Market timing by global fund managers

Journal of International Money and Finance 25 (2006) 1029e1050 www.elsevier.com/locate/jimf

Market timing by global fund managers Debra A. Glassman a, Leigh A. Riddick b,* a b

University of Washington Business School, Box 353200, Seattle, WA 98195, USA Kogod School of Business, American University, 4400 Massachusetts Avenue, N.W., Washington, DC 20016-8044, USA

Abstract We analyze the market timing ability of US global equity fund managers in the late 1980s and early 1990s, before hedge funds became prominent in global investing. We examine both portfolio weights and returns to distinguish between world market timing (movements of funds between all equity markets and cash) and national market timing (movements out of one country’s equity market into one or more other countries’ equities). We find no evidence of world market timing, but do find evidence of national market timing. Earlier papers examining multi-country fund management find very little evidence of market timing ability, but they do not explicitly consider the difference in world and national timing. Our results suggest that it may be important to capture the distinction between the two types of global timing activity. Our methodology and results may also be pertinent to domestic papers that examine simultaneous movements between asset classes and cash. Ó 2006 Elsevier Ltd. All rights reserved. JEL classification: G11 Portfolio Choice Keywords: Market timing; Asset allocation; Portfolio performance; International portfolio performance

1. Introduction This paper provides evidence on market timing by managers of US global investment funds. An important characteristic of our analysis is that we distinguish between two types of global market timing: world market timing, i.e., a general movement into or out of cash relative to all * Corresponding author. Tel.: þ1 202 885 1944; fax: þ1 202 885 1946. E-mail address: [email protected] (L.A. Riddick). 0261-5606/$ - see front matter Ó 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.jimonfin.2006.08.007

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countries’ equity assets; and national market timing, i.e., the reallocation of funds among various countries’ equity markets based on expectations about relative market strength. We focus on major market moves in the period 1985e1990 because hedge funds were not yet an important player in the market. We assess timing by searching for patterns in both equity portfolio weights and equity portfolio returns for a sample of US global fund managers. We find no evidence of world timing but we do find evidence for national timing in our sample. It is significant that we find evidence of market timing ability when the great majority of both domestic and international portfolio performance papers have found little support for any timing ability, despite examining many types of funds with many different techniques. Recent examples include Becker et al. (1999), who study US mutual funds; Fletcher (1995) and Blake et al. (1999), who study UK pension fund data; Fung et al. (2002), who study international hedge funds; and Kao et al. (1998), who study international mutual funds.1 Our results suggest that considering both world and national timing measures can capture timing ability that may otherwise go undetected. The intuition for this interpretation is that a test that explicitly distinguishes between the two can capture the fact that global managers are simultaneously moving money from country to country while considering cash as a safe alternative. Examining weights allows us to both separate movements of funds between national equity markets from movements out of all countries into cash, and to easily consider relative movements between countries. Using weights for statistical tests of portfolio positions also requires no assumptions about either the underlying model that drives asset returns or related benchmark portfolios. Since results in timing studies can be very sensitive to both (see, e.g., Grinblatt and Titman, 1993), this is a strength of the approach.2 However, any portfolio analysis without a benchmark portfolio for comparison is subject to questions about its ability to differentiate between luck and skill. Since it is not possible to address both benchmark sensitivity and skill with one approach, we also examine portfolio returns for a sample of US global mutual fund managers for the same period, using a benchmark model. In this second analysis we measure timing ability by extending the standard single world index timing model (our world timing scenario) to accommodate movements between countries (our national timing scenario).3 We examine all months in our sample in both the national and world timing analyses, but we are most interested in examining periods around the large market moves during our sample period: the October crash of 1987, the October crash of 1989, and the drop in Japan that began in January 1990. While we find evidence of market timing in other periods, our strongest evidence supports the hypothesis that managers moved out of the Japanese market and into other countries just prior to Japan’s dramatic drop in early 1990. However, the managers did not make

1

One recent exception is Bollen and Busse (2001) who find some evidence of daily timing ability when monthly timing ability is negligible during the same period. Bange et al. (2004) also find some evidence of strategic asset allocation skill with quarterly data prior to the 1997 crash, but not after. 2 While Grinblatt and Titman (1993) also provide a benchmark-free analysis, our research question and methodology necessarily differ. They examine the average covariance between returns and portfolio weights over an entire sample period, to examine average performance. We examine individual asset weights, alone, but for specific points in a time series, and thus require a different methodology than that in Grinblatt and Titman. 3 A number of prior studies use a single world benchmark to assess timing ability, but do not consider movements between countries (see, e.g., Eun et al., 1991; or more recently Kao et al., 1998; and Fung et al., 2002). While, as we will show, single index models can capture world market timing ability, they do not provide a good benchmark unless purchasing power parity (PPP) holds, and it is well established that PPP does not hold (see Glassman and Riddick, 1996).

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strategic moves prior to the worldwide 1987 and 1989 market crashes that affected most markets around the world. Recent work has raised issues about the usefulness of international diversification because of increases in correlation, both generally over time and during extreme market moves such as those we examine here. For example, Longin and Solnik (1995, 2001) show that conditional correlations have risen over the past 30 years, and that correlations rise during periods of high volatility, particularly in bear markets. These documented increases in correlations are widely believed to have decreased diversification opportunities in international investing, which makes it more difficult for managers to successfully engage in timing activities. To address this issue, we provide evidence that significant diversification opportunities remain in our sample period. The remainder of the paper is organized as follows. Section 2 covers the analysis with the pension fund portfolio weight data and addresses the correlation issue. Section 3 presents the analysis of the global mutual fund returns. Section 4 concludes. 2. Analyses of portfolio weight data for pension fund managers We begin by examining the month-to-month changes in portfolio weights for a sample of pension fund managers. In particular, we are interested in identifying any patterns of national or world market timing around the three major market moves during the 1985e1990 sample: the October 1987 worldwide stock market crash, the smaller October 1989 crash, and the dramatic drop in the Japanese market that began in January 1990. We begin by describing our data and the major market moves in the sample period. We then describe the logratio methodology, correlation issues, and close with a report of our results. 2.1. Data Data on individual portfolio weights are rarely available for research outside an investment house.4 However, we have been fortunate to obtain data on portfolio weights for roughly $40 billion in US pension fund monies tracked by the Frank Russell Company, a major pension fund management firm. The data are for end-of-month aggregate positions in each national market, and the sample period is 1985:1e1990:12, prior to the time hedge funds became major players in this market.5 During that time, from 21 to 70 individual portfolios were formed by portfolio managers who had been given a ‘global brief’ by their pension fund clients, which means that they included international as well as US assets in their asset menu. Managers had no restrictions on amounts of money per country or on how to allocate funds within countries. Our 4

No statistical agency currently reports the aggregate holdings of a specific country’s investors in the assets of other countries on a regular basis. In the mid to late 1990s the US Department of the Treasury surveyed US investment houses to construct a point estimate of holdings, the first since the close of World War II (and has also begun collecting data on foreigners’ ownership of US stocks), and two updates are available. Most authors proxy the international distribution of equity holdings by cumulating country-specific flow data, as in French and Poterba (1991b), but recent evidence in Warnock and Cleaver (2003) has shown such cumulative measures to be deficient. 5 Hedge funds have mandates that create incentives for fund managers that often lead to extreme positions. We wanted to test our method on a more typical population. Additionally, since the majority of the work in this area looks at larger, well-diversified funds, we wanted our sample to be comparable for valid comparisons (see Fung et al., 2002 for results on hedge funds).

