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Can institutions and macroeconomic factors

predict stock returns in emerging markets?

Paresh Kumar Narayan a,1, Seema Narayan b,2, Kannan Sivananthan Thuraisamy a,⁎a School of Accounting, Economics and Finance, Deakin University, Melbourne, Australiab

School of Economics, Finance and Marketing, RMIT University, Melbourne, Australia

a r t i c l e i n f o a b s t r a c t

Article history:

Received 13 January 2014

Received in revised form 13 March 2014

Accepted 3 April 2014

Available online 13 April 2014

In this paper we test for predictability of excess stock returns for 18

emerging markets. Using a range of macroeconomic and institutional

factors, through a principal component analysis, we nd some

evidence of in-sample predictability for 15 countries. In-sample

predictability is corroborated by out-of-sample tests. Using a

mean-variance investor framework, we show that investors in most

of these emerging markets can make signicant prots from dynamic

trading strategies. Finally, we show that investors in most countries

where short-selling is prohibited could make signicant gains if

limited borrowing and short-selling were allowed.

© 2014 Elsevier B.V. All rights reserved.

Keywords:

PredictabilityReturns

Mean-variance investor

Institutions

1. Introduction

In this paper our focus is on the predictability of stock market returns in emerging markets. The literature

is voluminous. See, for example, the long list of inuential papers cited in Ferreira and Santa-Clara (2011) and

Westerlund and Narayan (2014a,b). Two directions are popular. One stream of studies considers whether

returns are predictable using macroeconomic indicators, while the other stream considers nancial ratio

predictors and, at best, the results are mixed. The main issue is that in-sample and out-of-sample testsproduce conicting results, which is problematic. The background to the existing tension is as follows.

Generally, in-sample tests of return predictability have found some encouraging results favouring

predictability, which is best summarised by Lettau and Ludvigson (2001, p. 842), “It is now widely accepted

that excess returns are predictable by variables such as dividend–price ratios, earnings–price ratios,

dividend–earnings ratios, and an assortment of other nancial indicators”.

Emerging Markets Review 19 (2014) 77–95

⁎ Corresponding author at: 70 Elgar Road, Burwood Highway, Centre for Financial Econometrics, School of Accounting, Economics

and Finance, Deakin University, Australia. Tel.: +61 3 9244 6913.

E-mail addresses: [email protected] (P.K. Narayan), [email protected] (S. Narayan),

[email protected] (K.S. Thuraisamy).1 Tel.: +61 3 924 46180.2 Tel.: +61 3 9925 5890.

http://dx.doi.org/10.1016/j.ememar.2014.04.005

1566-0141/© 2014 Elsevier B.V. All rights reserved.

Contents lists available at ScienceDirect

Emerging Markets Review

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / e m r

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This literature has attracted criticism though. On econometric grounds, there is not one main issue but

many: (a) the predictor variables have been highly persistent, and when corrected produce even weaker

evidence of predictability (see Ang and Bekaert, 2007; Stambaugh, 1999); (b) the predictive model's

errors are correlated with predictor innovations (see Lewellen, 2004); (c) data mining (see Foster et al.,

1997; Rapach and Wohar, 2006a); (d) parameter instability, so much so that the hypothesis of a constant

regression coef cient is almost always rejected (see Paye and Timmermann, 2006); (d) heteroskedasticity

(Westerlund and Narayan, 2012, 2013, 2014a); and cross-sectional dependence (Westerlund and

Narayan, 2014b). Persistency and correlation between returns and predictor innovations have the

tendency to bias the regression coef cients and the ensuing t-statistics on which the null hypothesis of no

predictability is based (see, inter alia, Lewellen, 2004; Stambaugh, 1999). Compared with in-sample tests

of return predictability, there are limited studies on out-of-sample tests. Of the limited studies (see Welch

and Goyal, 2008 and the references therein) that exist, the evidence is generally negative. Welch and

Goyal (2008) represent a comprehensive analysis of stock return predictability. They consider a wide

range of predictor variables and perform both in-sample and out-of-sample tests. They conclude that most

models do not reveal predictability of returns, which is true both in-sample and out-of-sample. Thus, they

claim that predictive regression models “would not have helped an investor with access only to available

information to protably time the market” (p. 1455). The lack of consensus on return predictability hasmotivated much of the recent literature, with Ferreira and Santa-Clara (2011, p. 515) claiming that: “The

predictability of stock market returns … remains an open question”.

While the focus of much of the stock return predictability literature has been on nancial ratio

predictors, it is unknown whether other non-nancial predictors also fail to predict returns in-sample and

out-of-sample and, thus, the question remains unanswered.3 It is well-known that emerging market risk

return characteristics are different compared to developed markets. Compared to developed markets, for

instance, emerging markets are highly volatile and provide attractive returns. Harvey (1995) argues that

emerging markets are segmented with high degree of return predictability.4 On the issue of predictability

of returns in emerging markets, Hjalmarsson (2010) nds that fundamentals, such as earning–price ratio

and dividend–price ratio, have reasonable ability to predict stock returns of emerging markets.

