Home bias among institutional investors: a study of the Economist Quarterly Portfolio Poll

J. Japanese Int. Economies 19 (2005) 72–95 www.elsevier.com/locate/jjie

Home bias among institutional investors: a study of the Economist Quarterly Portfolio Poll Jungwon Suh Department of Finance, College of Economics and Business, Kookmin University, 861-1, Chungnung-dong, Seoul 136-702, South Korea Received 25 July 2002; revised 1 December 2003 Available online 10 March 2004

Suh, Jungwon—Home bias among institutional investors: a study of the Economist Quarterly Portfolio Poll I study the home bias pattern in international portfolios recommended by global financial institutions. These recommended portfolios from the Economist Quarterly poll provide an interesting research opportunity because transaction costs and other observable barriers in cross-border portfolio investments do not interfere with asset allocation decisions of the polled institutions. Thus, an examination of this poll data can shed light on the role of unobservable factors in international portfolio investments. I find that the institutions tilt recommendations towards their home markets; they change home market weights more frequently relative to other market weights or relative to institutions from other countries; and they change weights of geographically distant markets less often than other market weights. Overall, the evidence from the analysis suggests that home bias can arise from unobservable factors such as information asymmetry and investor optimism. J. Japanese Int. Economies 19 (1) (2005) 72–95. Department of Finance, College of Economics and Business, Kookmin University, 861-1, Chungnung-dong, Seoul 136-702, South Korea.  2004 Elsevier Inc. All rights reserved. JEL classification: G10; G12 Keywords: Home bias; International portfolio investments

1. Introduction Investors tend to hold a substantially larger proportion of their equity in domestic equities than is suggested by the value-weighted world equity portfolio or by the mean–variance E-mail address: [email protected]. 0889-1583/$ – see front matter  2004 Elsevier Inc. All rights reserved. doi:10.1016/j.jjie.2003.12.003

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optimal world equity portfolio.For example, while the US equity market comprises less than 48 percent of the world equity market, US investors’ foreign equity holdings account for only about eight percent of their total equity holdings. A number of studies document this phenomenon, referred to as the home bias puzzle. Lewis (1999) and Britten-Jones (1999) suggest that US investors’ optimal weight in foreign equities is about 40 percent, which implies that US investors would benefit by diversifying into foreign equities. Traditional explanations of the home bias puzzle focused on observable barriers to international investment such as governmental restrictions on cross-border capital flows, foreign taxes, and high transaction costs. Several later studies, however, suggested that observable costs may not be sufficiently large to account for the degree of home bias. For example, Cooper and Kaplanis (1994) find that the estimated deadweight costs in international investments are much bigger than can be explained by observable costs involved in international investments. Their result suggests that unobservable barriers such as information cost may play an important role in international investments. Tesar and Werner (1995) report that the turnover rate on equities held by non-residents is higher than the overall turnover rate on the local market. This observation of investors making frequent and sizable shifts in their holdings of foreign securities suggests that transaction costs faced by investors in cross-border investments may not be prohibitively expensive. An interesting study of Coval and Moskowitz (1999) approaches the home bias puzzle from a different angle, by examining US domestic portfolio holdings. They report that US investors tilt their portfolio holdings towards companies located near their home. This local equity preference, they argue, arises because investors have easier access to information about local companies. Though they do not directly examine international portfolio allocations, their result suggests that information advantages/costs will play an important role in international portfolio choices as well. Indeed, the notion that information costs affect portfolio choices is not new. Merton (1986) offers an equilibrium pricing model in the world where information costs are present. Kang and Stulz’s (1997) study on portfolio holdings of foreign investors in the Japanese stock market indicates that information costs are one of the primary factors in international portfolio investments. Brennan and Cao (1997) construct and test a model in which foreign investors face an information disadvantage with respect to local assets vis-à-vis local investors. This present study attempts to shed more light on the home bias puzzle by looking into international portfolios recommended by large global institutional investors from around the world. These international portfolios from the Economist Quarterly Portfolio Poll provide an interesting research opportunity because transaction costs and other observable barriers in cross-border portfolio investments do not interfere with asset allocation decisions of the polled institutions. In a sense, these recommended portfolios offer a snapshot of a world where observable barriers are not present. Thus, this poll can bring out the role of unobservable factors in creating home bias. That is, any bias detected in these recommended portfolios will be due to unobservable barriers such as information advantages/costs.1 I examine the recommended portfolio holdings of ten institutional investors from three regions in the world—two American, two Japanese, and six European institutions—over

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ten-year period between the first quarter of 1989 and the second quarter of 1999. I find that institutional investors in the poll exhibit a strong preference for their home markets in their recommendations. Specifically, I detect two types of home bias tendency from the data. First, the recommended portfolio holdings are substantially tilted towards home markets of the institutions. The extent of this home bias is statistically significant against chosen benchmarks. Second, institutional investors exhibit home bias when they change portfolio weights in their recommended portfolios. That is, they change home market weights more frequently relative to other market weights or relative to institutional investors from other countries. Furthermore, I find that institutional investors make relatively few changes over time in the weights of some markets, which I call “weak spots.” These weak spots coincide with the markets geographically distant from the institutions’ headquarters. The existence of “weak spots” indicates either that institutional investors in the poll receive meaningful information from distant markets less often or that they do not pay serious attention to those markets. The rest of the paper is organized as follows. The following section describes the data; Section 3 presents the findings from the analysis; and Section 4 discusses the results and concludes the paper.

2. Description of data The main data for this study come from the Economist publication, “Our Quarterly Portfolio Poll” for the period Q1/89 to Q2/99. Table 1 displays a typical format for this poll. Each quarterly poll is comprised of three parts. In the first part, participating institutions recommend portfolios allocated among three broad asset classes: cash, bonds, and equities. In the second part, institutions recommend equity portfolios diversified into eight geographical markets of the world: the US, the rest of the Americas, the UK, Germany, France, the rest of Europe, Japan, and the rest of Asia. In the third and last part, institutions recommend portfolios of sovereign bonds denominated in six different currencies. “O” in Table 1 represents the neutral portfolio. For the equity portfolio, the neutral portfolio is the value-weighted world market portfolio based on the MSCI. For the bond portfolio, the neutral portfolio is based on Salomon Brothers’ world governmentbond index. “A” through “J” represent participants. For example, “A” is Merrill Lynch, “B” is Lehman Brothers, “C” is Nikko Securities, “D” is Daiwa Europe, “E” is Credit Agricole, “F” is Robeco Group Asset Management, “G” is Bank Julius Baer, “H” is UBS International Investment, “I” is Commerz Int’l Capital Management, and “J” is Credit Suisse Asset Management. Since this present study looks mainly into issues related to equity home bias, the first and second parts of the poll will be the focus of the study.2 1 Actual portfolios of institutional investors—such as life insurance companies, pension funds and mutual funds—exhibit home bias, as documented in Tesar and Werner (1995) and Lewis (1999). Note that home bias in actual portfolios can arise from a variety of sources including both observable and unobservable barriers. 2 An example of a recent academic study that uses the Quarterly Portfolio Poll of the Economist is Bange (2000). She looks at the return performance of the cash–bonds–equity portfolios that are from the first part of the poll.