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0.4 0.3 0.3 0.2 0.2 0.1 0.1

0

Japan and U.S. Weights

Germany, U.K., and Cash Weights

0.4

Germany Japan U.K. U.S. Cash

0 1:85

1:86

1:87

1:88

1:89

1:90

Month Fig. 1. Actual portfolio weights.

country-specific portfolio weights are computed from the aggregate holdings of this set of global managers. There are two asset categories in the data set, equity and ‘other’, where the latter category comprises investment in bonds and cash assets. In practice, the great majority of the holdings are in the equity category. The ‘other’ category has virtually no bonds: it is viewed as a cash asset or as cash held in transition from one equity market to another. The managers in our sample were not typically restricted by their institution in the amount of cash they could hold; any restrictions were self-imposed. We therefore refer to the ‘other’ category as cash in this paper, and view it as serving both an asset and a holding function. For parsimony in presentation and sufficient degrees of freedom, our analysis will focus only on equity investment in four national stock markets e Japan, Germany, the UK, and the US e plus the cash category.6 Since we are limiting the list of assets we examine, it is important to show why this limitation will not affect our results. As we discuss in the following subsection, the logratio test has the appealing property that it is insensitive to the omission of assets from the investor’s portfolio. Fig. 1 presents the five series of monthly portfolio weights from the aggregate of our sample of pension fund managers. (Note that identical symbols are used for any given country in all figures in the paper.) We are particularly interested in the key months around October 1987 (8e22% drop for our sample countries), October 1989 (2e10% drop for our sample countries), and early 1990 (the Japanese market dropped 6% in US dollar terms in January, 10% in February, and 19% in March 1990). Over the sample period, the average change in each of the country return indices was less than 2% per month, so these major market moves are notable. Did portfolio managers anticipate these market moves and reallocate their portfolios accordingly? Our priors are that the October 1987 and October 1989 crashes were unanticipated.7 In

6

We also repeated the analysis with four additional countries (Canada, France, Netherlands, Switzerland) and the results were substantially the same. 7 For example, we note that the findings of Roll (1988) can be interpreted as an indication that there was little market timing in anticipation of the October 1987 crash.

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contrast, there is abundant anecdotal evidence that the fall-off in the Japanese market had been long-awaited, at least by foreign investors. Shiller et al. (1991) document this with survey data. Observers cited a variety of reasons to expect a market fall, including slow money supply growth, higher interest rates in Japan, and an appreciating yen (The Economist, 10/21/89 and 11/11/89). Furthermore, Japanese P/E ratios, which reached levels above 60 in 1989, were extraordinarily high by US standards. Even adjusting for Japanese cross share holding and the important accounting differences between the two countries, the market appeared overvalued (French and Poterba, 1991a; Ueda, 1990). Our intuitions appear to be borne out in Fig. 1. First, there were large changes in portfolio weights in the key time periods, but the changes seem to come after October 1987, and during October 1989.8 Second, the October 1989 reallocation is dominated by a dramatic movement of funds out of Japan.9,10 The reallocation out of Japan reduced the portfolio weight for Japan from the 0.15e0.20 range, where it had been for most of the sample period, to less than 0.05 by the end of 1990. This qualitative evidence suggests that managers reacted to, but did not anticipate, the worldwide crashes, and that they anticipated the Japanese market crash. However, only statistical tests can tell us whether these portfolio reallocations were significant in size; we turn now to those tests. 2.2. Methodology of the logratio test We wish to compare asset weights in the periods before and after each major market move. In this section we first briefly describe how the logratio test methodology is applied to testing hypotheses about changes in portfolio weights. Then we present the test statistics for hypotheses about changes in the overall portfolio and individual asset weights. 2.2.1. The logratio test Testing hypotheses about portfolio weights is complicated by the fact that a portfolio’s weights sum to 1. Standard multivariate tests cannot be applied to a set of asset weights because this adding-up constraint makes the covariance matrix for the weights singular. The logratio test overcomes these problems by the application of a logratio transformation, as proposed 8

We recognize that portfolio weight data may reflect passive as well as active portfolio adjustments. In the case of international investment positions, there are two potential sources of passive changes: the weight may change because the stock prices (market indexes) went up or down, or because the exchange rate changed (given that all portfolio holdings are measured in dollar terms). While it is not possible to completely separate active from passive changes, the absence of high correlations between weight changes and contemporaneous returns or exchange rates makes us comfortable in interpreting portfolio weight changes as evidence of deliberate (i.e., active) portfolio rebalancing. In addition, the change in the Japanese market in October 1989 is much smaller as a percent than the changes in the other markets we examine. Thus, we are confident that the dramatic changes that we observe in the Japanese weights for our sample of managers are due to more than mere loss of capital value for a passive position. 9 According to the Japanese Ministry of Finance, net stock purchases/sales by all foreign investors follow a similar, although slightly less dramatic pattern. There were net stock sales of $183.5 million in August 1989, $291 million in September, $4.15 billion in October, and $1.12 billion in November. However, December 1989 saw net purchases of $6.28 billion, followed by net sales of $3.34 billion in January 1990 (source: Asian Wall Street Journal Weekly, various issues). 10 The Japanese market continued to rise until it peaked on December 29, 1989, at 38915.87. Since foreign holdings account for a very small percentage of Japanese equity (between 3.6% and 6% in the 1980e1987 period, according to Takagi, 1989), it is not surprising that the withdrawal of foreign holdings was not sufficient to outweigh apparent Japanese investor optimism.