The goal of this paper is to examine whether macroeconomic factors and institutional factors predictexcess returns. We consider institutional risks associated with corruption, ethnic tension, internal

country-specic conicts, and law and order, and macroeconomic risks associated with up to 10

macroeconomic indicators. While some studies, such as Ferson and Harvey (1994, 1998), consider

macroeconomic predictors, none of the studies has considered the role of institutions in predicting

returns.5 Our analysis is conducted on time series data and considers 18 developing countries.

The contribution of our paper is three-fold. First, we focus on emerging markets where institutions play

an important role in the performance of stock markets. Therefore, for the 18 countries we choose, there is

a rich data set on institutions. This allows us to gain more insights on the role of institutions. We are also

able to examine whether or not investors can make use of the information content in institutions to make

non-negligible prots in developing countries. In addition, we also learn from the literature that

macroeconomic indicators are successful predictors of returns, although studies on this subject arelimited. Therefore, we also entertain macroeconomic predictors of returns. It follows that our use of both

institutional and macroeconomic variables as predictors of returns for emerging markets is unique and,

therefore, offers a fresh perspective on return predictability. Moreover, with limited emphasis on

developing country markets, very little is known about the role of short-selling. We contribute to this

literature as well. We nd that there are nine countries in our sample in which short-selling is prohibited.

3 The study that comes closest to our work is Mateus (2004), who examines stock return predictability both for individual

countries and for a cross-section of 13 EU accession countries. Related studies on return predictability have used other approaches to

testing for predictability; for an example, see Kinnunen (2013) and Eterovic and Eterovic (2013).4 Lack of integration of emerging markets with developed markets has also been conrmed in more recent studies (see Bekaert et

al., 2011; Cakici et al., 2013).5 There are, however, related studies that have generally considered the effects of institutions on specic rm issues. Demirguc-

Kunt and Maksimovic (1999) explain the role of institutions in debt composition for both developed and developing countries. The

impact of religion on market outcomes has been considered by Kumar et al. (2011). The role of governance at the rm level on stock

returns has been considered by Core et al. (2006). The relationship between societal norms and nancial sector development has

been analysed by Garretsen et al. (2004).

78 P.K. Narayan et al. / Emerging Markets Review 19 (2014) 77 –95


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We estimate prots allowing for limited borrowing and short-selling for those nine countries and show

how much better off investors in those countries could be.

Second, our estimation approach is different from the extant literature. We propose a conditional

capital asset pricing model (CAPM)-based predictive regression framework. This approach departs from

the cross-sectional and/or panel data approaches, thus ensuring that we can easily pick up any

heterogeneous effects of institutions and, for that matter, macroeconomic factors on returns in each of the

18 countries. This point is relevant as institutional strength and macroeconomic settings in different

developing countries are different, as several studies have documented, so it is not news. The data we use

reveal that institutional and macroeconomic conditions vary from one developing country to another; and,

as a result, these are likely to have a heterogeneous effect on returns at the country level. However, to-date

this has not been tested, thus, nothing is known about this subject.

Third, nothing is known about the economic signicance of return predictability when one uses institutions

and macroeconomic variables as predictors for emerging markets. We undertake a rigorous analysis of

protability using both passive and dynamic trading strategies. Moreover, drawing on a mean-variance

investor framework, we estimate investor utilities for each of the countries for which predictability exists.

We arrive at a number of new ndings which can be summarised as follows. First, we nd that excess

returns are predictable for 15 countries. Moreover, using popular out-of-sample tests, we are able to ndreasonable evidence that our proposed predictive regression model outperforms the historical average.

Second, we nd a heterogeneous response of returns to institutional and macroeconomic predictors. In 12

countries, institutions predict returns, while in nine countries macroeconomic indicators predict returns.

Third, based on evidence of predictability, we examine whether investors can gain from such

information. We consider a mean-variance investor, who has a portfolio of risky (stock market) assets and

risk-free (short-term interest rate) assets. The portfolio is decided purely on information that results from

the predictive regression model. The investor will invest little in stocks if the predicted excess return

(volatility) is low (high). In our setup, the investor is allowed to rebalance his portfolio once a month. The

coef cients of the model are re-estimated each month when new information becomes available. This

information is suf cient for the investor to revise her/his beliefs about expected returns and volatility. We

devise trading strategies that allow investors to engage in limited borrowing and short-selling and wheretransaction costs are also allowed. We call them dynamic trading strategies from which prots are

generated. These prots are compared to those obtained from passive trading strategies. We also estimate

the certainty equivalent return for an investor, following closely the work of Marquering and Verbeek

(2004). From this, we are able to address the question of how much would an uninformed investor, with a

given risk-aversion, be willing to pay to switch from a static to a given dynamic portfolio? We nd that

while investors prefer dynamic trading strategies the magnitude of prots in each of the 15 countries

differs. This reects the different roles played by institutions and macroeconomic factors and, as a result,

the different scopes for prots in those countries.