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Table 1 Example of the Economist Quarterly Portfolio Poll O

A

B

C

D

E

F

G

H

I

J

40 55 5 100

69 23.5 7.5 100

35.5 40 24.5 100

36 58 6 100

– – – –

60 40 0 100

50 30 20 100

60 30 10 100

Part 1. Basic portfolio 50 60 48 50 25 52 0 15 0 100 100 100

US Other America UK Germany France Other Europe Japan Other Asia

38.9 2.8 10.8 4.1 3.7 9.4 26.2 4.1 100

40.5 2 10.5 3.4 5.6 8.7 25 4.3 100

39 5.5 7.5 6 6.5 15.5 10 10 100

36 1 11 6 4 4 30 8 100

Part 2. Equity portfolio 15 30 25 2 0 4 10 10 12 8 13 6 4 20 8 15 5 12 40 15 22 6 7 11 100 100 100

37 0 30 7 11 0 0 15 100

2.9 0 71 15.3 0 0 5.8 5 100

25 0 6.5 12 15 14 27.5 0 100

33 6 9 8 9 10 17 8 100

USD Yen BP DM FFr Others

51.7 17.5 7.4 7.3 6.6 9.5 100

50 23 3 6 5 13 100

45 0 0 18 12 25 100

54.5 17.8 5.9 16.1 4.8 0.9 100

Part 3. Bond portfolio 50 30 15 5 15 5 10 10 20 0 0 15 15 15 30 20 30 15 100 100 100

45 10 8 5 7 25 100

0 0 70.2 0 17 12.8 100

35.2 0 3 3 18.8 40 100

37 11 6 12 14 20 100

Equities Bonds Cash

To understand the poll data better, it is worthwhile to consider the aim and audience of the poll. At the inception of the poll in 1989, the magazine states the nature of the poll as follows: The Economist asked nine money-managers for their opinions on the best mix of investments over the next 12 months. They were asked to design a portfolio for an investor with no existing investments, no overriding currency considerations and an objective of long-term capital growth. (The Economist, 4/25/89.) First, it is essential to note that the institutions participating in the poll do not recommend portfolios exclusively for their home country clients. It is understood that the poll targets a worldwide audience, as the subscribers to the Economist are scattered around the world. This interpretation is consistent with the nature of the poll quoted above: “. . . the recommended portfolios are designed for an investor with no overriding currency consideration. . . ” Given this interpretation, one can assume that the institutions in the poll will not consider transaction costs or capital barriers in their portfolio recommendations. The rationale for the assumption is that most transaction costs and capital barriers are country-specific (i.e., specific to the country in which the investor resides or to the country in which the investor invests). In recommending portfolios for investors scattered around the world, institutions will need to ignore transaction costs and capital barriers that are

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country-specific in nature. Indeed, through a careful reading of the magazine’s written summary of each quarterly poll, I find no mention of transaction costs or capital barriers by participants as factors affecting their recommendations. They generally cite changes in economic and political situations as factors affecting their recommendations. The above reasoning tells us that by examining the poll data, one can abstract from the effect of transaction costs and other observable cross-border capital barriers on portfolio allocation decisions. In other words, the poll data allow us to focus on the role of unobservable factors such as information asymmetry in portfolio allocation decisions. With regards to the home bias puzzle, this means that any home bias detected in the recommended portfolios may come from unobservable factors. I examine the polls published over the period 1Q/1989 to 2Q/1999. The Economist has changed the participants and format of the poll several times since the poll’s inception in 1989. In some years, polls were not published quarterly. For example, polls were published five times in 1990, three times in 1991, and twice in 1992. And while eighteen institutions participated in the poll from Q1/1989 to Q2/1999 in total, no single institution participated in every poll. For this study, I choose ten institutions that participated most actively in the poll over the ten year period. For some analysis requiring consistent participation over a given period of time, I look at the period 1Q/1993 to Q1/1997, during which the ten institutions provided recommendations consistently for each quarter. The ten institutions, listed in Table 2, are from three regions of the world: two are from the US, two from Japan,3 and six from European countries. It is difficult to designate home countries for four European institutions: Robeco Group Asset Management, Bank Julius Baer, UBS International Investment, and Credit Suisse Asset Management. I treat these institutions simply as European. These ten institutions have good reputations and large assets under management. They also have strong worldwide information networks through their presence around the world in the form of representative offices, branches or subsidiaries. Table 2 Poll participants Institution Merrill Lynch Lehman Brothers Nikko Securities Daiwa Europe Credit Agricole Robeco Group Asset Mgt Bank Julius Baer (Zurich) UBS International Investment Commerz Int’l Captal Mgt Credit Suisse Asset Mgt

Home

Label

US US Japan Japan France Europe Europe Europe Germany Europe

US1 US2 JP1 JP2 FR ER1 ER2 ER3 GRM ER4

The table lists the ten institutions that most actively participated in the Economist portfolio polls between 1Q/89 and 2Q/99.

3 I treat Daiwa Europe as a Japanese firm. Its home bias pattern to be presented in the next section is consistent with this treatment.

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Table 3 Descriptive statistics Basic statistics US Rest of Americas UK Germany France Rest of Europe Japan Rest of Asia

Mean return

Min return

Max return

Std. dev

0.148 0.120 0.119 0.121 0.089 0.192 0.043 0.139

−0.186 −0.409 −0.391 −0.251 −0.386 −0.113 −0.594 −0.533

0.386 0.531 0.468 0.709 0.551 0.583 1.205 0.950

0.071 0.129 0.102 0.114 0.134 0.088 0.208 0.179

The table shows how each geographic market performed over the period from 1Q/93 through 1Q/97. The mean return, maximum return, minimum return, and standard deviation of returns are all annualized numbers.

When evaluating the performance of recommended portfolios, I use national equity indices and exchange rates collected from Datastream. Appendix A contains equity indices and exchange rates used in the analysis. Using these indices and exchange rates, I compute the performance of each of the eight geographic markets over the sample period, as presented in Table 3. I use the Morgan Stanley Composite Index (MSCI) value-weighted world market portfolio as a proxy for the world market portfolio—which the Economist poll refers to as the neutral portfolio.