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generally by Aitchison (1986) and first applied to a portfolio setting by Glassman and Riddick (1994). Define Wit as the portfolio weight for asset ‘‘i’’ in month ‘‘t’’. Let there be M assets whose weights sum to 1. Aitchison suggests applying the following data transformation. Choose one asset, say the Mth asset, as a reference asset and define the ratios of the weights of all assets ‘‘i’’ to that of asset M. Applying a logistic transformation to each ratio, we have the ‘logratios’:   ln Wti =WtM ; i ¼ 1; .; M  1:

ð1Þ

Since the logratios do not sum to 1 (unlike the original portfolio weights), the covariance matrix of the set of M  1 logratios is nonsingular. Hence, standard multivariate statistical techniques can be applied to test hypotheses about the set of portfolio weights. Furthermore, the logratio test has the appealing property that it is insensitive to the omission of assets from the investor’s portfolio. That is, we can test a hypothesis about a subset of asset weights without observing the weights for other assets. This is because the ratio of two individual asset weights remains constant even if the addition or subtraction of assets to the portfolio alters the absolute size of the weights. For a detailed discussion of this property, see Glassman and Riddick (1994). Our hypotheses require us to do a series of before and after comparisons through time in order to identify those months in which significant portfolio adjustments were made. In what follows, we report comparisons of mean logratios for rolling 9-month windows. To make these comparisons, we compute the asset logratios from the period leading up to each month t (those logratios are denoted by Xt,ji, j ¼ t  8,.,t; i ¼ 1,.,M  1) and the asset logratios after time t (denoted by Yt,ki, k ¼ t þ 1,.,t þ 9; i ¼ 1,.,M  1). Then we compute sample means for the X’s and Y’s and compare them. Thus, the average logratios over the first 9 months of our sample are compared to the averages over the next 9 months. Then we roll 1 month forward, and repeat the calculation, until we have a series of 9-month comparisons. Our results are robust to other window sizes.11 Assume that the sets of before and after portfolio weight logratios are drawn from normal distributions as follows: Xt;j wNðmX;t ; SX;t Þ and Y t;k wNðmY;t ; SY;t Þ where Xt;j and Y t;k are (M  1)  1 vectors of the portfolio logratios. Denote the means of the logratios for a sample of n ¼ 9 months leading up to month t by the vector b uX;t : " b uX;t ¼ ð1=9Þ

t X j¼t8

t X

1 Xt;j ; .; ð1=9Þ

#0 M1 Xt;j

:

ð2Þ

j¼t8

Similarly, the means of the logratios for a sample of n ¼ 9 months following month t are uY;t : given as the elements in the vector b " b uY;t ¼ ð1=9Þ

11

tþ9 X k¼tþ1

1 Yt;k ; .; ð1=9Þ

tþ9 X

#0 M1 Yt;k

:

ð3Þ

k¼tþ1

The results for window lengths of 8, 10, 11 and 12 months are available from the authors. We were not able to use a shorter sample period than 8 months, in order to have a reasonable number of degrees of freedom for our tests.

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In our analysis we first ask if investors generally engaged in significant rebalancing of their portfolios prior to the event in question. To determine whether the entire portfolio after time t is significantly different than the portfolio before time t, we will conduct F tests on the full vectors b uX;t and b uY;t . Second, to determine whether any rebalancing was in the form of national or world market timing, we will rely on t tests applied to individual elements of b uX;t and b uY;t . We discuss each test in turn. 2.2.2. Comparing entire portfolios: the F test When we test whether there is significant rebalancing of the whole portfolio, we are testing a hypothesis about the vector of before and after means for the (M  1) asset logratios. The formal hypothesis is: H0 : mX  mY ¼ 0

ð4Þ

where we have dropped the subscript ‘‘t’’ for convenience. We are testing for the equality of two vectors of means with unequal covariance matrices.12 The appropriate test statistic, which is a variant of Hotelling’s T2 test known as the multivariate BehrenseFisher problem, is given by: i. h 0 ð5Þ ðb uZ S1b uZ Þnðn  M þ 1Þ ½ðn  1ÞðM  1Þ; where Z is defined as the vector of differences between X and Y observations for each point in time: Z ¼X Y

ð6Þ

so that b uZ is: b uZ ¼ b uX  b uY

ð7Þ

and: S ¼ ð1=ðn  1ÞÞ

n  X j¼1

zj  b uZ



zj  b uZ

0

ð8Þ

This test statistic is distributed as F(M  1,n  M þ 1) under the null hypothesis (Anderson, 1984; Chapter 5). Recall that in our application, n equals 9 months and M equals five assets, so the F-statistics have 4 and 5 degrees of freedom.13 2.2.3. Comparing individual weights: the t tests To test the equality of two individual asset means, we can use a two-sample t test. Again allowing for unequal variances, the test statistic for any asset ‘‘i’’ is: 12

Covariance matrix equality was rejected at the 5% level in 46 of 54 cases, and at the 1% level in 43 of 54 cases. Strictly speaking, as Anderson notes, the T2 test assumes that the numbering of the observations in the two samples is independent of the observations themselves. This assumption is likely to be violated in a time series setting. We checked the sensitivity of our results to the violation of this assumption by repeating the test with observations randomized within each 9-month window, and the results show no qualitative differences. 13

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b uiY uiX  b t ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ; ðs2X =n þ s2Y =nÞ

ð9Þ

where s2X is the sample variance for the asset ‘‘i’’ logratio before month t, and s2Y is the sample variance for the asset ‘‘i’’ logratio after t (note that the ‘‘i’’ subscript has been suppressed for the variances). The degrees of freedom for this univariate BehrenseFisher problem must be approximated. We use the ‘Welch approximation’ described in Bickel and Doksum (1977, Chapter 6):  1 dfz c2 =ðn  1Þ þ ð1  cÞ2 =ðn  1Þ ;

ð10Þ

where c equals ½ðs2Y =nÞðs2X =n þ s2Y =nÞ1 . The approximate degrees of freedom range theoretically from a low of (n  1), when s2Y is much larger than s2X, to a high of (2n  2) when s2X equals s2Y. Note that a negative value for the t-statistic means that the portfolio weight for the numerator asset ‘‘i’’ has risen relative to that for the denominator asset M. (Conversely, a positive t-statistic indicates a fall in the numerator asset weight relative to the denominator.) This could result from one of three types of reallocations: (1) money moves out of the denominator asset M into the numerator asset ‘‘i’’, (2) money moves into both assets ‘‘i’’ and M, but the increase for ‘‘i’’ is greater, or (3) money moves out of both ‘‘i’’ and M, but the decrease for ‘‘i’’ is smaller. Since we are primarily interested in (1), we will need to examine all asset pairs to distinguish changes consistent with (1) from cases (2) and (3). 2.3. Potential for diversification Much recent work has raised issues about the usefulness of international diversification because of increases in correlation, both generally over time and during extreme market moves. For example, Longin and Solnik (1995, 2001) show that conditional correlations have risen over the past 30 years, and that correlations rise during periods of high volatility. Most importantly for our work, the rise is particularly high in volatile bear markets. If good diversification opportunities do not exist, then managers may not be able to find alternate countries that are attractive when compared to their current portfolio. In such an instance, a manager who wishes to time the national markets may not be able to do so in any way that our test would measure. To address this potential problem as it applies to our analysis, we examine possible combinations of assets in our sample and show that economically significant diversification opportunities exist in our sample. First, in Table 1 we provide the correlation coefficients and other basic statistical information based on the MSCI monthly index returns for our sample countries during our sample period. Correlation values range from a low of 0.2294 (US, Netherlands) to a high of 0.8087 (US, Canada). Twelve of the remaining 28 coefficients are below 0.5. These index correlation coefficients are calculated based on monthly returns, to match our monthly data, and we cannot directly investigate the findings of Longin and Solnik for high volatility periods within each month for our sample with this data. This is an important issue for our results because the two October periods we examine are quite volatile. To try and get at the same point, we instead provide simulation results for two different pairs of portfolios: one formed by investing in the UK and the US versus the UK and Canada, and then one formed by investing in Germany and the Netherlands versus Germany and Switzerland. The intent is to