Finally, we identify nine countries in our sample where short-selling is prohibited. We show that if

limited borrowing and short-selling were allowed, investors in most of those countries would benet from

higher prots from dynamic trading strategies.The balance of the paper is organised as follows. In the next section, we discuss the theoretical

framework motivating the empirical analysis in this paper. In Section 3, we discuss the data and results. In

the nal section, we provide concluding remarks.

2. Motivating theoretical framework

2.1. Empirical framework

Following Ferson and Harvey (1998), we begin with the following unconditional CAPM:

r t þ1−E t r t þ1

¼ β t r M t þ1−E t r

M t þ1

n oþ μ t þ1

E t μ t þ1

¼ 0;

E t μ t þ1r M t þ1

¼ 0:

ð1Þ

79P.K. Narayan et al. / Emerging Markets Review 19 (2014) 77 –95


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Here, E t (.) denotes the conditional expectation given a common public information at time t . The return

for country i is represented by r t + 1 and the market portfolio return is represented by r t + 1M . The

corresponding conditional expected returns model has the following form:

E t r t þ1

¼ α t þ β t E t r

M

t þ1 :

ð2Þ

And, following Cochrane (1996), Lettau and Ludvigson (2001), and, more recently, Kang et al. (2011),

we represent the alphas and betas in the following manner:

α t ¼ α 0 þ α 1Μt ð3Þ

β t ¼ β 0 þ β 1Μt : ð4Þ

Here, Μt is a vector of conditioning variables. Given our aim of examining separately the effects of

institutions and macroeconomic factors on stock returns, in this paper we have two sets of distinct

conditioning variables. To separate these effects, we can further decompose the vector of conditioning

variables into a vector of institutional type variables and a vector of macroeconomic variables. We can,

therefore, re-write the conditional alphas and betas as follows:

α t ¼ α 0 þ α 1Nmt þ α 2N

I t ð5Þ

β t ¼ β 0 þ β 1Nmt þ β 2N

I t : ð6Þ

Here, Nt m and Nt

I are vectors of macroeconomic indicators and institutional indicators, respectively. As

we explain in the data section, we have on hand as many as 10 measures of macroeconomic performance

and four measures of institutional depth. Following Stock and Watson (2002) and Ludvigson and Ng(2007), we use a diffusion index approach and condense the macroeconomic and institutional risk

components into two summary measures, one for macroeconomic performance and one for institutional

performance, using principal component analysis for each country in our sample. Principal component

analysis is ideal in our case because it is able to suf ciently deal with problems of multi-collinearity and

over-parameterization. We, therefore, have a principal component based measure of macroeconomic

indicators, which we denote as PC M , and institutional indicators, which we denote as PC I . It follows that our

econometric model has the following form:

r t þ1 ¼ α 0 þ α 1PC M t þ α 2PC I t þ β 0r W t þ β 1PC M t r

W t þ β 2PC I t r

W t þ ε t þ1: ð7Þ

This multivariate predictive regression model is consistent with the models of return predictability

considered by Rapach and Wohar (2006b) and Marquering and Verbeek (2004) within a time series

framework.6 The rest of the variables in Eq. (7) are as follows: (a) the excess country return is denoted by r t ;

(b) world excess market return is denoted by r t W ; and coef cients β 1 and β 2 are associated with the interactive

effect of principal components through the world market return variable. Following Marquering and Verbeek

(2004), we use a GARCH-based predictive regression model, in which Eq. (7) is, typically, the mean equation

and the variance equation has the following form:

ht ¼ ν þ ν 1ε 2t −1 þ ν 2h

2t −1 ð8Þ

where ht is the conditional variance of returns, and ε t − 12

and ht − 12

represent short-term and long-termnews, respectively.

6 Using multivariate predictive regression models, Rapach and Wohar (2006b) examine predictability of returns based on

macroeconomic predictors, while Marquering and Verbeek (2004) use nancial ratio predictors.



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2.2. The relevance of institutional and macro factors for returns

2.2.1. The role of institutions

There is a large body of theoretical literature in corporate nance which shows that the choice of

securities or contracts between the rm and its investors depends, in large part, on the information

available to investors. The ability of investors to protect their investment depends on both nancial and

legal institutions.

Allen (2001) argued in favour of nancial institutions, drawing explicitly the link between nancial

institutions and asset pricing. He claimed that, “In standard asset pricing theory, investors are assumed to

invest directly in nancial markets. The role of nancial institutions is ignored” (p. 1165). His idea was rooted

in the belief that nancial institutions create an agency problemand have a crucial role in providing liquidity.7

In Qian and Strahan (2007), the focus was on understanding how laws and institutions shaped

nancial contracts with respect to bank loans. They nd that strong creditor rights improve loan

availability because in the presence of better legal protection lenders are more willing to provide credit on

favourable terms. This relationship is well entrenched within the theories of debt based on the transfer of

control rights upon default (see Aghion and Bolton, 1992). The implication is that if creditors possess

greater ability to force repayment or take control of the rm in the event of a default, they will offer crediton more favourable terms ex ante.

In a more extensive study involving thirty developed and developing countries, Demirguc-Kunt and

Maksimovic (1998) show that a well-developed legal system is imperative for rm growth. More

specically, they report that “Firms in countries that have active stock markets and high ratings for

compliance with legal norms are able to obtain external funds and grow faster” (p. 2134). La Porta et al.

