Mutual Funds Latin Crisis

Emerging Markets Review 6 (2005) 211–237

www.elsevier.com/locate/econbase

Mutual fund preferences for Latin American equities

surrounding financial crises

Susan Elkinawy *

Loyola Marymount University, Hilton Center for Business, One LMU Drive, MS 8385,

Los Angeles, CA 90045-2659, United States

Received 25 January 2005; received in revised form 28 April 2005; accepted 24 May 2005

Abstract

Using data on dedicated Latin American mutual funds and nearly 1000 Latin American stocks

during the Asian and Russian currency crises, I find that the effects of certain firm characteristics on

mutual fund stock ownership are different than in non-crisis years. In response to crises, fund

managers increase their holdings of cross-listed firms. This finding is evident among closed-end

funds, suggesting features beyond liquidity influence stock ownership. Funds also reduce their

holdings of firms competing with Russia’s main exports. These results suggest that in addition to

liquidity, trade links and governance concerns are important determinants of portfolio choice during

crises.

D 2005 Elsevier B.V. All rights reserved.

JEL classification: G11; G15

Keywords: Mutual funds; Financial crises; Emerging markets; Foreign portfolio investment

1. Introduction

The dramatic reduction of barriers to foreign investment in recent years has led to

increased interest in the study of foreign investor behavior. For example, Kang and Stulz

1566-0141/$ -

doi:10.1016/j.

* Tel.: +1 31

E-mail add

see front matter D 2005 Elsevier B.V. All rights reserved.

ememar.2005.05.001

0 338 2345.

ress: [email protected].

S. Elkinawy / Emerging Markets Review 6 (2005) 211–237212

(1997) and Dahlquist and Robertsson (2001) examine the preferences of foreign investors

in Japan and Sweden in order to improve understanding of the home bias phenomenon. In

the 1990s, foreign investors had a particular interest in emerging markets due to the

liberalization of many developing countries to foreign investment combined with

increased privatization of state-owned enterprises. While the influx of foreign investment

resulted in strong economic growth for emerging markets, the ease of capital mobility also

left these countries vulnerable to abrupt changes in investor sentiment with subsequent

turmoil in the currency and stock markets.

The financial crises of the late 1990s were unlike earlier crises that were generally local

or regional in nature. The turmoil that began in Asia and Russia in 1997 and 1998 resulted

in cross-border contagion effects that reached markets globally, with developing regions

like Latin America particularly vulnerable. Chriszt (1999) indicates that the problems in

Asia and Russia affected Latin America through trade and financial markets, as Latin

American stock prices plunged in 1997 and 1998. Bekaert et al. (2005) conduct formal

correlation tests of idiosyncratic shocks and confirm that contagion from Asia spread to

Latin America. The financial channel of contagion was most significant, since the Asian

crisis led investors to treat all emerging markets as an asset class and reassess their

investments in Latin America. These events have provided a new motivation for

examining the actions of foreign investors.

I provide evidence on foreign investor behavior surrounding the Asian and Russian

crises by determining particular firm characteristics that affect the portfolio choices of

U.S.-based mutual fund managers in dedicated Latin American funds. My results indicate

that fund managers’ preferences change subsequent to a crisis originating outside of their

region. This research is motivated by studies such as Kaminsky et al. (2001a) who find

that dedicated Latin American funds engage in contagion trading by selling assets from

one country when prices fall in another.

This study complements the contagion literature on fundamental factors as well as

herding and momentum trading.1 Fundamental factors include trade linkages or other

macroeconomic conditions. However, as Forbes (2004) argues, these factors are typically

aggregate country-level measures that ignore a good deal of within-country variation. She

indicates that micro-level data like the attributes of individual firms can provide greater

insight into the way financial shocks are propagated, since the effect of externally based

crises on local companies can vary widely. In addition, firm characteristics yield deeper

insight into the types of stocks that are subject to momentum trading and offer another

dimension to portfolio choice. Although this study examines how mutual funds react to

financial crises, the findings may provide a first step to better understanding the role of

foreign investors in causing the transmission of crises.

My sample consists of the portfolio holdings of all existing Latin American mutual

funds from 1996 to 2001, twenty-six open-end and eleven closed-end funds. This time

period encompasses the currency attacks in Thailand and surrounding countries in mid-

1 Herding occurs when a group of investors exhibit similar buying and selling behavior toward certain stocks,

while momentum trading refers to the systematic purchase and sale of stocks based on historical returns. See

Bikhchandani and Sharma (2000), Choe et al. (1999), Kaminsky et al. (2001a), Borensztein and Gelos (2001),

Froot et al. (2001), and Kim and Wei (2002).

S. Elkinawy / Emerging Markets Review 6 (2005) 211–237 213

to-late 1997, followed by the Russian default in August 1998 and the devaluation of

the Brazilian real in January 1999. The mutual fund holdings are matched with a

database of nearly 1000 stocks in the four largest Latin American markets. Equity

portfolio holdings are likely to be revealing in light of evidence from Kaminsky et al.

(2001b) who show only small changes in Latin American mutual funds’ cash holdings

over this period.

I find that over the 6-year period, mutual fund managers investing in Latin America

prefer large, highly visible and liquid firms with strong profitability. Fund managers also

prefer firms with low leverage, low dividend yields, and firms that are less likely to

compete with Asian exports. These findings are consistent with similar studies on the U.S.

and foreign developed markets, suggesting that mutual fund preferences for stock

characteristics are largely robust to geography.

In response to the Asian and Russian currency crises, the effects of certain firm

characteristics on mutual fund stock ownership are different than in non-crisis years.

Funds increase their holdings of ADRs and reduce their holdings of firms that operate in

similar industries as Russia’s main exports. My findings suggest that firm attributes

associated with trade links are among the key factors that influence portfolio choice in a

developing region during crises. This result is consistent with the findings of Forbes

(2004) and Bekaert et al. (2005), who show that trade channels contribute to the spread of

crises across regions.

The remainder of this paper is organized as follows. Section 2 develops an empirical

model of investor behavior and puts forth a series of hypotheses concerning differences in

mutual fund preferences between crisis and non-crisis periods. Section 3 describes the data

used in the study, and Section 4 presents the determinants of mutual fund stock ownership.

Section 5 concludes and offers suggestions for future work.

2. Empirical model and hypotheses

2.1. Empirical model

The approach I take in this paper is to estimate the quantity of a firm’s outstanding

equity held by dedicated Latin American mutual funds in a given year during or

surrounding the financial crises. In the basic specification, holdings of outstanding equity

are assumed to be a function of firm characteristics. Additional specifications include a set

of dummy variables representing the crisis years and a set of firm-characteristic/crisis-

period interaction terms. For ease of exposition, the present discussion will center on the

basic specification.

The dependent variable is the proportional mutual fund ownership of a particular firm’s

shares in a given year. Although many mutual funds voluntarily disclose their holdings on

a monthly basis to tracking services such as Morningstar, the Securities and Exchange

Commission only requires semi-annual reporting, in accordance with the fund’s fiscal

year-end. As a result, the portfolio holdings of the sample funds will be at different dates,

depending on the fund’s fiscal year and the timeliness of reporting to Morningstar.

Following Falkenstein (1996), I calculate the dependent variable as the aggregate


percentage of a specific stock owned by the sample of Latin American mutual funds in a

particular calendar year, ownershipit. This is defined as follows:

ownershipit ¼XMi

m¼1

sharesheldi;tmoutstandingi;tm

ð1Þ

where sharesheldi,tm represents the number of shares held of stock i by fund m at time tm,

and outstandingi,tm is the total number of shares outstanding of stock i at time tm. Time tmrepresents the date to which the portfolio data correspond to fund m’s holding of stock i,

and Mi is the total number of Latin American equity funds that hold the stock. The

dependent variable is calculated annually from 1996 through 2001.

Over 75% of all sample firms are not held by any mutual funds. This implies that the

value of the dependent variable, mutual fund stock ownership, clusters at zero. Ordinary

least squares estimation does not account for the censored nature of the data, leading to

biased coefficient estimates. Thus, I test the hypotheses using Tobit estimation, which

accounts for the clustering at zero and leads to consistent estimates.