3. Findings 3.1. Are recommendations homogeneous? The question I examine in this section is whether the recommended equity portfolios are different from the world market portfolio and across institutions. Examining this question is tantamount to testing the validity of the predictions of the Capital Asset Pricing Model (CAPM). In the world where the CAPM holds, the investors will hold the same identical equity portfolio, namely, the market portfolio. In a simplistic version of the international CAPM,4 investors will hold the same portfolio, namely, the world market portfolio, in which case there will be no home bias. I first examine whether the equity portfolios recommended by institutions are different statistically from the world market portfolio. It will be helpful for a reader to see the second panel of Table 1 for the format of an international equity portfolio. I use the MSCI based 4 The international version of the CAPM (for example, Adler and Dumas, 1983) predicts that in addition to

the world market portfolio, the investor holds the hedge portfolio for the purpose of hedging inflation or exchange risk. However, one may be able to ignore the hedge portfolio for several reasons, especially when one analyzes this poll data. First, the nature of the poll states that exchange risk is not a concern for the institutions in the poll (“. . . no overriding currency considerations. . . ”). Second, investors may successfully hedge exchange risk. Third, an empirical study by Cooper and Kaplanis (1994) suggests that actual portfolio holdings are not explained by the desire to hedge against inflation risk.

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value-weighted world market portfolio as a proxy for the world market portfolio and then conduct a simple χ 2 test of equality to see whether the recommended equity portfolios are the same as the world market portfolio. I find that the test rejects the null hypothesis of the equality at the 99% confidence level. Second, I examine whether the recommended equity portfolios of the ten institutions are statistically different from each other. A similar χ 2 test rejects the null hypothesis that the recommended equity portfolios are identical at the 99% confidence level. In sum, these tests suggest that the recommend portfolios are different from the world market portfolio and from each other. Another related prediction of the CAPM is that in the equilibrium, investor’s risk tolerance is irrelevant to the composition of the risky assets in the portfolio. With the poll data, one can test whether risk tolerance is irrelevant in portfolio formation. To test this question, I examine the basic portfolio allocated among cash, equities, and bonds. Part 1 of Table 1 shows the format of the basic portfolio. I choose the proportion of equities in the portfolio as a proxy for investor risk tolerance. And I use the bond-to-equity ratio as a composition of risky assets in the portfolio, treating cash as a riskless asset. If the CAPM holds, the bond-equity ratio (i.e., composition of risky assets) will be constant with respect to the proportion of equities (i.e., investor risk tolerance).5 The poll data show that investor risk tolerance is not related to the risky-asset composition in the portfolio. Figure 1 graphs the relations between investor risk tolerance and risky-asset composition for each quarter between 1Q/93 and 1Q/97. In each graph, the horizontal axis represents the proportion of equities in the portfolio (i.e., the proxy for the level of risk tolerance), and the vertical axis represents the bond-to-equity ratio in the portfolio (i.e., the mix of risky assets). Because I look at ten institutions that consistently participated in the poll over the period, there are ten corresponding points in each graph. The graphs exhibit a clear pattern. The bond-to-equity ratio decreases as the equity proportion in the portfolio increases. If the level of risk tolerance is irrelevant to portfolio allocation decisions, the points in each graph should fall on a horizontally flat line. The downward sloping relation in the graphs suggests that the more risk tolerant an institution is, the less likely it recommends equities relative to bonds. 3.2. Home bias The preceding analysis indicates that the portfolios recommended by participating institutions in the poll are different from the world market portfolio and are from each other. The next question to examine is whether this heterogeneity across recommended portfolios is related to the nationality of the recommending institutions—that is, whether recommended portfolios exhibit the pattern of home bias. In this analysis, I examine the equity portfolios from the poll and look into two types of home bias: (i) home bias in portfolio holdings, and (ii) home bias in portfolio rebalancing. 5 Canner et al. (1997) follow the same procedure and test whether US domestic cash–bond–equity portfolios

recommended by major institutional investors supports this CAPM prediction. They find that risk tolerance is negatively related to bond-to-equity ratios of the recommended portfolios.

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Fig. 1. Risk tolerance and risky-asset mix. The points in each graph correspond to portfolios recommended by the ten institutions listed in Table 2. Some graphs display fewer than ten points because the same basic portfolio is recommended by more than one institution.

3.2.1. Home bias in portfolio holdings Assessing the extent of home bias in portfolio holdings requires benchmarks. I choose two benchmark portfolios: (i) the world market portfolio, and (ii) the average portfolio recommended by institutions of other countries.

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Fig. 1. Continued.

The world market portfolio weights are based on capitalization of the eight geographical markets according to Morgan Stanley Capital International (MSCI). The average portfolio, the second benchmark, for each institution is obtained by taking the average of portfolio weights recommended by institutions from countries that are not the institution’s home. The world market portfolio weights have been used in several previous studies as the benchmark against which home bias is evaluated (Cooper and Kaplanis, 1994; French and Poterba, 1991). The advantage of using the average portfolio as an additional benchmark is that the weights of the average portfolio reflect the consensus expectations of

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Fig. 1. Continued.

institutions (from other countries) about the future performance of the geographic markets. This is because recommended portfolios are formed on the basis of the institutions’ expectations for the future performance of each geographic market. Thus, a deviation of an institution’s recommendations from this average portfolio will indicate a deviation of the institution’s expectations from those of investors of other nationalities. In contrast, the world market portfolio weights, the first benchmark, do not necessarily reflect the institutions’ expectations for the future performance of respective markets.

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Some previous studies on home bias use the mean–variance optimal world portfolio as a benchmark. However, the use of the mean–variance optimal world portfolio can be problematic, especially for this study. First, with the relatively small amount of historical returns at quarterly frequency, it is not easy to construct a statistically reliable mean– variance optimal portfolio. Especially, stock indices for some emerging countries are not available for the period before mid-1980s. Further, as Britten-Jones (1999) suggests, the standard errors in estimating the weights in the mean–variance optimal portfolio can be so large that the null hypothesis of no home bias relative to the mean–variance optimal portfolio will not be rejected.6 To assess home bias, I look at the international equity portfolios recommended over the period between Q1/1993 and Q1/1997. During this period, the ten institutions in Table 2 provided recommendations consistently. Table 4 presents the results of the analysis. The t-tests strongly suggest that most institutions exhibit home bias in their recommendations against either of the two benchmarks or both. For example, while the two US institutions, Merrill Lynch and Lehman Brothers, tend to underrecommend their home markets relative to the world market portfolio, they both tilt their recommendations towards the US market relative to the second benchmark—the average of US weights that non-US

Table 4 Home bias in portfolio holdings Institution(s) Merill Lynch Lehman Brothers Nikko Daiwa Europe Credit Agricole Commerz International Non-German Euro inst’s European institutions Joint test