(1997), using a sample of 49 countries, show that those with poorer investor protection, reected in weak

legal rules and poor quality of law enforcement, tend to be characterised by smaller and narrower capital

markets. For a similar analysis, see La Porta et al. (1998).8

In a relatively recent study, Papaionannou (2009) uses a panel data model consisting of 50 countries to

examine the determinants of international nancial ows from banks. One of his key determinants is

institutional quality. He nds that poorly performing institutions, such as weak protection of property rights,legal inef ciencies, and a high risk of expropriation, are key factors inhibiting growth of foreign bank capital.

While our motivation is the same as in the literature alluded to above, our approach and indeed our aim

with regard to the treatment of institutions and returns are different. We use time series data to capture the

effect of institutions on excess returns, while the extant literature examines the nancial performance of a

rm in relation to corporate governance, which is captured through the characteristics of the rm's board or

the corporate legislation and guidelines. A rm's board characteristics may include amongst other things the

board size, board diligence, and board independence (see, inter alia, Christensen et al., 2010; Lehn et al.,

2009). For studies that focus on corporate legislation and guidelines, see Bragaa-Alves and Shastri (2011),

Bebchuk et al. (2009), and Gompers et al. (2003). By comparison, we have four specic institutional factors.

Including all of them in a regression model will lead to multi-collinearity. Therefore, based on principal

component analysis, we create a single series, which effectively captures the institutional performance.

2.2.2. The role of macroeconomic indicators

There is a large body of literature that considers the relationship between returns and macroeconomic

factors. Compared with the returns–institutions literature alluded to above, much of the work on the returns

and macroeconomic factors is based on time series data. Essentially, two directions have been popular and, thus,

commonly explored on the returns–macroeconomic nexus. First, aggregate consumption growth directly enters

asset pricing models on which there is already a rich literature, owing, in large part, to the consumption-based

CAPM. The consumption effect was rst described by Lucas (1978) and Breeden (1979), who argued that the

risk premium on an asset is determined by the ability to insure it against uctuations in consumption. Later, the

consumption effect, through the ratio of stock market wealth and through risks inherent in cash ows in

inuencing asset prices, was documented by Bansal et al. (2005) and Da (2009), respectively. In much earlierwork, Campbell and Cochrane (1999) proposed a habit formation model, where both surplus consumption and

7 An informative discussion on agency problem can be found in Shleifer and Vishny (1997).8 An excellent discussion on investor protection and rm performance can be found in La Porta et al. (2000).



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per capita real consumption growth rate are factors that inuence the stochastic discount factor. Moreover, the

effect of labour income on returns has been considered by Santos and Veronesi (2006) and Danthine and

Donaldson (2002); the effects of money on asset returns have been shown by Barberis et al. (2001) and

Giovannini (1989); the effects of ination on stock returns through demand and supply disturbances have been

considered by Hess and Lee (1999); and the effects of government budgets on asset returns have been studied

by Boothe and Reid (1989).

A related strand of the literature considers the effects of macroeconomic news on the performance of

nancial markets. Jones et al. (1998) examine the reaction of daily Treasury bond prices to US

macroeconomic news announcements. Moreover, earlier studies (see, inter alia, Gennotte and Marsh,

1993) have considered the effects of variations in economic uncertainty on capital assets.

What do we learn from this literature and how does our approach differ in terms of modelling the

effects of macroeconomic factors? We learn that macroeconomic factors, such as ination, interest rates,

money, government budget balances, consumption, and income have been used to capture the effects of

macroeconomic risks. Clearly, the list of macroeconomic factors is long and we utilize up to 10

macroeconomic factors. Within a time series framework it is impossible, at least from an econometric

modelling point of view, to include all factors individually. Doing so would expose the model to the

problem of multi-collinearity, making the estimates biased. Our approach is to take a principal componentof the macroeconomic factors as a proxy for the measure of macroeconomic risk.

3. Data

The data used in this study include equity market returns for 18 emerging markets, the Morgan Stanley

Capital Index (MSCI) local currency world market index, and institutional and macroeconomic variables. 9

The data on returns are sourced from the Bloomberg Financial Database, and the institutional and

macroeconomic risk components are sourced from the International Country Risk Guide (ICRG) database.

Column 2 of Table 1 identies the sample period covered with respect to each of the 18 emerging markets.

The sample period is dictated by data availability in Bloomberg and ICRG databases. It is important to note

here that the ICRG database has as many as 70 developing countries. However, we excluded thosecountries in which there was little variation in institutional factors. This is needed because if institutional

risk components are not changing over long periods of time it is pointless to model them. Our ltering

approach was to exclude all countries which had continuously three years of institutional data that did not

change. In this way, we end up with a sample of 18 countries. It should also be noted that other studies

have also used institutional risk components from the ICRG database and, like us, they note that at least for

some of those countries in the ICRG database there is high “within” country variation in many of the

institutional measures; see, for instance, Papaionannou (2009) and Glaeser et al. (2004).