Due to the panel nature of the data, the primary Tobit specification is random effects.

Random effects assume that any unobserved heterogeneity present among the Latin

American stocks is uncorrelated with the explanatory variables. This may not be a valid

assumption. A firm with more block ownership, for instance, could either benefit or harm

minority shareholders, depending on the composition of the blockholders and whether

expropriation is more prevalent by the firm’s management or by governmental authorities.

Mitton (2002), for example, finds that firms with outside blockholders experienced higher

stock returns in Asia during the 1997–1998 crisis. In Latin America, data on the ownership

composition of specific firms is limited. Correlation between block ownership and the

incidence of expropriation could therefore cause a downward bias in the coefficient

estimates in the model, since the composition of the blockholders in the sample is

unknown and a firm’s sensitivity to corruption is unobservable.

Although fixed-effects estimation does not make the assumption that unobserved

heterogeneity is uncorrelated with firm attributes, this specification cannot be easily

implemented with a Tobit model. In addition, Greene (2003) finds that the parameters in

the fixed-effects model will be biased when the time series is small. Thus, it is not clear

that fixed-effects are the preferred specification here.

McDonald and Moffitt (1980) show that the coefficient estimates generated from the

Tobit specification can be decomposed into two parts. One part measures the effect of the

independent variables on the probability of the dependent variable being above zero, and

the other part measures the marginal effect conditional upon a positive value of the

dependent variable. This decomposition is potentially important in this study, since

aggregate mutual fund stock ownership is small for the majority of firms in the sample.

Section 4 examines this decomposition in more detail.

2.2. Hypotheses

The primary objective of this study is to determine whether the effect of firm

characteristics on stock ownership by mutual fund managers in Latin American markets is


different in response to a period of financial turmoil relative to a period of tranquility.

Because of the relatively unique nature of the recent financial crises, research has not

established clear predictions on the firm attributes that foreign portfolio investors in

emerging markets should value most highly during a turbulent period. I expect U.S.-based

mutual fund managers investing in Latin America to be primarily concerned with seeking

efficient portfolios while adapting to cash outflows (for open-end funds) or discounts to

net asset value (for closed-end funds).

Assuming that fund managers use firm characteristics to generate expectations about

risk and return, I predict that firm attributes provide different signals about what risk and

return will be in a financial crisis than they do in tranquil times. For instance, large firms

are generally less risky than small firms. During a crisis, large firms could become more

desirable to fund managers due to greater liquidity. If so, this finding would suggest that

the risk-return tradeoff of large firms is more favorable during crises.

Characteristics associated with asymmetric information, financial health, trade

channels, and governance are likely to be important determinants of mutual fund stock

ownership. Furthermore, I predict that fund managers perceive the effect of these firm

characteristics on shareholder wealth and risk differently in crises versus non-crisis

periods. The variables used to test the hypotheses are defined in Appendix A and

explained more fully below.

2.2.1. Asymmetric information

Merton (1987) hypothesizes that investors with incomplete information choose familiar

securities. Grinblatt and Keloharju (2001) suggest that foreigners suffer from an

informational disadvantage in investing abroad due to language, culture, and distance

barriers. Research has focused recently on these implicit barriers, in light of a reduction of

external barriers such as transaction costs and regulation. During a financial crisis, Johnson

et al. (2000) indicate that investor confidence falls due to perceived weaknesses in the

legal institutions of developing markets. Less certainty in a developing region’s investment

environment suggests that information asymmetry among foreign investors is greater

during crises relative to tranquil periods.

Variables such as firm size and whether a firm is cross-listed on a U.S. stock exchange

have been used in several studies, including Dahlquist and Robertsson (2001) and Lang et

al. (2003) to test for asymmetric information. Within the U.S., Falkenstein (1996) finds

that mutual funds prefer stocks with high profiles, since these stocks require lower search

costs and have less uncertainty in risk estimation. During crises, if mutual fund managers

perceive greater uncertainty and are subject to greater volatility in their flows, this

perception is reflected in the types of firm attributes they value. I expect that characteristics

associated with high visibility will become more important to mutual fund managers in a

crisis than in a tranquil period, since a crisis increases the difficulty in assessing a stock’s

risk.

The inclusion of a firm in a major stock index is one firm characteristic associated with

high visibility. Covrig et al. (2001) find that firms that are components of a stock index are

associated with greater stock ownership by foreign mutual fund managers, which the

authors attribute to increased firm recognition. Although the goal of actively managed

funds is to outperform their respective benchmark indexes, the increased instability in a


region during a crisis should be accompanied by higher search costs for individual stocks.

Thus, I predict that index stocks become more desirable during a crisis compared to a

period of tranquility.

Many studies document the preference towards large firms by both foreign and

institutional investors.2 Besides greater investor recognition, large firms are also more

liquid than small firms. There may, however, be reasons that large firms are undesirable in

a developing market, particularly during a crisis period. Schiffer and Weder (2001) suggest

that smaller firms may be exposed to less country risk than large firms. Small firms in

countries with weak legal environments can more easily avoid taxes and regulation due to

informal arrangements. In contrast, large firms may be more vulnerable to corruption by

government officials due to their higher visibility and generally higher profits. Gaviria

(2002) finds that while corruption is common for firms of all sizes in Latin America,

smaller firms tend to be more severely affected. Thus, the effect of firm size on stock

ownership should be greater in crisis periods relative to tranquil periods.

Cross-listed firms are also associated with greater foreign recognition. Lang et al.

(2003) find that non-U.S. firms that are listed on U.S. exchanges experience greater

analyst coverage, and thus greater investor recognition, relative to other non-U.S. firms.

Several studies suggest that cross-listed firms also have better governance.3 Like large

firms, cross-listed firms tend to be more liquid. Kaminsky et al. (2000) find that during

crises U.S.-based Latin American open-end fund managers tend to liquidate their most

liquid positions. Due to their high liquidity, ADRs should be easier to sell than non-ADRs

in order to meet fund redemptions. This suggests that while ADRs have desirable

attributes that could lead to increased ownership, fund managers may actually exhibit

weakened preference to ADRs due to redemption needs or perceived susceptibility of

ADRs to contagion. The difference in the effect of cross-listed firms on mutual fund stock

ownership between crisis and non-crisis periods is therefore uncertain.

2.2.2. Financial health

Financial variables provide insight into the health of a firm, which fund managers pay

close attention to in their portfolio-making decisions. Three variables of interest are a

firm’s return on assets, its current ratio, and its leverage ratio. Dahlquist and Robertsson

(2001) use the latter two variables as proxies for short-term and long-term financial

distress. The inclusion of the current ratio is motivated by Forbes (2004), who suggests

that crises could be transmitted to firms in other countries via a credit crunch due to lack of

liquidity. It is possible that the financial crises resulted in a dflight to qualityT response bymutual fund managers, leading to increased ownership. At the same time, stocks with

strong financial health may also be easiest to liquidate, leading to reduced ownership in

response to crises. Similar to the opposing effects of cross-listed firms on portfolio

holdings, the difference in the effect of financial health variables on mutual fund stock

ownership between crisis and non-crisis periods is uncertain.

2 See Falkenstein (1996), Del Guercio (1996), Kang and Stulz (1997), Dahlquist and Robertsson (2001),

Gompers and Metrick (2001), and Aggarwal et al. (2003).3 See Coffee (2002), Mitton (2002), Klapper and Love (2002), Reese and Weisbach (2002), and Doidge et al.

(2004).


2.2.3. Trade channels

Forbes (2004) finds that trade channels contributed to the transmission of the Asian

and Russian crises to other regions. Among her findings is that firms whose main product

line was in the same industry as a major export from East Asia and firms competing with

Russian exports experienced lower abnormal stock returns during the crises. She attributes

these findings to product competitiveness and income effects. Product competitiveness

occurs when the devaluation of a country’s currency (as occurred in Asia) causes that

country’s exports to be relatively cheaper in world markets, and therefore reduces the

competitiveness for other countries that compete with those exports. Income effects occur

when the devalued currency reduces the country’s purchasing power, so firms that export

to the affected country will suffer from reduced demand for their goods and services.