Bias US US Japan Japan France German German Europe –

Benchmark I

Benchmark II

Avg. bias

% bias

t-stat

Avg. bias

% bias

t-stat

−3.6 1.6 6.9 −0.7 6.4 1.0 1.9 5.3 –

−9.3 5.2 29.7 −2.7 165 23.2 55.6 19.3 8.4

−2.25 0.59 6.34** −0.73 6.22** 1.30 3.53** 6.02** 2.37*

2.8 7.9 9.5 1.9 4.8 −0.1 2.0 7.9 –

8.6 25.4 42.9 8.5 78.7 −1.9 47.0 30.7 23.2

1.61 3.03** 23.48** 1.94* 6.33** −0.13 7.13** 12.08** 8.28**

The table presents results on whether an institution’s recommended weight in its home market is greater than the corresponding weight in the benchmark portfolios. I choose two benchmarks. Benchmark I is the MSCI based value weighted world market portfolio. Benchmark II is the average portfolio recommended by the institutions from other countries. The null hypothesis is that the recommended weight in the home market is the same as the corresponding weight in the benchmark portfolio. The joint test in the last line of the table examines collective home bias of the participating institutions. Average bias is the average of the differences between the recommended weight and the benchmark weight over the sample period, in percent; % bias is average bias divided by the average home weight in the benchmark portfolio over the sample period. * Significance at the 90% level. ** Idem., 99%.

6 Britten-Jones even demonstrates that the null hypothesis that the optimal weights of foreign countries for US investors are zero cannot be rejected. If we apply the same magnitude of standard errors of optimal weight estimates from Britten-Jones’ study, the extent of home bias in the recommended portfolios in this study will be statistically insignificant against the mean–variance optimal portfolio as a benchmark.

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institutions recommend. Thus, US institutions tend to recommend the US market more than institutions of other nationalities do. Nikko, a Japanese institution, overweights its investment towards Japan against both benchmarks. Daiwa, the other Japanese institution, overweights towards home against the average portfolio recommended by non-Japanese institutions. Further, Credit Agricole’s recommendations are significantly biased in favor of its home, France, against both benchmarks. Interestingly, the German institution (Commerz International) does not show significant home bias. In contrast, the recommendations of other European institutions are biased in favor of Germany against both benchmarks. When all recommendations of European institutions are combined, I find that these institutions significantly overweight European markets, as reported in the second-to-last line of Table 4. Overall, home bias appears to be very strong in most institutions’ recommendations. I also conduct a joint test of home bias by combining the recommendations of all participating institutions. The result, reported in the last row of Table 4, shows that home bias is substantial collectively against both benchmarks. I also examine home bias over an extended sample period from Q1/1989 to Q2/1999 (Table 5). Note that the preceding analysis of home bias in Table 4 uses a relatively small number of observations, seventeen data points for each institution. During the periods prior to Q1/1993 and after Q2/1997, the ten institutions participated in the poll only intermittently. The number of observations displayed in the last column of Table 5 is different across institutions, reflecting different participation levels among them. I find that the institutions’ preference for their respective home markets is remarkably high over this extended sample period as well. As t-tests in Table 5 indicate, institutions exhibit home bias relatively to either the first or the second benchmark or both. For some institutions, the degree of home bias is stronger over this extended sample period. While Commerz’s home bias was not significant on the first benchmark over the short sample period, its home bias now is significantly positive over this extended period. Daiwa’s home bias was negative on the first benchmark over the short sample period, its home bias is now positive, though not significant, over this extended period. Table 5 Home bias in portfolio holdings for a longer sample period Institution(s) Merill Lynch Lehman Brothers Nikko Daiwa Europe Credit Agricole Commerz International Non-German Euro inst’s European institutions Joint test

Bias US US Japan Japan France German German Europe –

See notes in Table 4. * Significance at the 90% level. ** Idem., 99%.

Benchmark I

Benchmark II

Avg. bias

% bias

t-stat

Avg. bias

% bias

−3.3 −0.2 6.6 3.5 6.1 1.0 2.3 7.2 3.2

−8.2 0.8 29.9 27.6 154 26.9 64.9 27.0 17.3

−3.07 −0.12 5.31** 1.33 7.25** 1.70* 6.12** 7.76** 3.88**

3.7 7.5 8.0 8.5 4.4 −0.7 0.7 4.8 6.5

12.3 23.0 41.4 36.1 71.4 −9.2 24.7 18.3 26.3

# obs. t-stat

2.92** 4.09** 4.55** 3.93** 6.32** −1.17 1.55 4.88** 8.75**

26 25 26 36 27 31 40 40 153

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3.2.2. Home bias in portfolio rebalancing Now, I examine home bias in terms of the frequency of portfolio rebalancing. The idea behind this analysis is that if an institution has superior information regarding its home market, it will receive (more reliable) signals from its home market more frequently than it will from the other markets. These frequent (and more reliable) signals from home will prompt the institution to adjust its home market weight more frequently than other market weights. To assess the relative intensity of home-market-weight adjustment frequency, I examine the period between Q2/1993 and Q1/1997, during which the ten institutions provide recommendations consistently. This period contains seventeen quarterly data points, meaning that each institution has a maximum of 16 chances to adjust its portfolio. The first panel in Table 6 displays the frequency with which the institutions change weights over the sample period in each of the eight markets. Institutions often leave weights unchanged. For example, Merrill Lynch (US1) changes its home (i.e., the US) market weight fourteen times out of a maximum of sixteen, leaving the US weight unchanged twice. And as the total frequency of portfolio-weight adjustments in the bottom row shows, some institutions—US1, for example—are very active in adjusting recommendations whereas other institutions—ER1 for example—are not so active. First, the poll data suggest that US and Japanese institutions exhibit home bias in the frequency of portfolio-weight adjustments. Panel B in Table 6 provides the frequency of home-market-weight adjustments made by US and Japanese institutions, in relation to the frequency of adjustments in other market weights. US institutions tend to make more frequent adjustments in US weights than in other market weights. Merrill Lynch (US1) makes as frequent, or more frequent adjustment in the US weight than other market weights, except the Japan weight. Lehman Brothers (US2) makes as frequent, or more frequent adjustments in US weight than any other market weight. A similar observation is made for two Japanese institutions. Nikko (JP1) makes as frequent, or more frequent adjustment in the Japan weight than in other market weight, with only one exception. The frequency of the Japan-weight adjustment by Daiwa Europe (JP2) is the greater than that of any other market weight adjustment. Assessing European institutions’ home bias in their frequency of portfolio-weight adjustments requires a slightly different approach because four markets can be considered as Europe: UK, Germany, France, and the rest of Europe. For each institution, I calculate the ratio of the combined number of adjustments in these European market weights to the total number of adjustments for all markets. Panel C in Table 6 presents this relative frequency of Europe-weight adjustments for each institution. It seems that though there are a couple exceptions, many European institutions tend to adjust European-market-weights very frequently, relative to non-Europe institutions. Especially, ER1, ER2, ER3, and ER4 make Europe-weights more frequently than any non-Europe institution. The above observation of institutions changing home market weights more often than foreign market weights may need to be understood in relation to Tesar and Werner (1995). Tesar and Werner report that turnover rates for local equity holdings of foreign investors are high, meaning that foreign investors buy and sell local shares a lot. One might raise a possibility that Tesar and Werner’s observation predicts that institutions will change their portfolio’s foreign market weights very frequently. Note, however, that Tesar and Werner’s