We employ four institutional risk components; namely, corruption, ethnic tension, external conict, and

law and order. The corruption component of the institutional risk factor captures the level of risk arising from

corrupt behaviour mainly associated with the length of time the government has been in power continuously.

The next component under this group is ethnic tension, which measures the degree of tolerance andcompromise between various ethnic groups in a country. The third component, internal conict, captures any

institutional risk arising in the country due to civil war, civil disorder, and terrorism. The last component, law

and order, measures the strength and impartiality of the legal system, as well as the popular observance of

law and order in a given country. From these variables, as we explain later, we capture the effects of

institutions through principal component analysis for each of the 18 countries in our sample.

In addition, we employ 10 macroeconomic risk components; namely, budget balance as a percentage of

GDP, current account as a percentage of exports of goods and services (XGS), current account as a

percentage of GDP, debt service as a percentage of XGS, exchange rate stability, foreign debt, GDP growth,

ination, net international liquidity, and per capita GDP. From these variables, using principal component

analysis, we extract a single measure of macroeconomic risk for each of the 18 countries in our sample. We

briey outline how individual risk components are built using different scales to capture a country'smacroeconomic conditions: Budget balance as a percentage of GDP increases the rating when a given

9 Eighteen developing countries included in our sample are: Argentina, Bangladesh, Brazil, Chile, China, Egypt, Kenya, Lebanon,

Malaysia, Mexico, Oman, Pakistan, Peru, Russia, South Africa, Taiwan, Tunisia, and Venezuela.



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country has positive percentage while points are deducted as the country moves to a decit budgetary

condition. A country having a budget balance of 4% or over will be allocated 10 points while a negative 30%

of budget balance will earn zero points indicating higher level of risk. Similarly, current account as a

percentage of XGS earns 15 points for a surplus of 10% or more, while a negative 30% (imbalance) earnszero points, reecting a deteriorating condition with respect to imports and exports. Similar attribution is

made with respect to current account as a percentage of GDP. Ination rate of less than 2% in a country

earns a maximum of 10 points while ination rate of over 130% earns 0 points, indicating a higher level

of risk. A GDP growth of 4% or above in real terms earns 10 points while a negative growth rate (less than

−30%) earns zero points.

Debt related measures, such as foreign debt as a percentage of GDP, earn 10 points if the percentage lies

between 0 and 4.9%. On the other hand, indebtedness rising to 200% of GDP earns zero points. Similar scaling

is used for a county's capacity to service its debt as a percentage of exports of goods and services: a percentage

of 4.9% and below earns 10 points while a higher relative percentage (85% or above), reects constraints in

debt service capacity and, therefore, earns zero points. The exchange rate stability component is a risk

measure that captures the appreciation/depreciation of a country's currency against the US dollar over acalendar year. If the appreciation changes or the depreciation change is kept at the lower end (up to 9.9% for

appreciation change and−0.1% to−4.9 for depreciation change) then full 10 points are allocated, favouring

the country's currency situation with respect to this risk component. Net international liquidity is the

estimated of cial reserves for a given year measured in months. A cover of, at least 15 months earns all ve

points while a cover of two weeks or less earns 0 points, signalling deteriorating liquidity conditions.

4. Results

4.1. Preliminary analysis of the data

We begin the results with an analysis of our data set. Table 1 reports the summary statistics on excessreturns. Panel A contains the basic statistics while panels B and C contain the percentage contribution of

each of the principal components to the standardized variance of the institutional factors and macroeconomic

factors, respectively. We begin by examining statistics on excess returns. Therst thing we notice is that there

is wide variation in mean excess returns. It is lowest for Lebanon (−0.09%) and highest for Brazil (6.96%).

Table 1

Descriptive statistics. In this table we report selected descriptive statistics, namely mean, standard deviation, skewness, and kurtosis,

for excess returns. This is followed by a principal component analysis of institutions (PC I ) and macroeconomic factors (PC M ). For PC I we report the percentage contribution of the rst three principal components to the standardized variance, while for PC M we report

the percentage contributions of the rst ve principal components to the standardized variance.

Sample period r PC I [% of variance] PC M [% of variance]

Mean Std. dev. Skewness Kurtosis PC1 PC2 PC3 PC1 PC2 PC3 PC4 PC5

Argentina 1988.1–2011.6 3.808 2 0.311 2.887 18.127 48.7 8 1.6 1 00 43.3 6 6.7 8 0.1 8 6.4 9 1.5

Bangladesh 1988.11–2011.6 0.589 9.226 0.864 10.736 70.7 84.5 94.2 66.8 79.3 87.8 92.1 95.4

Brazil 1988.4–2011.6 6.957 19.651 0.618 6.092 66.2 85.4 96.8 40.1 61.5 76.1 84 90.1

Chile 1989.1–2011.6 1.347 6.267 −0.457 7.146 55.3 82.5 95.4 44.3 60 71.6 80.2 86.9

China 2002.1–2011.6 0.647 9.430 −0.562 4.123 61.5 86.7 96.7 39 62.1 73.3 82.6 91.4