Chriszt (1999) indicates that while exports contribute only a modest amount to Latin

American economies, a number of countries depend on oil and metals for export earnings.

Some countries such as Chile depend heavily on mineral exports to Asia, and mineral

products are one of Russia’s main exports. This suggests that firms perceived to be most

vulnerable to both types of trade channel effects should be relatively less attractive to

investors.

The industry of each Latin American company is identified via its North American

Industry Classification (NAIC) code.4 Appendix B provides Forbes’ (2004) list of the

major exports of the main crises zones. I hypothesize that mutual fund stock ownership in

Latin America should be negatively associated with firms operating in similar industries as

those of Asia’s and Russia’s main exports in crises relative to non-crisis periods.

2.2.4. Governance

Corporate governance is also an important element of investment decisions. Firms with

good governance structures contribute to firm value by aligning the interests of

stockholders and managers. Legal systems to protect shareholders vary greatly among

countries, suggesting that governance concerns play an important role in portfolio choice

overseas.

Although the ownership restrictions imposed on mutual funds limit their role in

corporate governance, like all investors mutual fund managers choose investments that

they expect to provide a fair return. Since minority shareholders rights are frequently not

well protected in emerging markets, it benefits managers to choose firms with

characteristics associated with low agency costs. Studies such as Klapper and Love

(2002) and Aggarwal et al. (2003) find that firms with better corporate governance in

emerging markets have higher market valuations and attract more investment by U.S.-

based mutual funds. Thus, I expect governance concerns to influence firm ownership by

mutual fund managers in Latin America during financial crises.

Studies of corporate governance generally examine variables such as the composition and

compensation of the board of directors as well as the percentage of inside ownership. Much

of this data is difficult or impossible to obtain for Latin American firms. Of the four markets

in my sample, only Brazil and Chile report ownership data. The disclosure indicates the

4 Data on the share of exports to specific countries by individual Latin American firms is not available.


ownership percentage of blockholders (defined as shareholders owning 5% or more of the

firm’s shares), but inside ownership and board of director information are not reported.

For Brazil and Chile, the degree of ownership concentration could provide indirect

evidence regarding corporate governance concerns. As Holderness (2003) indicates,

research has not definitively determined whether outside blockholders increase or decrease

firm value. This is due to two potentially offsetting effects: improved monitoring of

management by blockholders versus the private benefits of control. La Porta et al. (2000)

find that French civil law countries (which characterize the legal system in Latin America)

have the weakest protection of outside investors, and state that concentrated ownership is

needed in these countries as a commitment to limit expropriation. If fund managers

perceive block ownership as a favorable governance characteristic in Latin America, I

expect the effect of block ownership on mutual fund stock ownership in Brazil and Chile

to be greater during crises relative to non-crisis periods.

3. Data and summary statistics

3.1. Data

I choose U.S. institutions to examine the role of investor behavior during crises, since

they represent one of the largest investor groups in foreign securities. Aggarwal et al.

(2003) indicate that U.S. institutions are the world’s largest source of equity capital. Davis

and Steil (2001) indicate that approximately 50% of flows to emerging markets in the

1990s were portfolio flows by institutional investors.

The use of dedicated Latin American mutual funds is designed to reduce confounding

factors that can influence portfolio decisions within the mutual fund universe. For example,

broadening the sample to include all emerging market funds introduces the possibility that

fund managers are making regional currency bets in their decision to invest in Latin

America. In this situation, examining firm characteristics to increase understanding of

investor reaction to externally based crises would be less informative, since these fund

managers may be basing their decisions more heavily at the country level than at the firm

level. Since the funds in my sample are dedicated to the region, the attributes of the

individual firms should be the primary factors behind their portfolio choices.

An additional advantage to studying the behavior of dedicated Latin American funds is

that the investor group remains relatively constant over the time period. The sample period

covers a pre-crisis year (1996), followed by the Asian and Russian crises (1997–1998), the

Brazilian crisis (1999), and a post-crisis period (2000–2001). These periods therefore

provide an opportunity to contrast portfolio preferences between crisis and non-crisis years

for a reasonably homogeneous group of investors.

I identify U.S.-based Latin American equity mutual funds from Morningstar for open-

end funds and from CDA/Wiesenberger’s Investment Companies Yearbook (1996–2001)

for closed-end funds. These funds and their portfolio holdings are identified and collected

on an annual basis from 1996 through 2001. Table 1 lists the funds included in the sample

with numbers indicating the months in which portfolio data are reported. This list consists

of distinct portfolios only (so that multiple share classes of funds are excluded) and

Table 1

List of U.S.-based Latin American equity mutual funds

Fund type Fund name Inception date Year

1996 1997 1998 1999 2000 2001

Open ABN AMRO Latin Amer Eq Jun 96 12 12 6 9 11 3

Open AIM Latin America growth Aug 91 6 9 9 5 8

Open BT investment Latin Amer Eq Oct 93 9 9 9 11

Open Chase Vista Latin Amer Eq Dec 97 12

Open Evergreen Latin America Nov 93 10 4 4 11 11 9

Open Excelsior Latin America Dec 92 3 9 9 9 11 4

Open Federated Latin Amer Grth Feb 96 11 11 12 11

Open Fidelity Adv Latin America Dec 98 4 4 4

Open Fidelity Latin America Apr 93 4 4 4 4 4 4

Open Govett Latin America Mar 94 12 12

Open Invesco Latin American Grth Feb 95 7 9 9 9 4

Open Ivy South America Nov 94 6 9 9 9

Open Kemper Latin America Dec 97 9 6 9

Open Merrill Lynch Latin Amer Sep 91 5 8 8 5 9 7

Open Montgomery Latin America Jun 97 9 9

Open Morgan Stan Ins LatinAm Jan 95 12 12 9 6 11 6

Open Morgan Stanley Latin Am Gr Dec 92 9 9 9 9 9 6

Open Nicholas-Apple Latin Am Nov 97 11 11 6 10

Open Offitbank Latin Amer Eq Sel Feb 96 12 12 10 7 7 11

Open Prudential Latin America Jun 98 8 5 5

Open Scudder Latin America Dec 92 6 9 9 9 9 9

Open T. Rowe Price Latin America Dec 93 4 10 6 6 9 9

Open TCW Galileo Latin America Eq Mar 93 10 6 3 11 6 4

Open Templeton Latin America May 95 3 9 6 9 9 9

Open Van Kampen Latin Amer Jul 94 6 6 9 6 6 6

Open Wright EquiFund-Mexico Aug 94 6 9 11 2 6

Closed Argentina fund Oct 91 10 10 10 10 10 10

Closed Brazil fund Apr 88 12 12 12 6 6 6

Closed Brazilian equity fund Apr 92 3 3 3 3 3 3

Closed Chile fund Sep 89 12 12 12 12 12 12

Closed Emerging Mexico fund Jun 90 12 6 6

Closed Latin America equity fund Oct 91 12 12 12 12 12 12

Closed Latin America investment fund Aug 90 12 12 12 12 6

Closed Latin American discovery fund Jun 92 12 12 12 12 12 12

Closed Latin America smaller companies fund Nov 94 10 10 10

Closed Mexico equity and income fund Aug 90 7 7 7 7 7 7

Closed Mexico fund Jun 81 10 10 10 10 10 10

Total number of portfolios: 31 32 34 33 29 23

Numbers indicate month of portfolio disclosure.

Source: Morningstar and the Securities and Exchange Commission.


excludes index funds in order to examine active portfolio decision-making by fund

managers. Twenty-six open-end funds existed over the period. Eleven closed-end funds

existed in 1996 but by the end of 2001 two funds had liquidated and one merged with

another existing closed-end fund. Since mutual funds frequently undergo changes such as

name, investment advisor, mergers into other funds, or liquidation I check for these events

to ensure data integrity.