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Table 6 Home bias in portfolio rebalancing A. Frequency of portfolio-weight adjustments (maximum: sixteen) Market

US1

US2

JP1

JP2

FR

ER1

ER2

ER3

GRM

ER4

US Rest of Americas UK Germany France Rest of Europe Japan Rest of Asia Total adjustment

14 11 12 14 10 13 15 14 103

12 10 11 8 10 10 9 11 81

11 4 9 14 10 7 12 12 79

12 5 12 12 10 9 15 12 87

9 6 7 5 8 10 10 6 61

13 5 11 11 11 13 12 10 86

8 6 9 6 10 10 9 6 64

7 2 6 7 12 13 7 8 62

14 4 11 7 10 12 12 11 81

8 3 8 6 5 12 11 6 59

B. Home bias in portfolio-weight adjustment for US and Japanese institutions Institution

Proportion of non-US/Japanese markets for which the US/Japanese institution makes more frequent adjustments in weights than in US/Japan weight

US1 US2 JP1 JP2

1/7 0/7 1/7 0/7

C. Relative frequency of Europe-weight adjustments US1

US2

JP1

JP2

FR

ER1

ER2

ER3

GRM

ER4

0.476

0.481

0.506

0.494

0.492

0.535

0.547

0.613

0.494

0.525

D. Relative frequency of portfolio-weight adjustments Market

US1

US2

JP1

JP2

FR

ER1

ER2

ER3

GRM

ER4

US Rest of Americas UK Germany France Rest of Europe Japan Rest of Asia Total adjustment

0.136 0.107 0.117 0.136 0.097 0.126 0.146 0.136 1

0.148 0.123 0.136 0.099 0.123 0.123 0.111 0.136 1

0.139 0.051 0.114 0.177 0.127 0.089 0.152 0.152 1

0.138 0.057 0.138 0.138 0.115 0.103 0.172 0.138 1

0.148 0.098 0.115 0.082 0.131 0.164 0.164 0.098 1

0.151 0.058 0.128 0.128 0.128 0.151 0.140 0.116 1

0.125 0.094 0.141 0.094 0.156 0.156 0.141 0.094 1

0.113 0.032 0.097 0.113 0.194 0.210 0.113 0.129 1

0.173 0.049 0.136 0.086 0.123 0.148 0.148 0.136 1

0.136 0.051 0.136 0.102 0.085 0.203 0.186 0.102 1

Refer to Table 2 for institution labels.

result does not directly address weight-rebalancing of worldwide equity portfolios. After all, an institution can trade local shares actively in a given local equity market, while the market’s weight in the institution’s worldwide portfolio remains unchanged. In fact, the analysis in Section 3.4 of this paper suggests that weight-rebalancing in the actual worldwide equity portfolio can be very slow. Thus, the observation from the recommended portfolios is not necessarily in conflict with that of Tesar and Werner. To explore the poll data further, I introduce the concept of a “weak spot.” I define a weak spot as a geographic market for which an institution makes relatively few weight adjustments. A careful inspection of panel A of Table 6 reveals that many institutions,

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mostly non-US institutions, have weak spots. To show this more clearly, panel D of Table 6 tabulates relative frequency of portfolio-weight adjustments in each of the eight markets for each institution. The number in each cell of panel D is the ratio of the frequency of weight adjustments for the respective market to the total number of weight adjustments for all markets made by the institution over the sample period. First, one can see that non-US institutions make relatively few adjustments in the rest of the Americas compared with two US institutions. The relative portfolio-weight adjustments by non-US institutions in this market are below 10 per cent, while the relative portfolio-weight adjustment by two US institutions are over 10 per cent—0.107 and 0.123, respectively. Second, the rest of Europe seems to be a weak spot for Japanese institutions. JP1 and JP2 make relatively few weight adjustments in this market: 0.089 and 0.103, compared with other institutions. It is interesting to note that the relative frequency of weight adjustment in the rest of Europe by the six European institutions is generally greater than that in the same market by any of the four non-Europe institutions. Third, the rest of Asia appears to be a weak spot for most of European institutions. Except for GRM, the relative weight-adjustment frequency for the rest of Asia weight is lower for European institutions than that for any US and Japanese institution. Interestingly, those markets for which institutions make relatively few weight changes seem to coincide with markets located far away from the institutions’ headquarters. The rest of Americas for which Japanese and European institutions make few weight changes are far from their Japan and Europe headquarters—i.e. farther than the region is from the US headquartered institutions. The same goes to other weak spots. The rest of Europe is farther from Japan-headquartered institutions than the region is from the US or Europeheadquartered institutions. Finally, the rest of Asia is farther from Europe-headquartered institutions than the region is from the Japan or US headquartered institutions. In summary, the analysis in this subsection shows that institutions tend to make more frequent changes in their home market weights relative to other market weights. In addition, many institutions tend to make relatively few changes in the distant geographic markets. These two findings fit the description of home bias based on information asymmetry. If an institution has superior information regarding its home market and thus receives information more frequently from home, it will change its home market weight relatively more often. And if an institution receives little information from distant geographic markets, it will change weights of these markets relatively less often. 3.3. Performance of recommended portfolios In this section, I examine the performance of the recommended portfolios, relative to that of benchmark portfolios. I use the Sharpe ratio, defined as the mean return divided by the standard deviation of returns, to determine portfolio performance. Two benchmark portfolios used in the comparison are: (i) the portfolio invested 100% in home market (i.e., home market index), and (ii) the world market portfolio (i.e., the neutral portfolio).

