more on monetary policy and stock price returns

CFA Institute

More on Monetary Policy and Stock Price ReturnsAuthor(s): J. Benson DurhamReviewed work(s):Source: Financial Analysts Journal, Vol. 61, No. 4 (Jul. - Aug., 2005), pp. 83-90Published by: CFA InstituteStable URL: http://www.jstor.org/stable/4480689 .Accessed: 27/09/2012 00:17

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Financial Analysts Journal X

Volume 610 Number 4I ?2005, CFA Institute

More on Monetary Policy and Stock Price Returns

J. Benson Durham

Recent research suggests a persistent empirical relation between U.S. monetary policy and stock returns since the mid-1980s. Thefindings seem questionable and incomplete, however, for at least three reasons. First, the results are sensitive to sample selection. Second, this research does not distinguish between anticipated and unanticipated monetary policy decisions. Third, such analysis does not satisfactorily consider that returns and policy are probably determined simultaneously because prices contain information about market expectationsfor the economy and, in turn, policy. Together, these issues suggest that investors are unlikely to profitfrom strategies based on past or anticipated Federal Reserve decisions.

N o one would argue that Federal Reserve policy is irrelevant to financial markets. A more focused question is whether past data on the prevailing stance of monetary

policy and stock prices imply that there is an anomaly that investors can exploit to realize excess returns. A cursory read of Conover, Jensen, Johnson, and Mercer (2005), which reports an empirical link between monetary policy cycles and stock returns since the mid-1980s, might prompt one to question the efficient market hypothesis (EMH) and accept the existence of such an anomaly.1 At least three issues prevent me from drawing such a conclusion from the data.

First, the findings are highly sensitive to sample selection. Second, the authors' dichotomous measure of the stance of monetary policy does not distinguish new from old information, which is absolutely necessary for detecting an anomaly. Third, unlike other recent academic research, the authors do not acknowledge that stock returns and monetary policy are probably determined simultaneously because stock prices contain information about investors' expectations for the economy and, in turn, Federal Reserve policy. Therefore, investors should view any result that is based on the assumption that policy is exogenous with some suspicion.

Short Literature Review A number of studies, including Conover, Jensen, and Johnson (1999), have reported an empirical link between U.S. monetary policy cycles and stock

returns. Their findings are effectively based on univariate regression models, such as

St= c + PRESTRICTt +t, (1)

where St is the local percentage gross stock return, RESTRICTt is a dummy variable equal to 1 if the prevailing local monetary regime is restrictive (0 if expansive), and Ft is an error term. "Restrictive" ("expansive") monetary policy cycles are defined as periods during which the most recent move in the Federal Reserve's discount rate is an increase (decrease).2 To net out "announcement effects," Conover et al. (1999) and others omitted observations from the sample during the first move in the cycle (i.e., the initial directional change in interest rates). Using data since the mid-1950s, they found a negative and statistically significant estimate for f3, which suggests that stock returns were lower (higher) during tightening (easing) cycles in the period studied.

When I divided the 45-year sample period and ran rolling regressions (Durham 2003b), the relation did not hold for recent data. In particular, the 1986-2000 period, which coincided with the final phase of Paul Volker's tenure as chairman of the Federal Reserve Board and the subsequent Alan Greenspan era, produced a statistically and eco- nomically insignificant estimate for P. Also, the overall finding largely vanished when I used other proxies for the stance of monetary policy, considered excess instead of gross stock returns, and ran panel regressions covering 16 countries.3

Conover et al.'s 2005 rejoinder to my critique addressed only my findings about time-series inconsistency. They used daily instead of monthly data, specifically studied the 1986 through 2000 period that I examined, and found an annual return

J. Benson Durham is a senior economist at the Board of Governors of the Federal Reserve System, Washing- ton, DC.

July/August 2005 This article qualifies for 1 PD credit. www.cfapubs.org 83

Financial Analysts Journal

difference between periods of 12.8 percentage points a year, with a p-value of 0.100. They also reported more sizable coefficients for some investment styles, industries, and international indices, and they concluded that monetary policy "has had and continues to have a strong relationship with security returns" (p. 78).