-15%

-10%

-5%

0%

5%

10%

15%

- -

-35%

-30%

-25%

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

Jan-

95

May

-95

Sep

-95

Jan-

96

May

-96

Sep

-96

Jan-

97

May

-97

Sep

-97

Jan-

98

May

-98

Sep

-98

Jan-

99

May

-99

Sep

-99

Jan-

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May

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Sep

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Jan-

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May

-01

Sep

-01

Sep-9

8

A

B

Sep-0

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Jan-

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May

-01

Sep-0

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May

-00

Jan-

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Sep-9

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May

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Jan-

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May

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Jan-

98

Sep-9

7

May

-97

Jan-

97

Sep-9

6

May

-96

Jan-

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Sep-9

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May

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Jan-

95



The funds’ holdings are obtained from Morningstar and the U.S. Securities and

Exchange Commission’s EDGAR system. Both Morningstar and the Securities and

Exchange Commission indicate whether the holding is an ADR. The primary source for

ADR ratios is the Bank of New York, which I use to convert the number of ADR shares

into underlying firm shares. Similar to Falkenstein (1996), I use the most recently reported

portfolio holdings in the Morningstar database as of year-end for the open-end funds. For

the closed-end funds and for missing open-end portfolio data, I use the annual shareholder

report (N-30D filing) from the Securities and Exchange Commission. Since the majority of

the funds report in the second half of the year, the implication is that the portfolios

analyzed in the crisis years reflect choices made subsequent to the onset of the crises.

The primary source for firm financial data is Economatica, a private company that

specializes in the collection of Latin American company data. This source tracks detailed

financial data on every publicly listed firm from 1986 (or later if a firm became public at a

later date). In addition, de-listed firms are maintained in their database, allowing for

analysis of holdings that are no longer listed on the stock exchanges.

The universe of firms available for mutual fund investment includes all stocks in the

four main Latin American markets tracked in the Economatica database. The four markets

include Argentina, Brazil, Chile, and Mexico and consist of over 950 publicly traded

stocks in the Economatica universe. With few exceptions, the firms listed in the four

markets were freely open to foreign investment in 1996, the starting period for this study.

Mexico and Brazil make up the largest percentage of the funds’ holdings throughout the 6-

year period. Argentina, Brazil, Chile, and Mexico collectively represent over 90% of the

market value of the funds’ holdings over the sample period.

I choose the 1996 to 2001 period because it encompasses the recent global crises and the

ensuing effect on mutual fund behavior. Bekaert et al. (2005) find that unlike the Mexican

crisis, the Asian crisis worsened contagion in Latin America. Bazdresch and Werner (2000)

indicate that by 1996 markets had recovered from the Mexican crisis, making it a suitable

starting period for examining the crises that followed. Fig. 1A indicates that open-end

mutual funds experienced outflows beginning in the second half of 1997 that persisted

through late 1998/early 1999, with the devaluation of the Brazilian real. Fig. 1B shows that

the discounts of closed-end funds exhibited a relatively continuous increase in magnitude

over the period, with the largest discounts occurring in 1998 and a modest recovery

beginning in the middle of 2000. Interestingly, the total market capitalization of Latin

America grew between 1996 and 1997. This is consistent with the fact that the aggregate

market value held by the funds grew from $4 billion in 1996 to $6 billion in 1997, an increase

of 50%. This increase is likely due to a combination of increased open-end fund flow in the

first half of 1997 combined with appreciation of asset values. By the end of 1998 the market

value of the funds’ holdings fell below 1996 levels, likely in reaction to the Russian crisis.

My sample period provides an opportunity to examine portfolio decisions made in response

to the two most widespread crises that were not Latin American in origin.

Fig. 1. (A) Aggregate dollar flow of open-end Latin American mutual funds scaled by total net assets, 1995–2001.

Flow is computed as the percentage monthly growth in total net assets less capital appreciation. Source: The

Center Research in Security Prices Mutual Funds Database. (B) Average premium/discount of closed-end Latin

American mutual funds, 1995–2001. Source: Barron’s National Business and Financial Weekly.

Table 2

Summary statistics of mutual fund stock ownership and firm characteristics, 1996–2001

N Mean Median Std. dev.

Mutual fund stock ownership (%) 5715 0.35 0 2.08

Mutual fund stock ownership (%)a 1259 1.59 0.71 4.20

Total assets ($millions) 4540 1540 257 5780

ADR program (%) 5715 10.76 0 30.99

S&P index (%) 5715 3.74 0 18.99

Current ratio (%) 4438 9.13 1.20 292.12

Return on assets (%) 4492 �2.20 2.60 114.01

Dividend yield (%) 3720 4.95 1.90 11.77

Leverage (%) 4138 25.18 21.20 30.52

Asian exports (%) 5715 38.64 0 48.70

Russian exports (%) 5715 5.56 0 22.93

Share turnover (%) 3440 0.09 0.04 0.43

Beta 2961 0.62 0.60 0.39

Stock return (%) 3954 7.38 -8.90 101.28

Free float (%) 3013 31.22 26.30 23.84

Mutual fund stock ownership is calculated as the aggregate percentage of shares held by U.S.-based Latin

American equity mutual funds of a publicly listed firm in Argentina, Brazil, Chile, or Mexico in a calendar year.

The data on fund ownership are collected from Morningstar and the Securities and Exchange Commission’s

EDGAR database. The data on firm characteristics are collected from Economatica and the Bank of New York.

The sample of firms excludes Mexican banks (NAIC code 52), resulting in 953 firms over the 6-year period. Firm

characteristics are defined in Appendix A. aMutual fund ownership on non-zero holdings only.


3.2. Summary statistics

Table 2 provides summary statistics for the firm attributes along with the dependent

variable, firm ownership, over the 1996 to 2001 period. The mean and median size of a Latin

American firm is $1.5 billion and $257 million, respectively, indicating that the mean is

highly skewed by a few very large firms. Approximately 11% of the firms have ADR

programs, while 4% are part of the Standard and Poor’s Latin American 40 index. The

average turnover ratio and beta are only 0.09% and 0.62, suggesting that many of these

stocks trade infrequently. The average free float percentage in Brazil and Chile is 31%,

indicating that close to 70% of the shares are held by blockholders. Latin American mutual

funds hold a small percentage of firm equity, as the mean percentage held of a Latin

American company’s stock is only 0.3% of the shares outstanding. However, since 70% of

the shares are unavailable to minority shareholders, Latin American funds hold about 1% of

the total shares available. This percentage is non-trivial, given the small number of funds.5

4. The determinants of Latin American mutual fund stock ownership

Table 3 presents the baseline results of the random effects Tobit model of stock

ownership. These equations pool observations across crisis and non-crisis years to

5 Latin American funds also hold approximately 1% of the market value of all shares traded in the main Latin

markets.

Table 3

Random effects Tobit model of Latin American mutual fund stock ownership, 1996–2001

Model (1) Model (2) Model (3)

Tobit coeff. p-value Tobit coeff. p-value Tobit coeff. p-value

Total assets 0.7433 0.000 0.8027 0.000 5.3633 0.001

Total assets squared �0.1130 0.005

ADR program 0.8676 0.007 1.1717 0.000 1.1516 0.001

S&P index 1.9938 0.000 2.0951 0.000 2.2696 0.000

Current ratio �0.0112 0.778 �0.0026 0.948 0.0036 0.925

Return on assets 0.0473 0.000 0.0483 0.000 0.0499 0.000

Dividend yield �0.0316 0.023 �0.0312 0.026 �0.0318 0.022

Leverage �0.0130 0.105 �0.0100 0.211 0.0366 0.119

Leverage squared �0.0008 0.036

Asian exports �0.5187 0.117 �0.6433 0.071 �0.7457 0.032

Russian exports 0.7045 0.140 0.5947 0.203 0.5238 0.256

Share turnover 0.3743 0.000 0.3498 0.000 0.3589 0.000

Beta 1.2767 0.001 1.3366 0.000 1.3442 0.000

Stock return 0.0004 0.727 0.0009 0.425 0.0009 0.431

Interest rates �0.0308 0.067

Foreign exchange rate �0.0004 0.967

GDP growth rate 0.0186 0.444

U.S. market return 0.0254 0.000

Year 1997 �0.4158 0.124 �0.4235 0.117

Year 1998 �1.3681 0.000 �1.3867 0.000

Year 1999 �1.2221 0.000 �1.2298 0.000

Year 2000 �1.5995 0.000 �1.5996 0.000

Year 2001 �2.1244 0.000 �2.1127 0.000

Constant �15.5445 0.000 �15.6409 0.000 �61.7057 0.000

Percentage of uncensored obs. 0.2844 0.2844 0.2844

N 2064 2064 2064

Log likelihood �1713.48 �1699.79 �1693.37

In models (2) and (3), the omitted year is 1996. Variable definitions are provided in Appendix A.