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Table 1 Statistics across sample markets (1985e1990) Canada A. Correlations Canada 1 France Germany Japan Netherlands Switzerland UK US

France 0.4353 1

B. Returns and standard deviations Return 0.0077 0.0228 S.D. 0.0516 0.0770

Germany

Japan

Netherlands

Switzerland

0.3011 0.7121 1

0.2396 0.4663 0.3116 1

0.6854 0.6305 0.7128 0.4017 1

0.5241 0.6349 0.7503 0.4106 0.7655 1

0.6385 0.5246 0.4810 0.4135 0.7175 0.6545 1

0.8087 0.4925 0.3736 0.2294 0.6333 0.5287 0.6072 1

0.0206 0.0800

0.0207 0.0821

0.0163 0.0500

0.0166 0.0635

0.0178 0.0695

0.0106 0.0510

C. Actual monthly returns, October 1987 and 1989 1987 0.027 0.017 0.098 1989 0.008 0.061 0.061

0.040 0.051

0.077 0.052

0.056 0.053

UK

0.048 0.050

US

0.088 0.018

see if our mangers should have been able to find portfolios using the monthly data available that would have provided significant diversification; i.e., can they find better positions through diversification using only monthly data? These country comparisons were chosen only because the correlations for the UK and Germany with the other countries are quite close in value to each other and relatively high in absolute value. In other words, these countries seemed to offer relatively low diversification opportunities in our sample. If the portfolios formed work well, we would expect the rest of the sample to provide even better opportunities. We had no priors on outcome. Data and results for the simulations are presented in Table 2. We see evidence that economically important diversification is possible. Panel 2-A in the table shows results for return and variance for equally weighted portfolios, where all calculations were made using actual sample statistics. If we compare portfolios #1 and #2, we see a difference in monthly expected return of 0.14%, with virtually no increase in risk (risk numbers are identical due to rounding). On an annual basis, this would be an improvement. Conversely, comparing portfolios #3 and #4 shows almost no difference in return, but the change in risk is relatively high for a minimum variance frontier.14 In Panel 2-B we present actual portfolio monthly returns for the 2 months that our analysts do not show timing ability e October 1987 and 1989. If we compare these to the actual country returns during that month, which are in Table 1, we see significant differences in return between the pairs of comparison portfolios. For example, the actual monthly loss in the US MSCI index in October 1987 was 0.088%. In contrast, the UK/US portfolio actual loss would have been 0.068% and the UK/Canada portfolio actual loss would have been only 0.038%. Similarly, the actual returns for these portfolios in October 1989 were much higher than individual country returns.

14 Many additional combinations were checked as well. While not all provided good diversification opportunities, the results presented are representative of those that do. The key point is that several such opportunities existed for our managers given the correlation structure in the data.

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Table 2 Possible portfolios Panel 2-A: Portfolios with actual correlations Portfolio

Correlation

Country

Weights

Monthly return

Monthly std. dev.

#1

0.61

0.1615

0.64

0.0128

0.1615

#3

0.71

0.0184

0.1804

#4

0.75

0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

0.0142

#2

UK US UK CAN GER NETH GER SWITZ

0.0186

0.1902

Panel 2-B: Actual monthly returns Portfolio

Correlation

Actual returns, October 1987 #1 0.61 #2

0.64

#3

0.71

#4

0.75

Actual returns, October 1989 #1 0.61 #2

0.64

#3

0.71

#4

0.75

Country

Weight

Return

UK US UK CAN GER NETH GER SWITZ

0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

0.0677

UK US UK CAN GER NETH GER SWITZ

0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

0.0376 0.0876 0.0769

0.0340 0.0291 0.0768 0.0770

Finally, we note that the impact on portfolio weights from having high correlations is unclear in the literature. Ang and Bekaert (2004) show that effects are negligible when risk aversion is low, while Das and Uppal (2002) disagree. Both papers rely on regime switching models, which do satisfy the Longin and Solnik critique about underlying distributions for returns. We believe this issue needs further investigation for researchers to be able to make general statements about diversification, but we are confident that diversification opportunities do exist in our sample.

2.4. Empirical results In this section we use the logratio test methodology described above to identify periods of significant portfolio rebalancing during 1985e1990 and to formally test whether managers anticipated the three major moves of interest: the crash of October 1987, the smaller crash of October 1989, and the Japanese stock market drop of early 1990. We first discuss the general pattern of results, and then analyze the specific months around each event in more detail. We

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Value of F Statistic Hundreds

5

4

3

2

1

0 1:86

1:87

1:88

1:89

1:90

Time Fig. 2. F-statistic for portfolio. Notes: F-statistic for October 1989 is larger than shown; graph was truncated to preserve visual scale at a useful level.

also calibrate our results by examining a baseline period of little extreme market activity in 1996. 2.4.1. General patterns in results The hypothesis of a change in portfolio weights from one time period to the next can be formulated as a test of whether the average of portfolio weights after a given date is significantly different from the average of portfolio weights for a sample of previous months. Given the 72 months in the sample 1985:1e1990:12 and our 9-month comparison window, the joint F and individual t tests described above are repeated 54 times. We present the results for some specific months in tabular form, and provide graphs of the entire series, as well. The F test is invariant to the choice of the denominator for the logratios, since it incorporates comparisons between all asset pairs simultaneously. Fig. 2 presents the entire series of monthly Fstatistics for the hypothesis of overall portfolio rebalancing. We note that the logratio F test is quite sensitive: a reallocation between just one asset pair is usually enough to generate a significant Fstatistic. Hence, we were not surprised to find that all the months in our sample show significant F values, ranging in size from a low of 5.62 (August 1986) to a high of 413.8 (October 1989). The critical values for the F(4,5) statistics are 5.192 for 5%, and 11.391 at the 1% level. The individual t test, which looks only at rebalancing between the numerator asset and the denominator asset, is not invariant to the choice of denominator asset. One must look at how the numerator asset share changes relative to every one of the other assets to get a complete picture of the rebalancing, and individual t-statistics for all asset pairs must be calculated. The pairwise t-statistics for each asset as the denominator asset are graphed in five separate panels e one for each denominator e in Fig. 3AeE. Since we examine five assets, there are 20 possible pairs of numerator ‘‘i’’ and denominator M (for M not equal to ‘‘i’’). These graphs show that the most significant t-statistics are in October 1989.15 The patterns of t-statistics confirm the movement of funds out of Japan as the dominant feature of that 15

Recall that the degrees of freedom for the t-statistics are approximate in this application. They range in value from 8 to 13.2, so that the critical values range from 2.157 to 2.306 at the 5% level, and from 3.005 to 3.355 at the 1% level.