I will momentarily suspend the unanswered aspects of my initial study and focus on their new findings. To begin on a comparable footing, I used daily data to replicate their results and regressed percentage returns to the Wilshire 5000 Index on their dummy variable for restrictive versus expansionary monetary policy periods for 3 January 1986 through 2 January 2001.4 Regression 1 in Table 1 reports a more statistically robust result than in Conover et al., as indicated by the p-value for the dummy variable of 0.089, which is below their 0.100 estimate. Also, the coefficient suggests that daily returns were, on average, about 5.7 bps lower per day during restrictive monetary policy periods. Indeed, the size of the estimate merits a closer look.

Sample Selection When I dug a little deeper, the result did not seem very sturdy. Consider one possible outlier that might drive the finding-the stock market crash of 19 October 1987, which occurred during a restrictive monetary policy period. The cause of the approximate 20 percent decline of broad indices on that single day has puzzled financial economists and practitioners alike, but few would attribute the crash to the inception of Federal Reserve tightening more than a month earlier (on 9 September). None- theless, this critical observation informs the authors' estimates, and to address the sensitivity of their results, I included a simple dummy for Black

Monday in Regression 2. As the second column in Table 1 shows, the coefficient for the restrictive dummy variable in this specification became statistically insignificant, with a p-value of 0.159. Also, the economic significance, although perhaps still notable, declined to about 4.5 bps.

Consider also an extension of the Conover et al. sample to include the most recent expansionary cycle of 3 January 2001 through 28 June 2004. To assess whether Federal Reserve policy is still relevant today, the most recent data are quite important. To that end, for Regression 3, I extended the sample through 28 June 2004. As Table 1 shows, these results cast further doubt on previous findings, as the p-value increased substantially, well outside any possible criterion for statistical inference. Moreover, the economic significance fell to about 2.5 bps.

A final important sample consideration is that the communication practices of the Federal Reserve have evolved over time, which researchers need to consider in assessing the time invariance of any finding that relates monetary policy to asset prices. Before its 4 February 1994 meeting, the Federal Open Market Committee (FOMC) did not publicly announce changes in its monetary policy stance, and the Federal Reserve has revised its communica- tions policy periodically since then.5 Even though market participants could ultimately decipher monetary policy during, say, Volker's tenure, most com- mentators would consider early 1994 something of a watershed in Federal Reserve disclosure practices. Therefore, in considering whether monetary policy is relevant today, the period since February 1994 may be more informative than earlier periods, even at the price of fewer observations and limited vari- ation in the dependent variable of interest.

Table 1. Sample Sensitivity Analysis (p-values in parentheses)

Regression 1 Regression 2 Regression 3 Regression 4 Regression 5 (3/Jan/86- (3/Jan/86- (3/Jan/86- (4/Feb/94- (4/Feb/94-

Independent Variable 2/Jan/01) 2/Jan/01) 28/Jun/04) 2/Jan/01) 28/Jun/04)

Conover et al. restrictive dummy -0.0573 -0.0453 -0.0251 -0.0302 0.001(

(0.089)* (0.159) (0.436) (0.539) (0.983) Dummy for 19 October 1987 -17.9096 -17.9096

(0.000)*** (0.000)***

Constant 0.0699 0.0699 0.0497 0.0650 0.0338

(0.001)*** (0.001)*** (0.008)*** (0.047)** (0.192)

Observations 3,587 3,587 4,426 1,675 2,514

R 2 0.001 0.091 0.066 0.000 0.000

*Significant at the 10 percent level. **Significant at the 5 percent level. 'Significant at the 1 percent level.

84 www.cfapubs.org ?2005, CFA Institute


For this reason, I ran Regression 4 to cover the period from 4 February 1994 through 2 January 2001 and Regression 5 to cover the period from 4 February 1994 through 28 June 2004. The results, shown in Table 1, are clear. Not only does the coefficient from Regression 5 suggest, perversely, that daily returns were about a tenth of a basis point greater during restrictive periods, but also neither regression produced an estimate within standard confidence intervals. Therefore, the results found by Conover et al. did not hold when I reproduced their study for the modern era of Federal Reserve communication practices.

Of course, one can assail any seemingly robust statistical result with changes in the sample and torture the data until they confess.6 But these sample considerations, especially the increase in observations to include data past early 2001, seem to produce a more rigorous test of the hypothesis. Either way, one might wish to know the effects of outliers and additional information before accept- ing the inference that the "relationship between returns and monetary policy remains strong in more recent periods" (Conover et al., p. 75).