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Because I am examining internationally diversified portfolios, the performance comparisons can be conducted under several alternative assumptions regarding the choice of numeraire currencies and the possibility of currency hedging. Table 7 presents the Sharpe ratios for the recommended and benchmark portfolios over the period between 1Q/93 and 1Q/97. The first panel shows the performance of portfolios using the US dollar as the numeraire currency. The key assumption here is that investors who follow the recommendations provided at time (quarter) t will assess investment performance at time (quarter) t + 1 (i.e., one quarter after the recommendation is made). In effect, I evaluate one-quarter returns on the recommended portfolios relative to those on the benchmark portfolios. I report the following results from the table. First, no recommend portfolios outperform the neutral portfolio in Sharpe ratio over the sample period. In panel A, the Sharpe ratio for the neutral portfolio is 0.89, while the Sharpe ratios for recommended portfolios are lower, ranging from 0.71 to 0.88. Second, some recommended portfolios underperform home indices while others do not. For example, the recommended portfolios of the US institutions underperform their home index: the Sharpe ratios for US1 and US2, 0.71 and 0.74, are lower than the Sharpe ratio for the US market index, 0.94. On the other hand, the recommended portfolios of two Japanese institutions outperform the Japanese market index over the sample period. I also evaluate recommended portfolios’ performance under a different assumption regarding the numeraire currency. The results in panel B of Table 7 are obtained assuming that institutions intend to maximize returns expressed in their home currency. The table shows that the recommended portfolios of eight institutions are outperformed by the neutral portfolio. Only two institutions—ER1 and ER3—beat the neutral portfolio in their recommendations. Recommended portfolios do not perform well against respective national indices, either: only three institutions beat home indices. Finally, I evaluate portfolio performance, assuming that institutions eliminate the effect of exchange rate Table 7 Performance of recommended portfolios US1

US2

JP1

JP2

GRM

ER4

Avg. µ

Avg. σ

FR

ER1

ER2

ER3

Recom. prt. Neutral prt. 100% home

0.71 0.89 0.94

0.74 0.89 0.94

0.65 0.89 0.06

A. Portfolio performance in US dollars 0.80 0.81 0.85 0.68 0.88 0.86 0.89 0.89 0.89 0.89 0.89 0.89 0.06 0.35 0.87 0.87 0.87 0.70

0.73 0.89 0.87

10.3 10.7 11.0

13.4 12.0 23.5

Recom. prt. Neutral prt. 100% home

0.83 0.90 0.98

0.99 0.90 0.98

0.68 0.90 0.06

B. Portfolio performance in home country currency 0.82 0.82 0.88 0.79 0.98 0.91 0.82 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.06 0.43 0.84 0.84 0.84 0.70 0.84

11.1 11.1 11.4

13.1 12.3 23.3

Recom. prt. Neutral prt. 100% home

0.71 0.89 0.94

0.74 0.89 0.94

0.29 0.36 0.02

C. Portfolio performance with perfect hedge 0.33 0.55 0.55 0.44 0.61 0.54 0.48 0.36 0.59 0.55 0.55 0.55 0.55 0.55 0.02 0.38 0.62 0.62 0.62 0.63 0.62

10.7 11.1 11.0

21.7 20.7 24.5

For the four European investors, ER1, ER2, ER3, and ER4, their home market return is assumed to be the weighted average of returns on UK, Germany, France, and the rest of Europe indices. The last two columns report averages of mean returns and standard deviations—both in %—from the portfolios in the same row. Refer to Table 2 for institution labels.

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fluctuations by implementing perfect currency hedge. That is, portfolios earn local currency returns from each local market. Panel C of Table 7 presents the results. I find that only three institutions—US2, ER3, and GRM—provide recommendations that outperform the neutral portfolio. The recommended portfolios perform relatively well relative to home indices: seven institutions beat home indices in their recommendations. Overall, it seems that recommended portfolios do not offer superior investment performance relative to benchmark portfolios in terms of the return per standard deviation. Recommended portfolios generally perform poorly, especially relative to the neutral portfolio. To check the robustness of this finding, I have repeated the same performance evaluation over an extended sample period—from Q1/89 to Q2/99. Though I do not present the results, I make the same observation over this extend period: recommended portfolios do not offer superior returns to investors relative to the benchmarks. Previous studies also report that recommended or surveyed portfolios do not offer superior returns. For example, Graham and Harvey (1997) find that market-timing abilities of recommendations in investment newsletters are not particularly good. Bange (2000) looks at two groups of surveyed portfolios—the first one held by individual investors and the other held by institutional investors. Neither of the two groups of surveyed portfolios do not outperform the market. Similarly, De Bondt (1990) reports that a sample of surveyed economists lacks predictive ability for the direction and magnitude of stock market changes. 3.4. Volatility of implied returns In this subsection, I examine implied returns derived from recommended portfolio holdings of the poll and those from actual aggregate international portfolio holdings of US investors. With a set of assumptions, one can derive implied returns from a given portfolio. Recommended portfolios are formed on the basis of the returns anticipated by the institutions for each of the eight geographic markets. Since the portfolio recommendations are made without interference of transaction costs and other capital barriers, implied returns derived from recommended portfolios will be close to returns anticipated by institutions. In contrast, implied returns derived from actual international portfolio holdings are not likely to be a pure reflection of returns anticipated by investors because of the presence of transaction costs and other capital barriers. The purpose of this analysis is to see whether implied returns from recommended portfolios are more volatile than those from actual international portfolios. From recommended portfolios and actual international portfolios collected over a period of time, one can construct a time series of implied returns for these two groups of portfolios. Then, one can evaluate the volatility of the two groups’ implied returns. High (low) volatility of implied returns means that the holder of the portfolio adjusts his portfolio weights in large (small) increments and/or very fast (slowly). Our prior is that implied return volatility for recommended portfolios will be higher than that for actual portfolios. The returns implied by actual portfolio holdings will not be much variable because of transaction costs and other capital barriers interfering with portfolio weight adjustment process in the case of the former. For another reason, it is conceivable that investors in actual investments may be more

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risk averse than the participants in the portfolio poll, who are not subject to real monetary loss from recommendations. To derive implied returns, I follow the equity premium literature and assume that the investor maximizes the CRRA (constant relative risk aversion) utility function of the following form: W 1−ρ , (1) 1−ρ where U , W , and ρ represent the level of utility, the level of wealth, and the level of relative risk aversion, respectively. The utility maximization problem, given the mean and variance of returns, leads to the following first-order condition: U (W ) =