D.A. Glassman, L.A. Riddick / Journal of International Money and Finance 25 (2006) 1029e1050

1040

Germany as denominator

Japan as denominator 20

30

10

t>0; numerator rise less than denominator rise

Value of t-Statistics

Value of t-Statistics

t<0; numerator rise greater than denominator rise 20

10

0

-10

-20

A 10:85

0 -10 t<0; numerator rise greater than denominator rise -20 t>0; numerator rise less than denominator rise -30 -40

10:86

Oct 87

10:87

10:88

Oct 89

B 10:85

10:89

10:86

10:87

Japan

U.K.

U.S.

Germany

Cash

U.K. as denominator

U.K.

U.S.

Cash

U.S. as denominator 30

t<0; numerator rise greater than denominator

t<0; numerator rise greater than denominator

20

Value of t-Statistics

Value of t-Statistics

10:89 Oct 89

Time

30

t>0; numerator rise less than denominator 10 0 -10 -20

10:88

Oct 87

Time

C 10:85

20 t>0; numerator rise less than denominator 10 0 -10 -20

10:86

Oct 87

10:87

10:88

Oct 89

10:89

D 10:85

10:86

Time Germany

Japan

Oct 87

10:87

10:88

Oct 89

10:89

Time Cash

U.S.

Germany

Japan

U.K.

Cash

Cash as denominator 30

Value of t-Statistics

t<0; numerator rise greater than denominator asset 20 t>0; numerator rise less than denominator asset 10 0 -10 -20

E 10:85

10:86

Oct 87

10:87

10:88

Oct 89

10:89

Time Germany

Japan

U.K.

U.S.

Fig. 3. Pairwise t-statistics.

month. However, the asset reallocations prior to October 1989 and prior to October 1987 follow less obvious patterns. We therefore examine these time periods in more detail. Tables 3 and 4 present the specific numerical results of our t tests and F tests around the months of particular interest e October 1987 and October 1989. Since a rebalancing from numerator asset ‘‘i’’ to denominator asset M is just the reverse of a rebalancing from numerator M to denominator ‘‘i’’, the t-statistics in these two cases are equal and opposite in sign. For this reason, only the 10 unique t-statistics are reported in each panel. Table 5 shows the same information for the months of September, October, and November 1986, which were chosen

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Table 3 Months surrounding 1987 crash. Individual t tests and overall F test of portfolio change; numerator is column asset, denominator is row asseta (t < 0 indicates movement from denominator to numerator asset; t > 0 indicates movement from numerator to denominator asset) Individual asset t-statistic Assets

Japan

A. August 1987 Germany 2.60* Japan UK US Overall F test: F(4,5) statistic ¼ 90.29** B. September 1987 Germany 2.73* Japan UK US Overall F test: F(4,5) statistic ¼ 172.61** C. October 1987 Germany 2.76* Japan UK US Overall F test: F(4,5) statistic ¼ 197.27** D. November 1987 Germany 2.61* Japan UK US Overall F test: F(4,5) statistic ¼ 49.89** a

UK

US

Cash

2.92* 1.99

0.83 2.40* 5.17**

2.29* 0.75 0.64 1.75

2.26* 0.63

0.23 3.00* 4.88**

2.92* 1.14 0.53 2.78*

1.76 0.35

1.09 4.09** 4.71**

4.06** 1.75 1.81 4.87**

0.82 1.51

3.20** 5.26** 4.18**

5.34** 2.51* 4.04** 9.98**

Note: An * or ** indicate significance at 5% or 1% level, respectively.

as baseline comparison months (i.e., months during the same season but in a year without a major market drop). We now discuss the results for each of these periods. 2.4.2. The October 1987 crash Consider first the results in Table 3A through D for August through November 1987, the months surrounding and including the October 1987 crash. While all months have significant F-statistics, they vary widely in size (from approximately 50 to 197). Based on these Fstatistics, it appears that larger amounts of rebalancing were occurring in September and October, as compared to August and November. However, this activity should not necessarily be interpreted as evidence that investors anticipated the crash, as an examination of the individual asset t-statistics will show. If investors anticipated the October crash, then we would expect to see world market timing in the form of significant movements out of all stock markets and into cash prior to, or possibly during, October. Instead, there are very significant reallocations of funds from one country to another from August through November; from the US to Japan and the UK, as well as from Germany to Japan. For example, in August (Table 3A), we read across rows and see statistically

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significant increases in the amount invested in other countries relative to Germany, except for the US (whose t-statistic is negative, but insignificant); a significant increase in holdings in Japan relative to the US; and a significant increase in the UK holdings relative to US holdings. Additionally, all but one of the cash t-statistics are negative in August through November, indicating movements out of (rather than into) cash, although they are not all significant. Thus, while there are some reallocations from stocks into cash the general patterns are inconsistent with anticipation of the markets’ simultaneous drop.16 We conclude that the bulk of evidence is against the hypothesis of world market timing.

2.4.3. The October 1989 drop Now, consider Table 4A through C for SeptembereNovember 1989. The F-statistic of 413.8 for October 1989 (Table 4C) is the largest, by far, in our sample (the next largest F-statistic is 197.57 in October 1987). Once again, the crash month and the prior month (October and September, respectively) show substantial rebalancing between stock markets. However, we do not see the generalized movement into cash that the world market timing hypothesis would predict. As was true in October 1987, two of the four cash-as-numerator t-statistics are negative and significant, indicating some increase in cash relative to other assets. However, unlike October 1987, the remaining two cash t-statistics are positive. What we do see in October 1989 are dramatically large and negative values for all the t-statistics with the Japanese asset as denominator. This indicates a large increase in holdings in all other assets relative to holdings of Japanese assets. The large relative size of these t-statistics is particularly apparent in panel B of Fig. 3, which shows the evolution over time of the t-statistics with Japan as the denominator asset; October 1989 values are huge compared to the rest of the series. The October 1989 crash had a more uneven effect on world markets than the October 1987 crash. Did investors anticipate the differential effects of the crash and engage in national market timing? The evidence does not support this hypothesis. The movement out of Japanese stocks could not have been a response to (or anticipation of) the October 1989 crash, since the Japanese market fell much less than other markets. Further examination shows that the patterns of t-statistics noted in October 1989 are repeated in the following months, through February 1990, but with gradually decreasing significance. Moreover, the F-statistics in these other months are a great deal lower. Clearly, October 1989 was the key month for the pension fund managers. To summarize, we observe a dramatic portfolio reallocation in October 1989, which is not consistent with either the world or national market timing behavior that we would expect to be related to the 1989 crash. Instead, we see uniform movement out of Japan, even though that market continued to rise until January 1990. This leads us to conclude that the majority of activity surrounding the market drop in October 1989 was due to rebalancing in anticipation of the drop in the Japanese market that began in January 1990.17 16