Anticipated vs. Unanticipated Policy A more substantive question for practitioners is: Do the results in Conover et al. violate the EMH and identify an anomaly that investors can exploit today? I have serious doubts because their measure of restrictive versus expansionary periods does not distinguish monetary policy surprises (new information) from prevailing or expected monetary policy (old information). The authors excluded the first two days of a new cycle to net out "announcement effects," but this adjustment is inadequate and ignores subsequent surprises related to either the length or the magnitude of the ensuing episode.7

How can researchers distinguish between policy surprises and the prevailing or expected stance of the Federal Reserve? With trading in federal funds futures contracts on the Chicago Board of Trade since October 1988, we can measure the surprise component of monetary policy decisions ex post over a given interval as the difference between the realized federal funds rate at time t and the observed futures price at time t - 1. Given a daily frequency, the daily change in, say, the one-month- ahead futures contract on days of FOMC meetings or policy actions is a good measure of the revision of near-term monetary policy expectations in the financial markets.8 A regression equation that simultaneously controls for surprise components of

new policy decisions and includes the Conover et al. restriction dummy isolates the effects of new and old information on prices. If the Conover et al. dummy variable were robust after controlling for policy surprises, it would constitute a blow against the EMH.

The results of this approach, provided in Table 2, generally suggest that if monetary policy affects stock prices at all, it is new, not old, information that is important.9 Put differently, all available data imply no anomaly regarding past or anticipated policy and stock returns. Turning to the details, note that Regression 6 had the same simple specification as Regression 1 in Table 1 to replicate the base result as closely as possible, but the sample began on 5 October 1988, when data on federal funds futures became available.10 Perhaps again indicating the sensitivity of the results to sample selection, the coefficient for the restrictive dummy variable has the anticipated sign but is statistically insignificant.

The same is true for Regression 7, which controlled for surprises, and for Regression 8, which additionally controlled for the surprise component of economic data releases (defined as the difference between the actual data and the forecast survey median from Money Market Services prior to the release).11

The coefficient for monetary policy surprises was not statistically significant in Regressions 7 or 8. But when I used all available data from 5 October 1988 through 28 June 2004 in Regression 9, it became safely robust, and the coefficient suggests that a 1 percentage point surprise increase in the near-term expected target federal funds rate implies about a 3.2 percentage point drop in stock prices. That is, a surprise 25 bp increase in the target corresponds to about an 80 bp decline in the stock market, on average, which seems to be of only moderate magnitude in light of overall equity market volatility.12 At the same time, the restrictive dummy variable from Conover et al. remained clearly insignificant.

To address the issue of modern Federal Reserve communication practices, Regression 10 considered only data since February 1994.13 The coefficient for monetary policy surprises remained highly significant and was of almost double the magnitude of the result from Regression 9, whereas the coefficient for the restrictive dummy variable was perversely positive and again clearly statistically insignificant. Therefore, in brief, the results indicate that unanticipated rather than anticipated monetary policy moves potentially matter for stock returns. The magnitude of even the surprise effect, however, is limited.

July/August 2005 www.cfapubs.org 85


Table 2. Monetary Policy Surprises (p-values in parentheses)

Regression 6 Regression 7 Regression 8 Regression 9 Regression 10 (5/Oct/88- (5/Oct/88- (5/Oct/88- (5/Oct/88- (4/Feb/88-

Independent Variable 2/Jan/01) 2/Jan/01) 2/Jan/01) 28/Jun/04) 28/Jun/04)

Conover et al. restrictive dummy -0.0367 -0.0354 -0.0379 -0.0108 0.0040

(0.273) (0.292) (0.259) (0.753) (0.934)

Monetary policy surprise, one month out -1.2188 -0.6330 -3.1629 -6.3139

(0.268) (0.573) (0.002)*** (0.000)***

Nonfarm payroll -0.0017 -0.0011 -0.0021

(0.010)*** (0.094)* (0.013)**

Retail sales -0.2480 -0.1014 0.0034

(0.156) (0.395) (0.985)

Leading indicators 0.7008 0.3212 -0.2576

(0.112) (0.450) (0.739)

Factory orders -0.2010 -0.1262 -0.2098

(0.116) (0.284) (0.228)