Egypt 1993.3–2011.6 1.011 8.490 −0.303 5.040 47.4 75.5 94.3 40.5 63.5 77.4 86 91.2

Kenya 1991.2–2011.6 0.307 7.078 0.964 9.093 55.4 80.8 94.5 58.4 76.9 86.2 91.3 94.2

Lebanon 1996.1–2011.6 −0.091 7.232 0.904 8.350 44.2 73.1 88.6 39.1 64 78.7 87.1 92.9

Malaysia 1987.6–2011.6 0.308 7.253 −0.147 5.766 54.3 89.3 95.9 42.5 62.9 75.3 83.1 88.4

Mexico 1994.1–2011.6 0.973 7.670 −1.027 5.872 46.2 83.2 95.6 41 59.5 73.2 81.8 88.9

Oman 1992.1–

2011.6 0.420 5.843 −

0.507 6.772 63.2 98.9 100 50.4 69.7 80.4 86.4 91.8Pakistan 1990.5–2011.6 0.930 9.709 −0.580 6.151 53.1 75.8 91.3 51.9 73.3 82.9 89.3 92.9

Peru 1990.1–2011.6 3.975 15.368 2.587 16.854 69.8 92.4 96.6 64 78 87.6 92.9 95.6

Russia 1997.9–2011.6 1.469 13.420 −0.856 6.333 60.6 85.6 98.1 49.7 75.7 85.1 89.7 92.9

S. Africa 1995.6–2011.6 0.679 6.296 −1.031 7.237 75.2 89.4 95.2 29.9 52.9 64.9 74.3 81.7

Taiwan 1987.12–2011.6 0.134 10.126 −0.354 5.269 49.9 79.9 96.3 44.4 65.3 78.5 88.3 93.3

Tunisia 1999.4–2011.6 0.740 3.988 −0.030 4.980 44.6 81.1 96.4 43.8 65.3 76.1 83.4 88.5

Venezuela 1993.3–2011.6 1.814 1 0.406 0.045 7.103 73.7 9 6.7 1 00 43.6 6 1.6 7 2.3 8 1.5 8 6.9



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Mean excess returns are close to 4% in Peru and Argentina and in excess of 1% in the case of Chile, Egypt,

Russia, and Venezuela. Countries with the highest mean returns also experience higher volatility as measured

by the standard deviation. Skewness is generally found to be negative while evidence based on the kurtosis

statistic suggests a leptokurtic distribution of excess returns for all 18 countries.

Next, we briey summarise the main features of the principal component analysis. With regard to the

institutional indices, where we have four specic variables, we nd that the rst principal component on

average across all 18 countries explains around 58% of the standardized variance. For about 44% of the

countries in our sample, the rst principal component explains over 60% of the standardized variance. By

comparison, the second and third principal components explain only around 27% and 11% of the

standardized variance, respectively. The cumulative percentage contributions of each of the three

principal components are plotted in Fig. 1. We, thus, conclude that the rst principal component explains

the variations in the institutional indices better than any other linear combination of variables.

For the macroeconomic variables, we have 10 indicators. We nd that, on average, the rst principal

component explains around 46% of the standardized variance, while the second and third principal

components explain, on average, around 20% and 12% of the standardized variance, respectively. The rest of

the principal components, by comparison, explain less than 13% of the variance. Again, as for institutional

factors, the rst principal component explains the variance better than any other linear combinations.The predictable regression should ideally have all variables in stationary form to avoid any bias in the

predictive regression model (see, Westerlund and Narayan, 2012; Narayan et al., 2014). While the

stationary form of excess returns and excess world market returns are rather obvious, the same cannot be

claimed for the principal components of institutions and macroeconomic conditions. We conrm the

integrational property of all the variables for each of the 18 countries by conducting an augmented Dickey

and Fuller (ADF, 1979) test. The ADF test examines the null hypothesis that there is a unit root against the

alternative that the variable is stationary. Our approach in running the ADF test is to utilize a maximum of

eight lags to control for any potential serial correlation in the variable. We then use the Schwarz

Information Criteria (SIC) to obtain the optimal lag length. The results are not tabulated here but are

available upon request. We only summarise the key nding here. First, we nd that the excess returns for

each country, the excess world market returns and the two interactive variables appear to be clearlystationary. The unit root null hypothesis is rejected comfortably at the 1% level for all 18 countries. Second,

except for Oman and Kenya, the principal component of institutional indicators are non-stationary, as the

unit root null hypothesis cannot be rejected at the 5% level. When we consider the principal component

of macroeconomic indicators, we nd that the unit root null hypothesis cannot be rejected at the 5% level

for any of the 18 countries, suggesting that for all countries the macroeconomic indicator is unit root

non-stationary. The implication here is that except for Oman and Kenya when using the institutional

0

10

20

30

40

50

60

70

80

90

100

PC1 PC2 PC3

Fig. 1. Percentage contribution of principal components to standardized variance of institutions. This gure plots the cumulative

contributions of the rst three principal components of institutional factors to the standardized variance. The bulk of the variations

comes from the rst principal component.