As noted in Section 2.2.3 above, the t-statistic for a pair of equity markets could be significant if holdings in both assets fell, but one fell more than another. Thus, it is possible that the significant t-statistics for equity market pairs represent differential movements out of stocks into cash. Looking back at Fig. 1, we can rule out this possibility. 17 As discussed above, there is much anecdotal evidence that US managers had been anticipating a major drop in Japan for some time. However, our results clearly suggest that our sample of managers made their move at a specific time just before the Japanese market fell.

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Table 4 Months surrounding 1989 market drop. Individual t tests and overall F test of portfolio change; numerator is column asset, denominator is row asseta (t < 0 indicates movement from denominator to numerator asset; t > 0 indicates movement from numerator to denominator asset) Individual asset t-statistic Assets

Japan

A. September 1989 Germany 8.02** Japan UK US Overall F test: F(4,5) statistic ¼ 72.55** B. October 1989 Germany 23.88** Japan UK US Overall F test: F(4,5) statistic ¼ 413.8** C. November 1989 Germany 9.11** Japan UK US Overall F test: F(4,5) statistic ¼ 38.11** D. December 1989 Germany 6.33** Japan UK US Overall F test: F(4,5) statistic ¼ 67.28** E. January 1990 Germany 5.03** Japan UK US Overall F test: F(4,5) statistic ¼ 16.64** F. February 1990 Germany 4.04** Japan UK US Overall F test: F(4,5) statistic ¼ 20.06** a

UK

US

Cash

6.17** 8.45**

3.78** 8.90** 5.44**

2.97* 7.18** 3.42** 0.03

10.48** 29.18**

7.14** 20.43** 5.49**

3.25** 22.82** 6.77** 1.54

11.06** 7.11**

7.65** 8.24** 5.12**

5.33** 6.71** 3.86** 0.66

5.48** 5.41**

6.64** 5.72** 2.13

9.65** 4.42** 1.55 0.16

2.93* 4.53**

4.73** 4.57** 0.85

0.96 3.69** 1.58 0.95

1.26 4.14**

2.46* 4.02** 0.18

0.33 3.42** 1.75 1.56

Note: An * or ** indicate significance at 5% or 1% level, respectively.

2.4.4. Baseline period around and including October 1986 To calibrate the findings for 1987 and 1989, we briefly examine the results in Table 5AeC for the baseline months of September, October and November 1986, a period with little volatility in market values. The F-statistics range from 10.56 to 13.49, which e while still significant e makes them quite small relative to the values in 1987 and 1989. Given the range of F-statistics in the overall sample, the values here are clearly toward the small

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Table 5 Baseline period in 1986. Individual t tests and overall F test of portfolio change; numerator is column asset, denominator is row asseta (t < 0 indicates movement from denominator to numerator asset; t > 0 indicates movement from numerator to denominator asset) Individual asset t-statistic Assets

Japan

A. September 1986 Germany 0.53 Japan UK US Overall F test: F(4,5) statistic ¼ 10.56** B. October 1986 Germany 0.55 Japan UK US Overall F test: F(4,5) statistic ¼ 12.28** C. November 1986 Germany 2.61* Japan UK US Overall F test: F(4,5) statistic ¼ 13.49** a

UK

US

1.60 1.79

2.28* 3.31**

0.23 1.06 3.74**

Cash 0.80 0.57 2.71* 1.05

0.39 0.20 5.10**

0.80 0.61 3.44** 0.75

3.20** 5.26** 4.18**

5.34** 2.51* 4.04** 9.98**

0.82 1.51

Note: * and ** indicate significance at 5% and 1% levels, respectively.

end of the spectrum. This is consistent with Fig. 1, which shows relatively little activity around October 1986. Now consider the t-statistics for these months: September and October have few significant statistics, while November has more. But, with the exception of the November columns for the US and cash assets e which show significant increases in US positions and significant decreases in cash positions relative to the other assets e there are few patterns in the t-statistics. Thus, we interpret the baseline case as having ‘‘normal’’ activity, which is not associated with market timing behavior in anticipation of any particular market moves. 3. Analysis of portfolio return data for mutual fund managers Based on the behavior of the pension fund portfolio weights, we have reasoned that the significant shift in portfolio holdings in October 1989 occurred in anticipation of the subsequent fall in the Japanese market. However, with only data on weights we are unable to provide definitive evidence that this portfolio rebalancing was related to market return expectations. A standard way to address such a question is to evaluate portfolio performance with a timing model which explicitly relates portfolio return to portfolio positions. Since we do not have data on the returns earned by the sample of pension fund managers, we turn to the returns earned by global equity mutual funds during the same period for our performance analysis. The remainder of this section is organized as follows. We first describe two variants of a model for evaluating the performance of a global mutual fund. The first variant allows for world market timing behavior, and the second variant incorporates national market timing.

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Then we describe the data on global mutual funds for a sample period of January 1985 to September 1994. Finally, we report the results from tests for world and national market timing ability on the part of these mutual fund managers. 3.1. Global market timing models Consider evaluating global manager performance relative to a world benchmark using a standard equation based on the world CAPM: Ri;t ¼ ai þ bi RW;t þ mt ;

ð11Þ

where Ri,t is the excess return on manager i’s portfolio in time period t, RW,t is the excess return on the world market index in period t, and ai is ‘Jensen’s alpha’, the standard measure of stock selection ability. Excess returns are computed relative to a risk free rate. Suppose that the manager engages in market timing, shifting funds out of cash and into the world market when the market is expected to rise and shifting funds into cash when the market is expected to fall. Then, using the notation of Chen et al. (1992), b varies as follows: i þ Q RW;t þ 3i;t ; bi ¼ b i

ð12Þ

i is the average level of bi and Qi, the market timing coefficient, is positive (the manwhere b ager increases systematic risk when the market is expected to rise and decreases it when the market is expected to fall). Substituting (12) into (11), we have: i þ Q RW;t þ 3i;t ÞRW;t þ mt Ri;t ¼ a þ ðb i  ¼ a þ bi RW;t þ Qi R2W;t þ ð3i;t RW;t þ mt Þ