Dependent variable: Aggregate proportional ownership of shares held of a Latin American stock by mutual fund

managers in a calendar year.


determine overall preferences for firm characteristics over the period.6 In model 1, I

include control variables for macroeconomic conditions in addition to the firm

characteristics. The macroeconomic variables for each country include the level of

interest rates, the percentage change in foreign exchange rates relative to the U.S. dollar,

the growth rate of gross domestic product, and the returns of the U.S. stock market (all are

adjusted for inflation). The negative coefficient on interest rates suggests that mutual fund

stock demand is lower when local interest rates increase. This is consistent with the fact

that government authorities in Latin America raised interest rates during the crises to

reduce capital outflows, signaling weaknesses in the region’s economic fundamentals.

Interestingly, the coefficient on the U.S. market return is positive, suggesting that mutual

fund stock ownership in Latin America increases with the returns of the U.S. stock market.

6 The regression analyses omit Mexican banks (NAIC code 52) due to an industry-wide change in accounting

methods in 1998. The Mexican banking industry represents about 2% of all firms available in the four markets.


This finding may indicate that the U.S. market serves as a proxy for expected global

market conditions or that a leader/follower relationship exists between the U.S. and Latin

American markets.

Controlling for macroeconomic conditions, the positive coefficients on total assets,

ADR program, S&P index, return on assets, and share turnover in Table 3 indicate that

managers prefer large, highly visible firms with strong financial health and high liquidity.

The positive coefficient on beta suggests that fund managers prefer stocks with high

systematic risk. Bennet et al. (2003) find that institutional investors, particularly less

conservative investors like mutual funds, have shifted their preferences toward riskier

securities over time. However, another possibility is that non-synchronous trading is

contributing to this result. Stocks that trade more frequently will have higher betas,

suggesting that beta is capturing the degree of trading activity. The negative coefficient on

leverage suggests aversion toward firms that are more likely to experience financial

distress. These results are similar to those documented in studies conducted on other

markets, suggesting institutional investor preference is largely robust to geography and

time period.

The negative coefficient on the dividend yield is consistent with studies such as

Dahlquist and Robertsson (2001), who find that foreign investors prefer growth firms. It

has been argued that the dislike for dividends may be motivated by tax considerations.

Three of the four Latin American markets withhold a portion of dividends paid to foreign

investors. As Christoffersen et al. (2003) indicate, the U.S. grants tax credits to taxable

accounts for foreign taxes paid, so that these accounts can at least partially offset the

withholding. However, mutual funds are not eligible for the tax credits, so the foreign

dividend withholding tax reduces the funds’ returns.

In model 2 of Table 3, I replace the macroeconomic variables with year dummy

variables in order to control for the effect of time on mutual fund stock ownership. The

results are similar to those of model 1, suggesting that the year dummy variables capture

the macroeconomic conditions in the four markets.7

In model 3, I examine more closely the relation between firm size and ownership, as

well as between leverage and ownership. Dahlquist and Robertsson (2001) find that both

foreign and domestic institutional investors prefer large firms in Sweden, leading the

authors to conclude that this preference is an institutional investor bias rather than simply a

foreign investor bias. However, firms that are too large could be undesirable to minority

shareholders in an emerging market due to greater agency costs associated with less ability

to monitor the actions of the firm’s management, controlling shareholders, or

governmental agencies in the home country. I investigate this possibility further by

including a quadratic term for total assets in model 3. The negative coefficient on the

quadratic term confirms that mutual funds do exhibit a non-linear preference toward firm

size, suggesting support for the agency hypothesis of large firms.

I also investigate whether firm leverage exhibits a non-linear effect on mutual fund stock

ownership. As Jensen (1986) argues, debt can serve as a governance mechanism by reducing

7 The fact that a model including both time dummy variables and macroeconomic variables leads to

multicollinearity between the two sets of variables strengthens this argument. Future specifications, therefore,

include only controls for time.


the agency costs of free cash flow through the contractual obligation associated with interest

payments. Harvey et al. (2004) find that debt creates value for emerging market firms that

have high expected overinvestment and managerial agency problems. However, too much

debt increases the risk of financial distress. Table 3 indicates that leverage has a significant

negative effect on mutual fund ownership, although the relationship does not appear to be

non-linear at conventional significance levels.

Since the mutual funds own equity in a relatively small number of Latin American

firms, it is likely that firm attributes have a much stronger effect on the decision to

purchase a stock than on the quantity of the stock purchased. I investigate this possibility

by decomposing the marginal effects of the firm attributes using the procedure developed

by McDonald and Moffitt (1980). Table 4 presents the decomposition for each of the

variables shown in model 3 of Table 3. As suspected, each coefficient has a greater

marginal effect on the probability of a stock being held than on the level of stock

ownership. For example, a 1% increase in total assets results in a 0.55 percentage point

increase in the probability that the stock is held by mutual funds, while a similar increase

in total assets results in a 0.38 percentage point increase on the level of stock ownership,

given that the stock is owned.

Table 4

McDonald–Moffitt decomposition of marginal effects, 1996–2001

Marginal effect on the

probability of holding a stock

Marginal effect on the level of stock

ownership, given positive ownership

Total assets 0.5456 0.3825

Total assets squared �0.0115 �0.0081

ADR program 0.1171 0.0821

S&P index 0.2309 0.1619

Current ratio 0.0004 0.0003

Return on assets 0.0051 0.0036

Dividend yield �0.0032 �0.0023

Leverage 0.0037 0.0026

Leverage squared �0.0001 �0.0001

Asian exports �0.0759 �0.0532

Russian exports 0.0533 0.0374

Share turnover 0.0365 0.0256

Beta 0.1367 0.0959

Stock return 0.0001 0.0001

Year 1997 �0.0431 �0.0302

Year 1998 �0.1411 �0.0989

Year 1999 �0.1251 �0.0877

Year 2000 �0.1627 �0.1141

Year 2001 �0.2149 �0.1507

The McDonald–Moffitt estimation procedure decomposes the coefficient estimates generated from a Tobit

specification into two parts. One part measures the effect of the independent variables on the probability of mutual

fund stock ownership being positive, and the other part measures the marginal effect conditional on ownership

being positive. The omitted year is 1996. Variable definitions are provided in Appendix A.




The results in this section indicate that the portfolio choices of foreign investors are

generally consistent with those of previous studies, suggesting that investor preference for

firm characteristics is insensitive to geographic location. In addition, I find that firm

attributes have a greater influence on a fund manager’s decision to hold a particular Latin

American stock than on the quantity of the stock held. Next, I evaluate whether

preferences differ between crisis and non-crisis periods.

4.1. Mutual fund preferences in response to crises relative to non-crisis periods

In this section, I investigate the proposition that the demand by mutual fund managers

for particular firm characteristics is different in response to financial crises, relative to

periods of tranquility. The purpose of these tests is to assess whether certain characteristics

become relatively more or less desirable to mutual fund managers subsequent to financial

turmoil originating in a different region. Since the objective of this study is to increase

understanding of contagion across regions, the years of interest are 1997 and 1998, the

period encompassing the Asian and Russian crises.

In Tables 5 and 6, I interact a crisis dummy variable representing the 1997–1998 period

with the characteristics associated with the hypothesis of interest. The coefficients on the

interacted terms represent the difference in the effect of the chosen variable on mutual fund

stock ownership between the crisis period of 1997–1998 and the non-crisis years of 1996

and 1999–2001.