9/19

variable as a predictor, we take the rst difference of the principal components in testing for return

predictability. This approach ensures that our test for return predictability does not suffer from the

inherent bias imposed by persistency of predictors—an issue which has occupied much interest in nancial

economics (see, for instance, Westerlund and Narayan, 2014a).

4.2. Evidence on predictability

4.2.1. In-sample evidence

The results based on the predictive regression model (7) are reported in Table 2. The results are

organised row-wise by country. The main features of our results can be summarised as follows. First, we

nd that PC M predicts excess returns for seven countries (Bangladesh, Brazil, China, Argentina, Kenya,

Oman and Venezuela).

Table 2

Results for return predictability. In this table, we report the results on return predictability based on a GARCH model. The mean

equation takes the following form: r t + 1 = α 0 + α 1PC Mt + α 2PC It + β 0r t W + β 1PC Mt r t

W + β 2PC It r t W + ε t . The principal components

of macroeconomic indicators and institutional factors are denoted by PC Mt and PC It , respectively. The excess country returns is

denoted by r t .; world excess market return is denoted by r t W ; and coef cients β 1 and β 2 are associated with the interactive effect of

principal components through the world market return. Finally, the variance equation has the following form: ht = ν + ν 1ε t −

12 + ν 2ht − 1

2 , where ht is the conditional variance of returns, and ε t − 12 and ht − 1

2 represent short-term and long-term news,

respectively. Figures in parenthesis are the p-value and the asterisks indicate statistical signi cance at 1% (***), 5% (**) and 10% (*).

α 0 PC M PC I r W PC M r

W PC I r W R2

Argentina 3.2663** −1.3369* −1.5109* 0.569* −0.0362 −0.084 0.1029

(0.012) (0.052) (0.825) (0.712)

Bangladesh 0.1672 0.5316*** −0.2315 0.0701 0.04 −0.0675 0.0072

(0.706) (0.007) (0.485) (0.459) (0.360) (0.379)

Brazil 2.9995*** −1.449** −0.8188 −0.1684 −0.2817 −0.5322** −0.0067

(0.001) (0.050) (0.404) (0.453) (0.122) (0.038)

Chile 1.8839*** −0.088 −0.9664** 0.2388** −0.2169* 0.3042** 0.0603

(0.000) (0.839) (0.021) (0.046) (0.100) (0.012)

China −14.1267*** 5.8768*** 1.0752 −2.7123* 1.0497* 0.4752** 0.0289

(0.007) (0.002) (0.336) (0.052) (0.058) (0.050)

Egypt −0.8865 −0.6498 2.7331* 0.5751 0.002 −0.3061 0.0746

(0.642) (0.384) (0.015) (0.244) (0.992) (0.238)

Kenya 0.0302 −0.5474*** 0.7196* 0.1028 0.0396 0.0191 0.0103

(0.943) (0.000) (0.069) (0.273) (0.283) (0.856)

Lebanon 3.1995 −0.8385 −0.6149* −0.8333 0.0851

(0.505) (0.482) (0.571) (0.056) (0.067) (0.294)

Malaysia 0.6813** −0.1039 −0.1468 0.1046 0.0394 −0.135* 0.0349

(0.034) (0.554) (0.585) (0.245) (0.468) (0.089)Mexico 1.7432** −0.4044 −0.3495 −0.1086 0.1139 −0.0713 0.0146

(0.025) (0.333) (0.420) (0.706) (0.372) (0.573)

Oman 0.7113*** 0.2083* 0.4375** 0.1782*** 0.0526 0.0252 0.0133

(0.003) (0.085) (0.021) (0.006) (0.351) (0.555)

Pakistan 1.0151* 0.2034 −0.6803* 0.1362 0.0426 0.0987 0.0242

(0.071) (0.322) (0.095) (0.371) (0.516) (0.363)

Peru 3.1343*** −0.0002 −1.0423 0.2943 0.2066 −0.3192* 0.0363

(0.001) (1.000) (0.213) (0.228) (0.179) (0.099)

Russia 2.3924* −0.4269 −0.6744 0.4638** 0.0185 −0.1412 0.0538

(0.010) (0.625) (0.584) (0.032) (0.936) (0.615)

S. Africa 0.2502 0.3716 0.3072 0.5903** −0.1431 −0.294* 0.0111

(0.835) (0.388) (0.647) (0.031) (0.270) (0.077)

Taiwan 0.6534 −0.1477 0.3438 −0.0453 0.1107 0.023 −0.0014

(0.240) (0.620) (0.474) (0.756) (0.227) (0.849)Tunisia 1.7402 −1.1091 1.7051** 0.4971 −0.2096 0.1737 0.0866

(0.321) (0.151) (0.032) (0.123) (0.216) (0.338)

Venezuela 2.3412*** −0.8144* −0.4566 0.4455* −0.1585 −0.0098 0.0345

(0.006) (0.075) (0.511) (0.070) (0.189) (0.941)



10/19

Second, we nd that when the principal component of macro indicators is modelled interactively

through the world excess market return, it is able to predict the excess returns of Chile, China and

Lebanon. It follows that, either directly or indirectly (through the excess world market return), macro

indicators predict excess returns for as many as nine countries.