Industrial production -0.3530 -0.0839 -0.2850

(0.372) (0.823) (0.615)

Housing starts -1.2225 -0.7080 -1.2329

(0.217) (0.453) (0.327)

Capacity utilization 0.3837 0.3150 0.7233

(0.220) (0.313) (0.172)

Initial unemployment claims 0.0002

(0.950)

Retail sales (ex autos) -0.1526

(0.626)

Consumer Price Index (ex energy) -3.7150

(0.001)***

Producer Price Index (ex energy) -0.4293

(0.283)

Consumer confidence 0.0172

(0.398)

Constant 0.0644 0.0629 0.0613 0.0354 0.0240

(0.003)*** (0.004)*** (0.005)*** (0.070)* (0.357)

Observations 2,886 2,886 2,886 3,713 2,476

R 2 0.000 0.001 0.007 0.005 0.015

*Significant at the 10 percent level. **Significant at the 5 percent level.

***Significant at the 1 percent level.

The lack of robust anomalies in the broad market index hardly motivates rummaging through investment styles and industrial sectors. But to address similar analyses in Conover et al., Table 3 reports findings from regressions that used the specifications and data for Regression 10 in Table 2 within and between 6 equity investment styles and 11 industrial sectors.14

This part of the study addressed two questions. First, do any of these 17 time series of stock returns correlate with the prevailing stance of monetary policy when the surprise components of Fed- eral Reserve announcements (and economic news

releases) are controlled for? Regressions 1 through 17 clearly suggest that they do not; no model produced a statistically significant estimate of the Conover et al. dummy variable, whereas 7 of 17 models produced significant coefficients for monetary policy surprises. Second, do the time series of cross-sectional differences in style and industrial subcategories, all else being equal, exhibit such a relation? Regressions 18 through 87 suggest that the answer is no. Of the 70 possible cross-sectional differences, only one is statistically significant at the 5 percent level.15 (Two more are statistically significant at the 10 percent level.) These results



Table 3. Time Series of Alternative Indices and Time Series of Cross-Sectional Differences Restrictive Dummy Monetary Policy Surprise