ð13Þ

The hypothesis of world market timing ability corresponds to a significant coefficient Q on the squared term.18 Note that the compound error term in (13) is likely to be heteroskedastic. We now model national market timing by extending the quadratic regression approach to a multi-index framework. This extension is based on the intuition that a mutual fund can be viewed as a combination of portfolios for different asset classes. This suggests that a mutual fund’s return could be evaluated with a multi-index model that includes a passive portfolio for each asset class. Several domestic performance studies apply market timing measures to multi-index or multi-factor models. For example, Elton et al. (1993) take this approach in evaluating domestic (US) mutual funds relative to three passive portfolios: the S&P 500, non-S&P (small capitalization) stocks, and bonds. The idea of market (or sector) timing among multiple portfolios is also discussed in Admati et al. (1986) and Lehmann and Modest (1987). In particular, Lehmann and Modest implement a quadratic regression with squared terms for each of the factors that they examine. Following the intuition of such multi-index models, we evaluate global funds against a model that includes indexes representing each of the national markets where they invest. For 18 Based on the linear model of Eq. (13), we would expect the coefficient to be positive. However, if managers engage in non-linear strategies using derivatives, then we might see a negative and significant coefficient on a squared term (see Jagannathan and Korajczyk, 1986).

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parsimony, we cover the world asset menu by including five indexes (whose excess returns are Rj,t for j ¼ 1e5): the market indexes of Germany, Japan, the UK, and the US, and a ‘‘rest of world’’ (ROW) index created by taking the residuals from a regression of the world index on the four national market indexes.19 Applying the Treynor and Mazuy (1966) quadratic regression approach to our multi-index model results in an estimating equation similar to (13), with the exception that there are five squared terms, which correspond to the five indexes, instead of just one:

Ri;t ¼ a þ

X

i;j Rj;t þ b

X

j

Qi;j R2j;t þ ð3i;t RW;t þ mt Þ

ð14Þ

j

The hypothesis of national market timing ability corresponds to a statistically significant and positive Q coefficient on one (or more) of the squared terms.20 World market timing can be assessed by examining patterns in these coefficients for movements from countries to cash. 3.2. Global mutual fund data Global mutual funds were rare before the early 1980s and only became numerous in the mid1990s.21 We were able to obtain data on the returns for eight global equity mutual funds that existed as far back as January 1985 and a ninth that began in January 1986, matching the beginning of our portfolio weight data series. The original data are monthly, but they are aggregated (compounded) to quarterly levels because previous studies have shown greater stock market predictability for quarterly or longer horizons than for monthly ones.22 In order to have sufficient degrees of freedom for the quarterly analysis, the sample period is extended through September 1994. All returns include dividends and are computed in excess of the (compounded) 1 month LIBOR rate.23 The national return indexes are the same as those described above. The world return index is the Morgan Stanley Capital International Perspective World Index, including dividends. 19

Elton et al. (1993) also use orthogonalized indexes. As they note, with orthogonalization the index can no longer be interpreted as the return on a passive portfolio. 20 It is important to note that using the world market index as one component in the multi-country timing regression (14) is not equivalent to assuming PPP, as discussed in Glassman and Riddick (1996). Rather, in this instance we are using the world index variable as only one factor in our timing regression. The use of individual country indices allows for recognition, albeit crude, of the effects that create PPP deviations. The same cannot be said about the model in Eq. (13). As we noted earlier in the paper, timing models of this form are necessarily subject to the limitations of the underlying model. 21 We are interested in reflecting the characteristics of our portfolio weight sample in this sample of mutual fund returns. Thus, we are only interested in global funds, since they have a mandate to invest in domestic as well as foreign assets and we are interested in shifts in holdings between the US and foreign markets. Thus, we do not include funds which are international funds, meaning they only invest in non-US assets. 22 See, e.g., Fama and French (1989). Similarly, the Grinblatt and Titman (1993) benchmark-free performance analysis uses quarterly data. 23 These data were obtained from Datastream. The nine funds are: Dean Witter Worldwide, First Investors International Securities Fund, Merrill Lynch International Holdings, Morningstar, New Perspective Fund, Paine Webber Atlas Fund, Prudential Global Fund, SoGen International Fund, and United International Growth Fund. The first eight funds have monthly data going back to January 1985; the series for the ninth fund starts in January 1986.

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Table 6 World market timing regression results Eq. (13) (t-ratios in parentheses) Fund

World

1 2 3 4 5 6 7 8 9

0.833 0.326 0.732 0.681 1.009 0.926 0.566 0.880 0.742

World2 (7.052) (0.963) (5.845) (4.089) (4.294) (6.522) (2.143) (4.420) (8.577)

5.20 7.512 0.119 0.733 0.656 0.247 0.455 1.052 0.126

Intercept (0.801) (1.951) (0.180) (0.826) (0.247) (0.285) (0.339) (1.041) (0.268)

0.003 0.064 0.009 0.005 0.002 0.002 0 0 0.006

(0.450) (2.166) (1.002) (0.603) (0.172) (0.172) (0.013) (0.015) (0.749)

Notes: 1. Sample period: 1985:Ie1994:III (39 quarters) for funds 1e8. 2. Sample period: 1986:Ie1994:III (35 quarters) for fund 9. 3. Degrees of freedom: 36 for funds 1e8; 32 for fund 9. 4. Critical values (two-sided tests): DF ¼ 36: 5% level is 2.028, 1% level is 2.719 DF ¼ 32: 5% level is 2.037, 1% level is 2.738

3.3. Results from world and national market timing regressions Table 6 reports estimates of the world market timing regression, Eq. (13), for the nine global mutual funds, with the Morgan Stanley World Index used for RW. To correct for heteroskedasticity, the varianceecovariance matrix of the coefficients was estimated using the procedure of White (1980). These regressions provide no evidence of world market timing ability: none of the world market timing coefficients is significant, and seven of the coefficients are negative. Only one a (intercept) is positive and significant. These results are similar to those in much of the literature on market timing, e.g., see results in Kao et al. (1998). The results are also consistent with our conclusions from the examination of pension fund portfolio weights. These results for world timing are in contrast to the results for national market timing regressions in Eq. (14), reported in Table 7. All of the funds except the fourth have at least one statistically significant timing coefficient, and we find 13 significant coefficients on the quadratic terms (28.9% of the 45 squared terms in the nine regressions). Six of the 13 significant coefficients are positive, and five of these six are coefficients on the Japanese market. As discussed above, positive coefficients show that managers are correctly anticipating rises and drops in a market, so this group of managers appears to time correctly about half the time. This is not an impressive set of results when taken in total. However, the fact that virtually all the good timing choices were made with respect to Japan suggests that something about the managers’ timing abilities for that country were not random. The intercepts in the national market timing regressions indicate that the mutual fund managers have very little stock selection ability. Only two of our nine intercept terms are positive, and only one of these is statistically significant. This suggests that single index model timing results in other studies may fail to show significant selection abilities because they fail to consider national market timing as a separate possibility from world market timing.24 Further research is needed to draw strong conclusions.