Column 1 of Table 5 indicates that no significant shift toward large firms or S&P Latin

American index firms occurs in response to crises. However, the positive coefficient on the

ADR program dummy suggests that mutual fund managers move into stocks that trade on

U.S. exchanges during crises. This finding is consistent with a variety of interpretations,

including more information, greater liquidity, and potentially better corporate governance

of cross-listed firms relative to firms that are only locally listed. Interestingly, upon

investigating the two fund types separately, the preference toward ADRs during crises is

evident among the closed-end funds, with or without controlling for country fixed-effects.

Since redemption is not a concern for these funds, it is not clear that the shift is due

primarily to liquidity reasons.

I test the financial health hypothesis in column 2 using return on assets, the current

ratio, and the leverage ratio as proxy variables. None of the variables cause significant

portfolio shifts during crises. These findings are broadly consistent with Forbes (2004),

who indicates that firms with greater reliance on debt during the Asian and Russian crises

were not more adversely affected than other firms.

In column 3, I investigate the effect of trade competition on mutual fund stock demand.

No significant effect of Asian export competitors on ownership is apparent in response to

the crises. Unlike Asian export competitors, fund managers favored Russian export

competitors in the non-crisis period but reduced their holdings of these firms in response to

the events occurring in 1997 and 1998. However, the p-value of 0.11 is only marginally

significant at conventional levels.

I investigate the effect of governance as measured by the free float percentage in Brazil

and Chile in column 4. Family-dominated ownership structures are common in emerging

markets. As minority shareholders, mutual funds are subject to the risk of expropriation by

Table 5

Random effects Tobit model of Latin American mutual fund stock ownership in crisis versus non-crisis periods

Information asymmetry Financial health Trade channels Governance (Brazil and Chile only)

Tobit coeff. p-value Tobit coeff. p-value Tobit coeff. p-value Tobit coeff. p-value

Total assets 0.7710 0.000 0.7262 0.000 0.7191 0.000 0.8754 0.000

ADR program 0.5134 0.120 0.7130 0.023 0.7062 0.024 0.9889 0.010

S&P index 1.8840 0.000 2.1470 0.000 2.2003 0.000 3.2250 0.000

Current ratio �0.0170 0.663 �0.0270 0.572 �0.0172 0.662 �0.0040 0.931

Return on assets 0.0447 0.000 0.0500 0.000 0.0456 0.000 0.0420 0.001

Dividend yield �0.0370 0.007 �0.0374 0.007 �0.0377 0.007 �0.0313 0.034

Leverage �0.0216 0.005 �0.0223 0.010 �0.0224 0.004 �0.0168 0.068

Asian exports �0.4775 0.139 �0.4878 0.136 �0.4204 0.230 �0.7030 0.110

Russian exports 0.6331 0.187 0.6797 0.160 0.9674 0.063 1.1477 0.046

Share turnover 0.3586 0.000 0.3596 0.000 0.3634 0.000 0.0846 0.467

Beta 1.7476 0.000 1.7644 0.000 1.7050 0.000 1.7003 0.000

Stock return �0.0001 0.935 �0.0001 0.944 �0.0001 0.955 �0.0023 0.136

Free float 0.0228 0.007

Total assets*crisis �0.1454 0.257

ADR program*crisis 0.7092 0.080

S&P index*crisis 0.5420 0.320

Current ratio*crisis 0.0504 0.524

ROA*crisis �0.0179 0.375

Leverage*crisis �0.0001 0.991

Asian exp*crisis �0.1913 0.602

Russian exp*crisis �0.8852 0.110

Free float*crisis �0.0119 0.176

Crisis dummy 2.9836 0.250 0.2600 0.536 0.4579 0.040 0.5680 0.146

Constant �15.9658 0.000 �15.1597 0.000 �14.9935 0.000 �21.7643 0.000

Percentage of uncensored obs. 0.2844 0.2844 0.2844 0.2259

N 2064 2064 2064 1567

Log likelihood �1727.64 �1729.58 �1727.99 �1041.35

Crisis period is defined as the years 1997–1998. Coefficients on interaction terms represent the difference in the effect of the firm characteristic on mutual fund stock

ownership between crisis and non-crisis periods. Variable definitions are provided in Appendix A.

Dependent variable: Aggregate proportional ownership of shares held of a Latin American stock by mutual fund managers in a calendar year.

S.Elkin

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Review

6(2005)211–237

227

Table 6

Random effects Tobit model of Latin American mutual fund stock ownership in crisis versus non-crisis periods

(all variables)

All four countries Brazil and Chile only

Tobit coeff. p-value Tobit coeff. p-value

Total assets 0.7761 0.000 0.9900 0.000

ADR program 0.5249 0.116 0.6150 0.126

S&P index 1.9578 0.000 3.5312 0.000

Current ratio �0.0252 0.585 �0.0057 0.909

Return on assets 0.0521 0.000 0.0416 0.001

Dividend yield �0.0382 0.006 �0.0292 0.051

Leverage �0.0226 0.009 �0.0187 0.066

Asian exports �0.4409 0.212 �0.6215 0.193

Russian exports 0.9408 0.070 1.5150 0.021

Share turnover 0.3597 0.000 0.0839 0.473

Beta 1.7175 0.000 1.6084 0.000

Stock return 0.0000 0.988 �0.0017 0.268

Free float 0.0180 0.038

Total assets*crisis �0.1378 0.301 �0.2507 0.109

ADR program*crisis 0.6504 0.113 0.9728 0.046

S&P index*crisis 0.5783 0.292 �0.8902 0.219

Current ratio*crisis 0.0425 0.592 0.0132 0.883

ROA*crisis �0.0261 0.204 0.0223 0.468

Leverage*crisis �0.0004 0.974 0.0121 0.431

Asian exports*crisis �0.1606 0.679 0.0361 0.938

Russian exports*crisis �0.8449 0.131 �1.3181 0.037

Free float*crisis �0.0074 0.415

Crisis dummy 3.0635 0.258 5.2448 0.109

Constant �16.1048 0.000 �23.8514 0.000

Percentage of uncensored obs. 0.2844 0.2259

N 2064 1567

Log likelihood �1724.96 �1033.86

Crisis period is defined as the years 1997–1998. Coefficients on interaction terms represent the difference in the

effect of the firm characteristic on mutual fund stock ownership between crisis and non-crisis periods. Variable

definitions are provided in Appendix A.


a firm’s managers or controlling shareholders due to relatively weak legal protection by the

courts. The composition of the blockholders is unknown in this sample, but in Latin

America it is likely dominated by insiders. Thus, fund managers should be averse to firms

with high ownership concentration. Alternatively, in a crisis insiders can potentially

benefit minority shareholders due to their ability to deal with government officials.

According to Gaviria (2002), in the presence of corruption foreign direct investors will

choose to associate with local partners because of their knowledge of the bureaucratic

system. If firms with controlling shareholders are perceived to have close ties with political

officials, portfolio investors could view this favorably during crises if they associate high

ownership concentration with less risk of government expropriation.

The positive coefficient on free float indicates that mutual funds generally prefer less

concentrated ownership in non-crisis periods, but no significant shift occurs in response to

crises. However, fund type matters in this instance. Open-end mutual fund managers do


reduce their holdings of firms with highly dispersed ownership. Since the closed-end funds

do not exhibit this behavior, it is likely that liquidity concerns are driving this result for the

open-end funds, since firms with highly dispersed ownership should be the easiest to sell

during crises.

Since a number of the variables used to test the hypotheses are not independent, Table 6

presents the results of the regressions when all of the interacted variables are combined.

The first specification examines the four markets collectively. Although with both fund

types the p-value of 0.113 on the ADR Program*Crisis interaction term is not significant

at conventional levels, the preference toward cross-listed firms is still significant among

the closed-end funds when analyzed separately. This finding suggests that features besides

liquidity are important to fund managers.