µt = ρΣt xt ,

(2)

where µt is the vector of anticipated (i.e., implied) returns, Σt is the variance–covariance matrix of returns, and xt is the vector of portfolio weights at time t. In the portfolio poll data, there are eight geographical markets. Thus, µt and xt are 8 × 1 vectors and Σt is an 8 × 8 matrix. For an assumed level of relative risk aversion, ρ, Eq. (2) allows us to derive the implied return vector, µt , for each period, t, if the variance–covariance matrix, Σt , and the portfolio holding, xt , are given. For this analysis, Σt is estimated from the return data from 2Q/88 through the previous quarter, i.e., t − 1. The portfolio holding, xt , is the recommended portfolio of each institution. To get a reasonable level of relative risk aversion, ρ, I draw on the French and Poterba (1990), who set ρ equal to 3, calibrated to the international portfolio held by US investors. In the end, following this procedure, I obtain a time series of implied return vector, µt , for each institution. Table 8 presents the standard deviations of implied returns estimated over the period from 1Q/93 to 1Q/97. In panel A, the relative risk aversion is assumed to be 3 (i.e., ρ = 3). Columns 4–13 provide the standard deviations of implied returns for each geographic market, estimated from the recommended portfolios of each respective institution. The third column of the panel reports the average of standard deviations of implied returns across the institutions for each geographic market. For the purpose of evaluating the magnitude of the standard deviations of implied returns, the second column presents the standard deviations of actual stock returns for each geographical market.7 Clearly, one can see that implied returns are more variable than actual returns across institutions and markets. The average standard deviations of implied returns (on the third column) are at least twice as large as the standard deviations of the actual returns (on the second column). Next, panel B of Table 8 assumes ρ = 1, which is an extremely low level of risk aversion.8 Even under this extremely low level of risk aversion, implied returns seem more variable than actual returns in many cases. The average standard deviation of implied returns is greater than the standard deviation of actual returns for four of the eight markets: US, UK, the rest of Europe and Japan. 7 Appendix A provides data sources used to compute actual stock returns for each geographic market. 8 ρ = 1 is an extremely low level of risk aversion. In comparison, note that the level of relative risk aversion

required to explain the equity premium puzzle is over thirty, or sometimes up to one hundred.

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Table 8 Volatility of implied returns from recommended portfolios Market

MKTa

AVRGb

US1

US2

JP1

JP2

FR

US Rest Am. UK Germany France Rest Eur. Japan Rest Asia

0.071 0.129 0.102 0.114 0.134 0.088 0.208 0.179

0.298 0.248 0.353 0.309 0.381 0.399 0.905 0.285

0.296 0.254 0.364 0.298 0.370 0.414 1.006 0.306

0.284 0.225 0.384 0.479 0.499 0.479 1.123 0.298

0.327 0.292 0.374 0.203 0.341 0.393 0.821 0.313

A. ρ = 3 0.307 0.274 0.224 0.216 0.368 0.337 0.269 0.365 0.376 0.424 0.396 0.373 1.032 0.807 0.261 0.263

US Rest Am. UK Germany France Rest Eur. Japan Rest Asia

0.071 0.129 0.102 0.114 0.134 0.088 0.208 0.179

0.099 0.083 0.118 0.103 0.127 0.133 0.302 0.095

0.099 0.085 0.121 0.099 0.123 0.138 0.335 0.102

0.095 0.075 0.128 0.160 0.166 0.160 0.374 0.099

0.109 0.097 0.125 0.068 0.114 0.131 0.274 0.104

B. ρ = 1 0.102 0.091 0.075 0.072 0.123 0.112 0.090 0.122 0.125 0.141 0.132 0.124 0.344 0.269 0.087 0.088

ER1

ER2

ER3

GRM

ER4

0.316 0.303 0.435 0.332 0.449 0.481 1.253 0.343

0.258 0.191 0.309 0.239 0.313 0.305 0.488 0.256

0.321 0.272 0.341 0.384 0.393 0.458 0.996 0.264

0.347 0.312 0.330 0.195 0.292 0.380 1.118 0.293

0.245 0.190 0.293 0.324 0.355 0.314 0.401 0.247

0.105 0.101 0.145 0.111 0.150 0.160 0.418 0.114

0.086 0.064 0.103 0.080 0.104 0.102 0.163 0.085

0.107 0.091 0.114 0.128 0.131 0.153 0.332 0.088

0.116 0.104 0.110 0.065 0.097 0.127 0.373 0.098

0.082 0.063 0.098 0.108 0.118 0.105 0.134 0.082

Refer to Table 2 for institution labels. a Standard deviations of actual returns of each geographic market over the sample period. b The averages of standard deviations of implied returns from the ten recommended portfolios in the same row.

The above analysis indicates that implied returns from the recommended portfolio holdings are highly variable. Now, for comparison, I examine the volatility of returns implied by actual international portfolio holdings. Using the same procedure as above, I derive implied returns from the actual aggregate international stock portfolio held by US investors. To construct a time series of actual international portfolio holdings, comparable to the time series of the recommended portfolio holdings, I use aggregate US portfolio investment flows, published by the US Treasury Department. An issue to address in this procedure is the dominance of the US market weight in the aggregate US equity holdings. The US market portion in the aggregate US stock portfolio exceeds 90% throughout the sample period. As a consequence, including the US market portion in the analysis will result in extremely small variations in foreign market weights in the aggregate international stock portfolio. To avoid the problem, I exclude the US market portion from the analysis. In effect, US investors are assumed to invest only in foreign stock markets. Table 9 reports the variability of implied returns from the actual international portfolio holdings under several alternative assumptions on the level of risk aversion. I find that the variability of implied returns measured in standard deviation is extremely small. With ρ = 3 and ρ = 20, implied returns are much less variable than the actual return variability reported in the second column under MKT. Indeed, a level of risk aversion as extremely high as 100 is required to make the standard deviation of implied returns comparable with that of actual market returns.

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Table 9 Volatility of implied returns from actual US portfolio Market

MKTa

ρ =3

ρ = 20

ρ = 100

Rest of America UK Germany France Rest of Europe Japan Rest of Asia

0.129 0.102 0.114 0.134 0.088 0.208 0.179

0.002 0.003 0.003 0.003 0.003 0.008 0.002

0.012 0.019 0.026 0.027 0.023 0.055 0.017

0.058 0.093 0.103 0.113 0.117 0.277 0.073

The table reports the period between 1Q/93 and 1Q/97. The resulting US international portfolios do not contain investments in the US market, i.e. the US market weight is assumed to be zero. a Standard deviations of actual returns of each geographic market over the sample period.