24

World timing ability in Eq. (14) would be captured in simultaneous movements rather than by one coefficient, as in Eq. (13). We do not see such simultaneous movements here.

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Table 7 National market timing regression results, Eq. (14) (t-ratios under coefficients) ROW2

Fund

ROW

Germany

Japan

UK

US

1

1.095 2.370 1.161 1.164 0.454 0.860 0.456 0.818 1.886 2.151 1.081 1.377 1.049 1.298 0.902 1.492 1.005 2.809

0.077 1.219 0.410 2.063 0.099 1.646 0.071 1.155 0.066 0.658 0.059 0.708 0.082 0.837 0.043 0.539 0.340 4.540

0.131 3.619 0.092 1.064 0.012 3.199 0.012 0.313 0.064 0.940 0.149 2.640 0.065 1.033 0.039 0.683 0.129 4.469

0.305 3.256 0.789 2.293 0.050 0.465 0.050 0.474 0.302 1.700 0.258 2.013 0.060 0.442 0.454 3.189 0.096 1.092

0.431 1.163 3.930 0.033 0.479 5.401 1.337 0.074 0.587 7.515 5.223 0.218 0.629 10.960 6.105 0.294 0.804 31.410 4.850 0.500 0.437 27.651 2.947 0.491 0.732 17.413 4.515 0.358 0.594 15.987 5.653 0.451 0.259 70.847 2.178 2.703

2 3 4 5 6 7 8 9

Germany2

Japan2

UK2

US2

Intercept

0.305 0.950 2.187 2.416 0.128 0.474 0.427 1.387 1.041 1.901 0.271 0.688 0.505 0.906 0.227 0.753 0.413 1.089

0.393 2.730 0.123 0.188 0.414 2.299 0.189 1.165 0.611 2.657 0.367 1.672 0.850 3.419 0.337 1.970 0.555 3.771

0.840 1.773 9.362 2.902 0.020 0.034 0.785 1.208 0.157 0.188 0.913 1.219 2.253 2.276 0.207 0.314 0.005 0.011

1.006 1.395 3.538 1.650 0.812 1.100 1.363 1.413 4.031 3.200 2.591 3.011 4.679 3.064 2.624 3.591 0.449 0.562

0.010 1.329 0.092 3.135 0.021 1.665 0.001 0.064 0.020 1.273 0.010 0.835 0.009 0.642 0.002 0.184 0.009 1.351

Notes: 1. Sample period: 1985:Ie1994:III (39 quarters) for funds 1e8. 2. Sample period: 1986:Ie1994:III (35 quarters) for fund 9. 3. Degrees of freedom: 28 for funds 1e8; 24 for fund 9. 4. Critical values (two-sided tests): DF ¼ 28: 5% level is 2.048, 1% level is 2.763. DF ¼ 24: 5% level is  2.064, 1% level is 2.797. 5. ROW is a variable constructed to be rest of world; i.e., the world index excluding Germany, Japan, UK, and US.

In summary, our conclusions from the mutual fund market timing regressions are consistent with the results for the pension fund data. The fund managers in this sample show no world market timing ability, but several have statistically significant national market timing ability with respect to the Japanese market. This did not necessarily mean that their overall performance was good, given other bad timing choices, but it does suggest that they were able to anticipate the Japanese market’s drop. 4. Summary and conclusions This paper examines the global equity market timing behavior of US pension and mutual fund portfolio managers. We begin by distinguishing between world market timing and national market timing, a distinction which is based on the pattern of portfolio rebalancing among asset classes. World market timing implies a reallocation of all equity market funds to or from cash, while national market timing implies a reallocation of funds from those equity markets expected to have relatively low returns to those expected to have relatively high returns. We test for both national and world market timing in two ways. The first analysis uses monthly, country-specific portfolio weights for US pension fund managers with a global mandate. The country level disaggregation of data makes it possible for us to measure cross-border shifts in portfolio holdings for each month in our sample. We use the logratio test methodology

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to determine whether these movements are statistically significant. This approach requires no specification of an underlying asset model or benchmark portfolio, a characteristic which is both a strength and a weakness of the test procedure. While it makes it possible for us to identify statistically significant movements between countries around key market movements, it does not allow us to definitively show that the movements resulted from skill rather than luck. We perform our second analysis on a set of return data for a sample of US mutual fund managers who also invested globally during the same time period. For this evaluation, we model national market timing by extending a global returns model to include multiple national market indexes. This approach does require the identification of an underlying model, but allows us to assess performance relative to a benchmark for skill attribution. Our results from the first analysis suggest that the October 1987 crash and the smaller crash of October 1989 were unanticipated by the pension fund managers. While there is some rebalancing activity prior to these events, there is no statistically significant, across-the-board movement into cash. In contrast, we observe a dramatic reallocation of assets out of Japan in October 1989. The actual pattern of reallocations among national markets is consistent with managers anticipating the Japanese market drop that began in January 1990, rather than as their response to the concurrent world mini-crash. These conclusions are supported by the results from the mutual fund performance evaluation. While the mutual fund managers do not necessarily time well in other markets, we do find evidence of significant national market timing ability with respect to Japan over the period. We find virtually no evidence of world market timing ability for either group. In addition to these specific findings about the ability of US fund managers to time the Japanese market, our results indicate that single index models are not appropriate for assessing global performance, though this has been the standard in many earlier papers. Such models look at timing only with respect to the world market and would not be able to identify the performance with respect to Japan that we find during this period. This emphasizes the general lesson that it is important to account for both world and national market timing in building any global performance evaluation model. This fact may explain why earlier work failed to identify much timing ability among international fund managers. Finally, our results are based on an analysis of global fund data and cannot be directly interpreted as meaningful for domestic funds managers. However, domestic managers face essentially the same problem as global managers in that they simultaneously move money between asset classes (as opposed to countries) and cash. Thus, extensions of our techniques may prove valuable for more detailed studies of domestic portfolio management.

Acknowledgements This research was partially supported by a faculty development grant from the American University and by the Graduate School Research Fund of the University of Washington. We wish to thank Dana Schmidt of Morgan Stanley and Gunter Ecklebe of the Frank Russell Company for providing data. Jeffrey Weiss of Frank Russell was particularly helpful in assembling the data. Wayne Ferson, Ted Jaditz, Helen Popper, Michel Robe and an anonymous referee provided useful comments, as did seminar and meeting participants at Georgetown University, the University of Wisconsin-Milwaukee, Simon Fraser University, and the Financial Management Association. We appreciate the research assistance provided by Sharmila Srivastav. The usual caveat applies.

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