Regression f p-Value f3 p-Value

A. Time-series regressions

1 Small cap/value 0.0346 0.5529 -3.5166 0.0881*

2 Small cap/blend 0.0279 0.4859 -1.0203 0.4714

3 Small cap/growth 0.0073 0.8445 -0.1841 0.8884

4 Large cap/value 0.0162 0.7568 -5.4989 0.0029***

5 Large cap/blend 0.0193 0.6594 -1.8615 0.2300

6 Large cap/growth 0.0312 0.4582 -1.3559 0.3630

7 Technology 0.0255 0.7913 -15.9012 .0000***

8 Telecommunications -0.0278 0.6754 -2.6506 0.2670

9 Health care 0.0875 0.1115 -1.0846 0.5840

10 Basic materials 0.0111 0.8461 -5.9429 0.0040***

11 Energy -0.0107 0.8573 -0.4048 0.8496

12 Industrials 0.0213 0.6895 -8.5709 0.0000***

13 Utilities 0.0669 0.1628 -0.5993 0.7285

14 Consumer cyclical -0.0202 0.7068 -8.7692 0.0000***

15 Retail -0.0948 0.7546 -8.6606 0.4284

16 Consumer noncyclical 0.0266 0.6781 0.1100 0.9621

17 Financial 0.0241 0.6773 -6.3230 0.0025***

B. Time-series regressions of cross-sectional differences

18 Small cap/value-Small cap/blend 0.0067 0.7895 -2.4963 0.0048***

19 Small cap/value-Small cap/growth 0.0273 0.3673 -3.3325 0.0019***

20 Small cap/value-Large cap/value 0.0184 0.6081 1.9823 0.1188

21 Small cap/value-Large cap/blend 0.0153 0.7070 -1.6551 0.2494

22 Small cap/value-Large cap/growth 0.0033 0.9394 -2.1607 0.1632

23 Small cap/blend-Small cap/growth 0.0206 0.0536* -0.8363 0.0270**

24 Small cap/blend-Large cap/value 0.0117 0.7219 4.4785 0.0001***

25 Small cap/blend-Large cap/blend 0.0086 0.7602 0.8412 0.3978

26 Small cap/blend-Large cap/growth -0.0033 0.9102 0.3356 0.7496

27 Small cap/growth-Large cap/value -0.0089 0.8014 5.3148 0.0000***

28 Small cap/growth-Large cap/blend -0.0120 0.6662 1.6774 0.0895*

29 Small cap/growth-Large cap/growth -0.0240 0.3935 1.1719 0.2386

30 Large cap/value-Large cap/blend -0.0031 0.9053 -3.6374 0.0001***

31 Large cap/value-Large cap/growth -0.0151 0.6550 -4.1429 0.0005*$*

32 Large cap/blend-Large cap/growth -0.0119 0.5502 -0.5056 0.4743

33 Technology-Telecommunications 0.0533 0.5276 -13.2506 0.0000***

34 Technology-Health care -0.0620 0.4807 -14.8166 0.0000***

35 Technology-Basic materials 0.0144 0.8741 -9.9583 0.0023***

36 Technology-Energy 0.0362 0.7166 -15.4964 0.0000***

37 Technology-Industrials 0.0042 0.9505 -7.3303 0.0027***

38 Technology-Utilities -0.0413 0.6686 -15.3019 0.0000***

39 Technology-Consumer cyclical 0.0457 0.5340 -7.1320 0.0071***

40 Technology-Retail 0.1204 0.6952 -7.2406 0.5131 41 Technology-Consumer noncyclical -0.0011 0.9909 -16.0112 0.0000*** 42 Technology-Financial 0.0014 0.9862 -9.5782 0.0010*** 43 Telecommunications-Health care -0.1152 0.0713* -1.5661 0.4963

(continued)



Table 3. Time Series of Alternative Indices and Time Series of Cross-Sectional Differences (continued)