The second specification includes the free float variable for Brazil and Chile. This model

does not indicate that fund managers reduce their holdings of firms with highly dispersed

ownership in response to crises. However, fund managers increase their holdings of ADRs

and reduce their holdings of Russian export competitors, similar to the findings in Table 5.

4.2. The Brazilian crisis

Tables 5 and 6 provide suggestive evidence that a financial crisis originating in a

different region causes investors to re-evaluate the desirability of certain firm attributes

within their own region. In the previous analyses, I define 1999 as a non-crisis year in

order to gain insight into how fund managers react to crises originating across regional

borders. One unanswered question, however, is how does a crisis originating within a

region affect the desirability of firm characteristics?

In an unreported specification, I conduct the tests shown in Table 6 by redefining the

crisis period as the year 1999 in order to examine the response of mutual fund managers to

the Brazilian devaluation in January of that year. Unlike the 1997–1998 crises, the

Brazilian crisis did not cause mutual funds to significantly alter their preferences for

particular firm attributes. This result is perhaps not surprising, due to expectations prior to

the devaluation of the real. On March 12, 1999, Michael Chriszt from the Federal Reserve

Bank of Atlanta stated: bBrazil’s troubles did not begin in January 1999. The precipitant

for Brazil’s current problems—and Latin America’s for that matter—effectively occurred

nearly two years ago.Q Chriszt indicates that investors began to sell their Brazilian holdingsin 1998 due to the structural similarities between Brazil and the other crisis countries. The

IMF approved a $41.5 billion loan in November 1998, but the move was insufficient to

prevent the subsequent devaluation in January 1999. These events suggest that unlike the

Asian crisis, the problems in Brazil were anticipated prior to the collapse of its currency.

4.3. Country effects

The results shown in the prior tables are pooled across countries and do not incorporate

country fixed-effects. However, the results are largely robust to their inclusion. One

disadvantage of pooling is that it imposes the restriction of common slopes across

countries. This assumption may be too restrictive because the countries differ in many

respects, such as macroeconomic policies and tax laws. For example, of the four Latin


markets, only Argentina did not impose a foreign withholding tax on dividends during the

sample period. If the aversion toward dividends is due to tax concerns, then controlling for

other characteristics, we should not observe this aversion in Argentina. In addition, the

results in Table 6 indicate that some differences in preferences become apparent when

Brazil and Chile are analyzed separately. Therefore, examining stock ownership within

specific countries provides a more flexible specification.

In Table 7 I investigate whether any significant shift in preferences occurs within

countries between crisis and non-crisis periods. The preference toward large firms is

present in all of the countries in the non-crisis period, but fund managers reduce their

holdings of large Mexican firms in response to crises. Interestingly, fund managers

investing in Argentina are still averse to dividends in the non-crisis period. This suggests

that growth may be a more important factor in stock choice than tax concerns.

Table 7

Random effects Tobit model of Latin American mutual fund stock ownership in crisis versus non-crisis periods by

country

Argentina Brazil Chile Mexico

Tobit coeff. p-value Tobit coeff. p-value Tobit coeff. p-value Tobit coeff. p-value

Total assets 0.4783 0.018 1.5541 0.000 0.3305 0.001 1.1433 0.001

ADR program 0.0241 0.955 0.3355 0.527 0.0425 0.880 0.4903 0.432

S&P index 1.3619 0.003 4.9554 0.000 0.8552 0.068 �1.0012 0.239

Current ratio 0.5229 0.001 1.0018 0.000 �0.2205 0.024 0.2268 0.292

Return on assets 0.0813 0.003 0.0537 0.002 0.0352 0.039 0.1050 0.053

Dividend yield �0.1090 0.000 �0.0621 0.020 �0.0244 0.016 0.0902 0.200

Leverage �0.0033 0.776 �0.0015 0.911 �0.0489 0.000 �0.0121 0.561

Asian exports �0.6003 0.095 �0.2510 0.673 �0.0115 0.971 �1.5220 0.038

Russian exports 0.1041 0.808 1.7218 0.012 1.4124 0.001 2.6041 0.076

Share turnover �0.3761 0.006 0.1306 0.368 0.3637 0.000 0.7997 0.001

Beta 2.7989 0.000 1.5639 0.016 1.8522 0.000 �1.4827 0.154

Stock return 0.0020 0.521 �0.0037 0.082 �0.0007 0.683 0.0028 0.518

Total assets*crisis �0.3557 0.192 �0.3494 0.150 0.0992 0.484 �2.1526 0.002

ADR program*crisis 1.7351 0.023 0.6679 0.367 1.0169 0.010 �3.2009 0.006

S&P index*crisis �0.9242 0.348 �1.2037 0.223 �0.9373 0.153 4.9068 0.001

Current ratio*crisis �0.6712 0.000 �0.3542 0.397 0.1965 0.063 �1.2711 0.005

ROA*crisis �0.0811 0.005 �0.0044 0.923 0.0191 0.577 �0.0895 0.290

Leverage*crisis �0.0678 0.000 �0.0153 0.494 0.0284 0.054 �0.0347 0.358

Asian exp*crisis 0.8412 0.122 �0.2411 0.714 0.1909 0.633 0.6466 0.615

Russian exp*crisis �0.2375 0.734 �2.0518 0.014 �0.0390 0.951 4.1413 0.133

Crisis dummy 9.8827 0.068 8.5681 0.106 �3.6401 0.220 50.9405 0.001

Constant �16.3320 0.000 �36.9384 0.000 �4.7060 0.029 �17.7343 0.023

Percentage of

uncensored obs.

0.3911 0.2127 0.2684 0.6410

N 179 1105 585 195

Log likelihood �158.51 �708.33 �359.16 �329.26

Crisis period is defined as the years 1997–1998. Coefficients on interaction terms represent the difference in the

effect of the firm characteristic on mutual fund stock ownership between crisis and non-crisis periods. Variable

definitions are provided in Appendix A.




The move into ADRs in response to the Asian and Russian crises is significant in

Argentina and Chile. According to Ffrench-Davis and Larrain (2002), in September 1998

the Chilean Central Bank removed a non-interest bearing reserve requirement affecting the

purchase of ADRs by foreigners. This could possibly explain the move into Chilean

ADRs. Argentina is the only country where fund managers reduce their holdings of firms

with high leverage in response to crises. The median leverage ratio of 27% in Argentina is

the highest of the four markets, so the behavior among mutual funds appears consistent

with a greater likelihood of financial distress among Argentinean firms. In contrast, fund

managers increase their holdings of leveraged firms in Chile. Zervos (2004) indicates that

Chilean firms can issue debt more cheaply abroad than locally, and the value of new

Chilean international bond issues was greater than local bond issues for the latter half of

the 1990s. Mutual fund preference toward leveraged firms in Chile could reflect the desire

for increased monitoring associated with international debt, consistent with the findings of

Harvey et al. (2004). The trade channel effect appears to be dominated by Brazil, since

funds reduce their holdings of Russian export competitors. The reduction of holdings

of large, cross-listed firms and firms with high current ratios in Mexico can reflect

liquidity needs, although closed-end funds also reduce their holdings of large firms

and firms with high current ratios.

Table 8 investigates whether fund managers shift into or out of specific countries in

response to the 1997–1998 crises relative to non-crisis years, controlling for firm

attributes. Model 1 compares the effect of country on stock ownership (the omitted country

is Brazil). Of the three remaining markets, Argentina and Mexico are preferred in the crisis

Table 8

Country effects on Latin American mutual fund stock ownership in crisis versus non-crisis periods

Base model Model (1): difference

from non-crisis

Model (2): difference

from Brazil

Tobit coeff. p-value Coeff. p-value Coeff. p-value

Argentina 0.9819 0.058

Chile 1.2414 0.002

Mexico 0.5368 0.236

Argentina*crisis 1.0782 0.042 0.9211 0.0507 2.0601 0.0006

Chile*crisis �0.1945 0.628 �0.3516 0.2645 1.0469 0.0290

Mexico*crisis 2.2571 0.000 2.1000 0.0000 2.7939 0.0000

Crisis dummy �0.1571 0.531

Constant �16.7902 0.000

Percentage of

uncensored obs.

