rate surprises and equity returns, daily data. The gure is a
scatterplot of one-day CRSP value-weighted equity returns against the surprise element of
changes in the federal funds rate, for the 131 event days in the sample. Observations are
distinguished by their association with FOMC meetings, intermeeting target rate changes,
the release of employment reports, and changes in the direction of rate movements (reversals). The six observations with boldface date labels are those agged as candidate outliers
on the basis of regression inuence statistics. The two observations with italicized date
labels are those associated with unusual announcements by the FOMC.
50
Number of observations
120
100
80
60
40
20
0
0.05
0.10
0.15
0.20
0.25
0.30
> 0.30
Upper bound of bin
Figure 2. Distribution of regression inuence statistics. The statistics are based on the
changes in the estimated parameters from a regression of one-day CRSP value-weighted
equity returns on the surprise and expected components of the federal funds rate change,
dropping each observation in turn from the sample.
51
Change in 3-month fed funds futures rate, %
0.25
1-for-1 effect on 3-month expectations
Greater than 1-for-1 effect
Less than 1-for-1 effect
Perverse effect
0.00
8 / 16 / 19 9 4
5 / 17 / 19 9 4
-0.25
- 0.50
-0.50
ts
fec n
ef tatio
r-1 ec
-fo exp
1
on
-0.25
0.00
0.25
Federal funds rate surprise, %
Figure 3. Federal funds rate surprises and funds rate expectations. The gure is a
scatterplot of one-day changes in the three-month-ahead federal funds futures rate against
the surprise element of changes in the federal funds rate, for the 131 event days in the
sample. Observations are distinguished according to whether the reaction of three-monthahead expectations are greater than, less than, equal to, or opposite in sign from the federal
funds rate surprise. The two observations with date labels are those associated with unusual
announcements by the FOMC.
52
15
CRSP value-weighted return, %
10
5
0
-5
-10
-15
-20
-0.7
candidate outliers
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
Federal funds rate surprise, %
Figure 4. Federal funds rate surprises and equity returns, monthly data. The gure is a
scatterplot of one-month CRSP value-weighted equity returns against the surprise element
of changes in the federal funds rate, for the 164 months in the sample. Ten candidate
outliers, identied on the basis of regression inuence statistics, are distinguished.
53
0
Estimated industry response, %
Energy
Utilities
-5
-10
Nondurables
High-tech
-15
Telecom
-20
-20
-15
-10
-5
0
Industry response implied by the CAPM, %
Figure 5. Estimated industry responses and CAPM implications. The gure depicts
the one-month responses of the Fama-French industry portfolios to a one percentage point
federal funds rate surprise. The values on the horizontal axis are the industry stock return
responses implied by the CAPM. The vertical axis values are the estimated industry return
responses reported in Table 9. The vertical lines represent the 80% condence intervals
associated with the estimated industry responses.
54
excess equity return
10-year to 1-month spread
0.30
0.00
0.00
-0.04
-0.30
-0.60
-0.08
-0.90
Initial response = -11.6%
-1.20
0
5
10
15
-0.12
20
0
real interest rate
5
10
15
20
dividend/price ratio
0.40
0.03
0.30
0.02
0.20
0.10
0.01
0.00
-0.10
0.00
0
5
10
15
20
0
change in bill rate
5
10
15
20
relative bill rate
0.09
0.09
0.06
0.06
0.03
0.03
0.00
0.00
-0.03
-0.03
0
5
10
15
20
0
5
10
15
20
Months following federal funds rate surprise
Figure 6. The dynamic responses of excess equity returns, interest rates, and the
dividend-price ratio to federal funds rate surprises. Each panel depicts the response of
the indicated variable to a one percentage point federal funds rate surprise. The contemporaneous response to the funds rate surprises is estimated on the February 1994 to December
2002 subsample. A six-variable VAR(1), estimated over the 19732002 sample, is used to
project the future path of each variable. Because of the large difference in scale, the initial
excess return response is not shown. Each variable is experessed in monthly percentage
terms.
55