The result from Table 6 suggests that investors in actual international portfolio investments adjust their portfolio weights in small increments and/or very slowly. To gain a better understanding of this observation, it helps to revisit the result of Tesar and Werner (1995) that turnover rates for foreign equity holdings are high. Their result of high turnover rates for foreign equity holdings does not necessarily mean that global investors adjust weights in their worldwide portfolio very actively. A global investor can engage in an active buy-and-sell strategy within each geographic market—thus the turnover rates are high for each market’s holding, while the portfolio weight assigned to each geographic market in his worldwide portfolio is kept relatively stable. The result in Table 9 suggests that investors may not actively change portfolio weights in their worldwide equity portfolio. Hence, combining the result of Tesar and Werner with that of this study, it seems that investors who engage in international investments may be active traders in that they trade actively shares within a given local country or region, while they may not be active traders in that they do not actively change global portfolio weights. To make a summary of the results in this subsection, it appears that the weights of the actual aggregate international equity portfolio do not change as much or fast as those of recommended portfolios. This may not be a surprising observation. What is interesting, however, is the magnitude of the implied return volatility from actual aggregate international equity portfolios. For recommended portfolios, the level of relative risk aversion that makes the implied return volatility comparable to actual return volatility is about 1. In contrast, for the actual aggregate international portfolio, we need the relative risk aversion as high as 100 in order to make the implied returns volatility comparable to actual returns volatility. The implication of these results is that capital barriers and high risk aversion in actual international investments may cause international equity portfolio weights to change in extremely small increments and/or slowly.

4. Summary and discussion This study of the recommended portfolios from the Economist Quarterly Portfolio Poll produces several interesting findings. The recommended portfolios are different across institutions and different from the world market portfolio. Contrary to the prediction of

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the CAPM, the degree of risk-tolerance seems to affect the mix of risky assets in the recommended portfolios. The heterogeneity among the recommended portfolios is related to home bias of institutions. Home bias in the recommended portfolios is observed on two levels. First, institutions tend to recommend their home markets more, relative to the weight in the world market portfolio and relative to the weight recommended by institutions from other countries. Second, institutions tend to change their home market weights more frequently than other market weights. Further, many institutions appear to have weak spots, that is, geographic markets for which they make infrequent weight changes. These weak spots coincide with markets that are geographically distant from the institutions’ headquarters. Finally, I find that actual aggregate portfolio holdings are adjusted in very small increments and very slowly compared to recommended portfolio holdings. In order to look for the source of home bias in the recommended portfolios, one can draw first on the information-asymmetry story from the literature, for example, Coval and Moskowitz (1999). In reporting home bias in US domestic portfolio holdings, Coval and Moskowitz argue that investors may have easier access to information about companies located near them. In a similar way, one can argue that institutions participating in the Economist portfolio poll may have better access to detailed macroeconomic information about their home markets through their ties to government officials and research firms located in the same region. Moreover, institutions tend to build a research structure that focuses on their home markets because home markets are their main customer base and their home customers invest mostly in domestic assets. A home-biased research structure can produce better information (and more frequent buy-and-sell signals) on the home markets than on other markets. However, one can raise a question as to whether the information-asymmetry story fully explains the home bias pattern in the poll data. If the recommended portfolios are formed on the basis of superior information (especially on home markets) held by the institutions, they are expected to produce superior returns relative to benchmark portfolios. Evidence suggests otherwise: the recommended portfolios underperform benchmarks on many occasions. To be fair, however, previous studies also indicate that the performances of recommended or surveyed portfolios are generally poor. Nonetheless, the poor performance of recommended portfolios from the poll calls for caution in attributing home bias entirely to the information-asymmetry story. The information asymmetry story can be interpreted more carefully. Most institutions in the poll have a presence in all eight markets in the form of representative offices, branches, or subsidiaries, meaning that they have strong worldwide information networks. And the kind of information needed to form recommended portfolios in the poll is mostly macroeconomic data regarding eight geographical markets. Macroeconomic data are relatively easy to obtain, compared to firm-level information. Thus, it is unlikely that an institutions as a whole faces an information disadvantage with respect to any geographic market. It may be that home bias in the poll data is not the bias of an institution as a whole, but of an individual respondent (whom the Economist magazine contacts for the poll). An individual is more likely to exhibit a bias because the amount of information an individual can acquire and process is limited, relative to what an institution as a whole can. In any case, it is still quite interesting that statistically significant home bias is observed over a period

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of time as long as more than ten years, as Table 5 reports. Over such a long time span, different individual respondents in the same institution must have taken turns to respond to the poll’s request. Thus, even if it is the case that an individual, not an institution as a whole, is responsible for the exhibited home bias, it is still intriguing that this bias seems robust to changing personnel. Finally, an alternative or supplementary explanation for home bias can be found in the literature on investor behavior. Several recent studies report that investors tend to be optimistic on the performance of their home equity markets. For example, Strong and Xu (2000) examine a survey data to find that fund managers express more bullish views on their home equity markets than do fund managers from other countries. There are other studies that indicate investors’ optimism toward their home economies or stock markets (Shiller et al., 1996; Kilka and Weber, 2000). These studies suggest that a certain portion of home bias in the Economist poll data may come from investor optimism about home markets. However, investor optimism is not likely to explain the entire home bias detected in this poll data because it is not able to explain why institutions adjust their home-market weights more frequently. It is a difficult task to determine how much of home bias can be attributed to investor optimism vis-à-vis information asymmetry. Further, even if investor optimism is related to home bias, it remains to be seen whether home biased portfolio holdings induce optimism, or optimism induces home bias. I leave these interesting but challenging issues for future work.

Table 10 Market US Rest of the America Canada Mexico Chile UK Germany France Rest of Europe Switzerland Netherland Italy Sweden Spain Belgium Denmark Finland Norway Japan Rest of Asia

Equity indices US

Local

MSUSAML

TOTMKUS

MSCNDA$ MSMEXI$ MSCHIL$ MSUTDK$ MSGERM$ MSFRNC$

TOTMKCN TOTMKMX TOTMKCL TOTMKUK TOTMKBD TOTMKFR

MSSWIT$ MSNETH$ MSITAL$ MSSWDN$ MSSPAN$ MSBELG$ MSDNMK$ MSFIND$ MSNWAY$ MSJPAN$ MSASXJ$

TOTMKSW TOTMKNL TOTMKIT TOTMKSW TOTMKES TOTMKBG TOTMKDK TOTMKFN TOTMKNW TOTMKJP TOTMKAJ

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Acknowledgments This paper is based on the second chapter of my dissertation at the University of Michigan. I am grateful to the doctoral committee members, Linda Tesar, Gordon Hanson, Joshua Coval, and Tyler Shumway, for their guidance and support. I thank Takeo Hoshi (the editor) and two anonymous referees for helpful comments and suggestions. Any errors are my responsibility. I acknowledge financial support from the Kookmin University research grant program.

Appendix A. Indices used in this study Table 10 lists equity indices used to evaluate the performance of the recommended and benchmark portfolios. All listed indices are from Datastream. Datastream mnemonics of the indices are reported. For the rest of the Americas and the rest of Europe, I use weighted averages of indices belonging to each respective region. The weights are based on capitalization of each country’s market from the Emerging Markets Fact Book (1997).

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