Restrictive Dummy Monetary Policy Surprise

Regression , p-Value 3 p-Value

44 Telecommunications-Basic materials -0.0389 0.5654 3.2923 0.1769

45 Telecommunications-Energy -0.0171 0.8158 -2.2459 0.3958

46 Telecommunications-Industrials -0.0491 0.3713 5.9202 0.0028***

47 Telecommunications-Utilities -0.0946 0.1512 -2.0514 0.3878

48 Telecommunications-Consumer cyclical -0.0075 0.8935 6.1186 0.0025***

49 Telecommunications-Retail 0.0671 0.8260 6.0099 0.5849

50 Telecommunications-Consumer noncyclical -0.0544 0.4757 -2.7607 0.3152

51 Telecommunications-Financial -0.0519 0.3766 3.6723 0.0823*

52 Health care-Basic materials 0.0763 0.2027 4.8584 0.0245**

53 Health care-Energy 0.0981 0.1168 -0.6798 0.7630

54 Health care-Industrials 0.0662 0.1663 7.4863 0.0000***

55 Health care-Utilities 0.0206 0.7227 -0.4853 0.8166

56 Health care-Consumer cyclical 0.1077 0.0223** 7.6847 0.0000***

57 Health care-Retail 0.1823 0.5458 7.5760 0.4862

58 Health care-Consumer noncyclical 0.0608 0.3318 -1.1946 0.5969

59 Health care-Financial 0.0634 0.2022 5.2384 0.0034***

60 Basic materials-Energy 0.0218 0.7224 -5.5382 0.0122**

61 Basic materials-Industrials -0.0102 0.8234 2.6279 0.1098

62 Basic materials-Utilities -0.0557 0.3514 -5.3437 0.0132**

63 Basic materials-Consumer cyclical 0.0314 0.5136 2.8263 0.1024

64 Basic materials-Retail 0.1060 0.7254 2.7176 0.8027

65 Basic materials-Consumer noncyclical -0.0155 0.8328 -6.0530 0.0222**

66 Basic materials-Financial -0.0130 0.8005 0.3800 0.8373

67 Energy-Industrials -0.0320 0.5898 8.1661 0.0001***

68 Energy-Utilities -0.0775 0.1930 0.1945 0.9278

69 Energy-Consumer cyclical 0.0096 0.8760 8.3645 0.0002***

70 Energy-Retail 0.0842 0.7826 8.2558 0.4528

71 Energy-Consumer noncyclical -0.0373 0.6226 -0.5148 0.8505

72 Energy-Financial -0.0348 0.5819 5.9182 0.0093***

73 Industrials-Utilities -0.0455 0.4120 -7.9716 0.0001***

74 Industrials-Consumer cyclical 0.0415 0.1960 0.1983 0.8640

75 Industrials-Retail 0.1162 0.6990 0.0897 0.9934

76 Industrials-Consumer noncyclical -0.0053 0.9340 -8.6809 0.0002***

77 Industrials-Financial -0.0028 0.9414 -2.2479 0.1013

78 Utilities-Consumer cyclical 0.0871 0.1219 8.1700 0.0001***

79 Utilities-Retail 0.1617 0.5946 8.0613 0.4617

80 Utilities-Consumer noncyclical 0.0402 0.5689 -0.7093 0.7805

81 Utilities-Financial 0.0427 0.4479 5.7237 0.0048***

82 Consumer cyclical-Retail 0.0746 0.8023 -0.1087 0.9919

83 Consumer cyclical-Consumer noncyclical -0.0469 0.4613 -8.8793 0.0001***

84 Consumer cyclical-Financial -0.0443 0.2519 -2.4463 0.0795*

85 Retail-Consumer noncyclical -0.1215 0.6897 -8.7706 0.4239

86 Retail-Financial -0.1190 0.6929 -2.3376 0.8295

87 Consumer noncyclical-Financial 0.0025 0.9697 6.4330 0.0074***

Note: The right-hand side and the sample period of each regression followed Regression 10 in Table 2. *Significant at the 10 percent level.

**Significant at the 5 percent level. ***Significant at the 1 percent level.



hardly recommend that investors conduct thor- ough style or industry analysis vis-'a-vis the anticipated stance of monetary policy.16

Simultaneity Bias Another potential methodological issue simultaneity bias-raises questions about the findings of Conover et al. and a number of studies. Just as monetary policy affects financial asset prices, the prices of financial assets, such as stocks, probably affect monetary policy. To be sure, cen- tral banks do not target asset prices explicitly. The issue is more subtle. Stock prices contain valuable information about investors' expectations regarding the course of the economy and, in turn, monetary policy.17 In technical terms, the potential joint determination of stock prices and monetary policy requires estimation techniques other than standard ordinary least squares.

Researchers have begun to address this issue. Efforts include a simple application of two-stage least squares in an error-correction framework (Durham 2003a) and a much more robust identifi-

cation strategy based on heteroscedasticity (Rigo- bon and Sack 2003). Canonical, stylized facts to guide practitioners have not emerged from this literature, but until they do, investors should view suspiciously any result that is based on the assumption that monetary policy is exogenous.18 Perhaps practitioner research should focus on this area.

Conclusions So, is Federal Reserve policy still relevant for investors? No and yes. Old information does not move stock prices. New information seems to affect equity returns, but considering overall stock market volatility, the impact is not substantial. If the data are any guide, portfolio managers are unlikely to profit from trading strategies based on past and anticipated Federal Reserve policy decisions.

I thank Stefania D'Amico, Refet Gurkaynak, and Jonathan Wright. My views do not necessarily reflect those of the Board of Governors of the Federal Reserve System or any other member of its staff.

Notes 1. Unless noted otherwise, reference to Conover et al. is to the

2005 article. 2. This specification might incidentally capture the more gen-

eral effects of business cycles to some extent because required returns are perhaps higher (lower) during reces- sions (expansions).

3. The point about using excess instead of gross stock returns is crucial. The EMH implies that excess, not gross, returns are unpredictable, and the risk-free rate may or may not be different, on average, between tightening and easing cycles. Therefore, Conover et al. and previous papers are silent on the critical question of stock return predictability.

4. I used daily data grudgingly; I do not agree that higher- frequency data make the estimates in this particular application more precise. Although use of daily data increases the sample size, it also increases the error variance, and because daily returns are more volatile than monthly returns, daily data provide no clear gain in estimation efficiency.

5. The most recent such change occurred in January 2005 with the early release of the FOMC minutes.

6. Conover et al. suggested that excluding one cycle in the 1990s, Period 19 (the restrictive period of 19 May 1994 to 30 January 1996) in their study, made their results more robust. I saw no persuasive reason to exclude that period (and do not concur that it is a special episode that merits exclusion from the analysis). However, when I made a similar omission but included data through 28 June 2004 and/or excluded data before 4 February 1994, their measure was clearly statistically insignificant. Results are available on request.