0.2844

N 2064

Log likelihood �1708.93

Crisis period is defined as the years 1997–1998. Specification includes all firm characteristics listed in Appendix

A. In model (1) the coefficients on the interaction terms represent the difference in the effect of the country on

mutual fund stock ownership between crisis and non-crisis periods. In model (2) the coefficients on the interaction

terms represent the difference in the effect of the country on mutual fund stock ownership relative to Brazil during

the crisis period.




relative to the non-crisis period. Chile, however, was not preferred during the crisis period.

The strong trade linkage between Chile and Asia is a probable reason why fund managers

did not prefer to invest in Chile. Chriszt (1999) indicates that Chile’s trade exposure to

Asia is over 30%, causing Chile to experience the adverse effects of the Asian crisis more

than most of the other Latin American countries.

Model 2 compares the effect of each of the three countries to Brazil on mutual fund

stock ownership in response to the 1997–1998 crises. All three markets are preferred to

Brazil during the period. Given that the Brazilian crisis was largely anticipated by the

latter part of 1998, the preference away from Brazil is not surprising.

5. Conclusion

In this study, I examine in Latin America from 1996 to 2001 the relation between

mutual fund portfolio holdings of a firm’s stock and a firm’s characteristics. I investigate

whether mutual fund stock holdings are influenced by specific firm attributes associated

with incomplete information, financial health, competition, and governance. The results

are consistent with studies conducted on other markets, suggesting that mutual fund

preferences are relatively insensitive to geography. However, the main contribution of

this study is that particular firm attributes exhibit different effects on mutual fund

ownership between crisis and non-crisis periods.

While most of the variables in the study influence mutual fund ownership, I find that firm

characteristics associated with competitive exposure through trade and governance are

among the key factors that determine differences in stock ownership between crisis and non-

crisis periods. Fund managers reduce their holdings of firms that compete with Russian

exports and increase their holdings of ADRs in response to crises. Since the move into ADRs

is evident among closed-end funds that require less liquidity than open-end funds, this

preference suggests that additional factors such as information asymmetry and governance

concerns could influence the way fund managers respond to a crisis. Since the fundamental

literature on contagion investigates trade linkages, the role of asymmetric information and

corporate governance in portfolio choice in emerging markets merits further examination.

Although the funds in the sample represent a relatively small number of decision-makers,

their choices may represent the preferences of broader classes of institutional investors.

However, whether there are differences in the behavior around financial crises across classes

of institutions is ultimately an empirical question and an agenda for future work.

Acknowledgements

This study is based on my dissertation written while at the University of Oregon. I

thank my committee members Diane Del Guercio (Chair), Wayne Mikkelson, Woodrow

Johnson, and Larry Singell for their valuable comments and advice. I also wish to thank

Mark Stater, Paula Tkac, Emery Ventura, two anonymous referees, and seminar

participants at Loyola Marymount University and the 2004 Financial Management

Association Conference for their assistance and helpful suggestions.


Appendix A. Definition of firm characteristics and macroeconomic variables

The source for firm financial data is Economatica, except for ADR program and S&P

index. ADR program is obtained from the Bank of New York, and S&P index is obtained

from Standard & Poor’s. Macroeconomic data is obtained from International Financial

Statistics. Financial variables are measured in U.S. dollars. All data is taken at the year-end

prior to the date of the portfolio holdings.

Firm characteristics

Asymmetric information

Total assets Total assets at year-end (natural log used in regressions)

ADR Dummy=1 if firm has an ADR program. This variable could also serve as a

corporate governance variable due to the protection of minority shareholders

through higher quality disclosure and the potential signaling of growth opportunities.

S&P index Dummy=1 if stock is a component in the S&P Latin American 40 index.

The funds in the sample are benchmarked against various Latin American stock

indexes, including Standard and Poor’s and Morgan Stanley Capital International.

However, the major indexes use similar screening criteria to determine inclusion in

the index. The main criteria generally include investability, liquidity, and market

capitalization. The S&P Latin American 40 Index consists of firms specifically

located in the four Latin American markets of interest.

Financial health

Return on assets Net income/ total assets at year-end

Leverage (Short-term debt+ long-term debt) / assets at year-end

Trade channels

Asian exports Dummy=1 if three-digit NAIC code is an Asian export industry

Russian exports Dummy=1 if three-digit NAIC code is a Russian export industry

Governance

Free float Percentage of firms not owned by blockholders, defined as shareholders who own

5% or more of the shares (Brazil and Chile only).

Additional control variables

Current ratio Current assets / current liabilities at year-end

Dividend yield (Sum of dividends per share paid during the year) / (year-end share price)

Share turnover (Average dollar trading volume during the year) / (year-end market capitalization)

(natural log used in regressions)

Beta Covariance between monthly Latin American stock return and local market

index return divided by standard deviation of local market index return

measured over the prior 60 months

Stock return Percentage year-end price change between year t and year t�1 adjusted

for cash dividends

Macroeconomic variables

Interest rates Year-end deposit rate adjusted for local inflation

Foreign exchange rate Percentage year-end change in national currency per U.S. dollar between year t and

year t�1 adjusted for inflation (base year=1993)

GDP growth rate Percentage year-end change in the level of gross domestic product in local currency

between year t and year t�1 adjusted for local inflation

U.S. stock market return CRSP value-weighted market return between year t and year t�1 adjusted for

U.S. inflation


Appendix B. Major exports from the Asian and Russian crises zones from Forbes

(2004)

ASIAN CRISIS

11: Agriculture, forestry, fishing, and hunting

111: Crop production

1111: Oilseed and grain farming

1113: Fruit and tree nut farming

1119: Other crop farming

112: Animal production

1129: Other animal production

113: Forestry and logging

1132: Forest nurseries and gathering of forest products

114: Fishing, hunting and trapping

1141: Fishing

21: Mining

211: Oil and gas extraction


212: Mining (except oil and gas)

2122: Metal ore mining

31–33: Manufacturing

311: Food manufacturing

3112: Grain and oilseed milling

3113: Sugar and confectionary product manufacturing

3117: Seafood product preparation and packaging

3119: Other food manufacturing

313: Textile mills

3132: Fabric mills

314: Textile product mills

3149: Other textile product mills

315: Apparel manufacturing

3152: Cut and sew apparel manufacturing

316: Leather and allied product manufacturing

3161: Leather and hide tanning and finishing

3169: Other leather and allied product manufacturing

321: Wood product manufacturing

3212: Veneer, plywood, and engineered wood product manufacturing

325: Chemical manufacturing

3252: Resin, synthetic rubber, and artificial and synthetic fibers and filaments manufacturing

3259: Other chemical product and preparation manufacturing

326: Plastics and rubber products manufacturing

3261: Plastics product manufacturing

331: Primary metal manufacturing

3315: Foundries

332: Fabricated metal product manufacturing

3329: Other fabricated metal product manufacturing

334: Computer and electronic product manufacturing

3341: Computer and peripheral equipment manufacturing

3342: Communications equipment manufacturing

3343: Audio and video equipment manufacturing

3344: Semiconductor and other electronic component manufacturing

3345: Navigational, measuring, electromedical, and control instruments manufacturing

335: Electrical equipment, appliance, and component manufacturing

3353: Electrical equipment manufacturing

336: Transportation equipment manufacturing

3364: Aerospace product and parts manufacturing

3365: Railroad rolling stock manufacturing

3366: Ship and boat building

339: Miscellaneous manufacturing

3399: Other miscellaneous manufacturing

42: Wholesale trade

421: Wholesale trade, durable goods

4219: Miscellaneous durable goods wholesalers

RUSSIAN CRISIS

21: Mining



31–33: Manufacturing

331: Primary metal manufacturing

3312: Steel product manufacturing from purchased steel

3314: Nonferrous metal (except aluminum) production and processing

335: Electrical equipment, appliance, and component manufacturing

3353: Electrical equipment manufacturing

Exports are defined as four-digit SITC groups for which total exports from counties in the crisis regions are 25%

or more of total world exports. NAIC codes are used to identify industries.


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