7. In practice, investors who strictly follow the strategy implied by Conover et al. would be stuck holding the easing (tightening) portfolio on the day of the first tightening (easing) that ended the cycle. I thank Jonathan Wright for pointing this out. A couple of cycles are notable in regard

to subsequent surprises related to either the length or the magnitude of the ensuing episode. Interest rates increased by considerably more than market participants had expected before the 1994 tightening cycle, and conversely, interest rates fell by more than investors had anticipated before the easing cycle that began in 2001.

8. In my study, if the FOMC meeting was in June, for example, I used the July federal funds futures contract. This common strategy is similar to the one in Kuttner (2001). Also, follow- ing conventions, for periods prior to 1994 when the FOMC did not announce changes in the target and market participants inferred policy changes from open-market operations, I used the change in futures rates on the day after the FOMC meeting. I considered all intermeeting policy actions of at least 25 bps.

9. This finding is consistent with Bernanke and Kuttner (2005). 10. Because data on federal funds futures contracts were not

available until October 1988, I also calculated monetary policy surprises based on the front-month Eurodollar futures contract, which has a 0.868 correlation with the federal funds futures measure. A regression using data over the precise sample in Conover et al. that included the Euro- dollar-based surprise measure (and a dummy for 19 October 1987) also produced an insignificant estimate for the authors' restrictive dummy (p-value of 0.189). Results are available on request.

11. Conover et al. did not acknowledge that factors other than monetary policy affect stock returns, despite a large literature on other possible determinants of stock market perfor- mance. To partially address this issue, I used all major economic news releases for which data were available for the entire sample under consideration.

12. This result lies at the lower range of estimates reported in Bernanke and Kuttner.



13. Regression 10 included more data releases than Regression 9 because data were not sufficient to calculate the surprise component for some releases prior to 1994.

14. I used Kenneth French's portfolios (available at mba.tuck. dartmouth.edu/pages/faculty/ken.french) but used industrial classifications from Dow Jones and Company.

15. With N categories, there are [N(N - 1)]/2 possible "cross- sectional" differences. Therefore, with 6 investment styles and 11 industrial categories, I ran 70 regressions.

16. Conover et al. examined five non-U.S. equity indices, but they did not show that Federal Reserve policy affects non- U.S. stocks beyond its purported effect on U.S. equities (or local monetary policy), which the analysis here suggests is highly fragile.

17. In addition, stock prices potentially influence household expenditures, via wealth effects, and affect the ability of

companies to raise funds in the capital markets. These conditions, in turn, weigh on monetary policy decisions (see D'Amico and Farka 2003).

18. A slight caveat is instructive. If one considers a sufficiently narrow interval around FOMC announcements, one might safely assume that monetary policy is exogenous in the general absence of other incoming news on the economy. Such an assumption becomes increasingly tenuous, however, as the frequency of the data decreases and the window around the policy decision widens. Even the daily measures of policy surprises that I used for Tables 2 and 3 are clearly imperfect because other news unrelated to monetary policy released on those days might also affect asset prices. Nota- bly, the temporal persistence of the Conover et al. dummy variable is perhaps hopelessly endogenous in this regard.

References Bernanke, B., and K. Kuttner. 2005. "What Explains the Stock Market's Reaction to Federal Reserve Policy?" Journal of Finance, vol. 60, no. 3 (June):1221-57.

Conover, C.M., G.R. Jensen, and R.R. Johnson. 1999. "Monetary Conditions and International Investing." Finanzcial Analysts Journal, vol. 55, no. 4 (July/August):38-48.

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D'Amico, S., and M. Farka. 2003. "The Fed and the Stock Market: An Identification Based on Intra-Day Futures Data." Working paper.

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Kuttner, K. 2001. "Monetary Policy Surprises and Interest Rates: Evidence from the Fed Funds Futures Market." Journacll of Monetary Economics, vol. 47, no. 3 (June):523-544.

Rigobon, R., and B. Sack. 2003. "Measuring the Reaction of Monetary Policy to the Stock Market." Quarterly Journal of Economics, vol. 118, no. 2 (May):639-669.


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