Third, we nd that PC I predicts excess returns for seven countries (Argentina, Chile, Egypt, Kenya,

Oman, Pakistan and Tunisia). Fourth, when we use the principal components of institutions interacted

with the world market excess returns, we nd that they predict excess returns for Brazil, Chile, China,

Malaysia, Peru and South Africa. In all, then, either directly or indirectly, institutions predict excess returns

for 12 countries. Lastly, we discover that for 15 countries, there is some evidence that either macroeconomic

or institutional factors predict excess returns.

The key implication is that one can use our proposed predictability model to forecast returns for these

15 countries. The next question is that if investors in these countries do indeed use our model to forecast

returns, how relevant are the forecasts? This, effectively, is an issue of the out-of-sample forecasting

performance of our model. To demonstrate the forecasting performance of our predictive regression

model, we follow Welch and Goyal (2008) and Rapach et al. (2010). We utilize 50% of the sample to

produce in-sample estimates and use those estimates to forecast returns for the rest of the 50% of the

sample for each of the 15 countries.

4.2.2. Out-of-sample evidence

To measure the out-of-sample performance of our forecasting model, we use commonly used metrics,

namely, the out-of-sample R2, which we denote as OOS_R2, the Theil U statistic, and the mean squared

error test proposed by McCracken (2007), which we denote as MSE − F . The OOS_R2 follows the proposal

of Welch and Goyal (2008) and Ferreira and Santa-Clara (2011):

OOS R2¼ 1−

MSE modelMSE mean

ð9Þ

where MSE model is the mean square error of the out-of-sample predictions from our proposed model, while

MSE mean is the mean squared error of the historical sample mean. When OOS_R2N 0, our proposed

predictive regression model predicts returns better than the historical mean, and vice versa. By

comparison, the Theil U statistic is dened as the ratio of the square roots of the mean-squared

forecasting errors of the predictive regression model relative to the historical average. If the Theil U b 1,

Table 3

Out-of-sample forecasting performance. In this table, we report the Theil U statistic, the OOS_R2, and the MSE-F statistic (denoted

with stars) as a metric for comparing the out-of-sample performance of our multivariate predictive regression model with that of

historical average. *** denotes the statistical signicance at the 1% level at which null hypothesis that the MSE from the predictive

regression model is equal to the historical average. The test statistics are considered only for the 15 countries for which some

evidence of return predictability was found.

Theil U OOS_R2

Argentina 0.7095*** 0.0308

Bangladesh 0.9403*** −0.0334

Brazil 0.8709*** −0.0674

Chile 0.8957*** −0.0398

China 0.6597*** −0.1094

Egypt 0.7086*** −0.1138

Kenya 0.7847*** −0.0382

Lebanon 0.9281*** −0.0222

Malaysia 0.8331*** −0.0255

Oman 0.7383*** −0.0648

Pakistan 0.8639*** 0.0069Peru 0.7446*** 0.0433

S. Africa 0.7363*** −0.3408

Tunisia 0.8700*** −0.0405

Venezuela 0.8600 0.0120



11/19

our proposed predictive regression model outperforms the historical average. Finally, the McCracken

(2007) test examines the equality of the MSE of the unconditional (historical mean) and the conditional

(predictive model) forecasts. Following the proposal in Welch and Goyal (2008), we generate bootstrap

p-values to test the null of equal MSE.

The results are reported in Table 3. The ratio of the Theil U statistic is reported in column 2. We nd

that all ratios are less than 1. This suggests that the proposed predictive regression model outperforms the

historical average for all countries. The McCracken MSE-F statistic corroborates this evidence; the F

statistics are all signicant at the 1% level for all countries (except Venezuela), favouring the predictive

regression model. Finally, when we consider the OOS_R2, the results are mixed. Only for four countries is

the OOS_R2 greater than zero, suggesting that only for those four countries does the predictive regression

model outperforms the historical average. On the whole, then, it is fair to claim the out-of-sample

superiority of our predictive regression model over the historical average. A nal note here is that, where

there is no predictability, such as in the case of Mexico, Taiwan, and Russia, the need for forecasts does not

arise. This also means that in evaluating trading strategies we only concentrate on those 15 countries for

which predictability has been found.

4.3. Pro ts from passive and dynamic trading strategies

The results from passive trading strategies are reported in Table 4. Essentially, we consider three types

of investments: 100% in the market; 50% in the market and 50% in a risk-free asset; and 100% in a risk-free

asset. It is true that these investment types are arbitrary and that one could come up with a type that best

addresses the research question. Our approach here is motivated by Marquering and Verbeek (2004), who

consider the same three types of investments. For each strategy, we estimate returns and their statistical

signicance, and we compute the standard deviation of returns. We nd that by investing 100% in a risky

asset (market), investors in ve out of 15 countries end up making statistically signicant prots. A 50:50

strategy reduces returns and risk, as expected. The most protable countries turn out to be Malaysia and

Oman, where a 50:50 strategy leads to pro

ts of over 1% per month.In Table 5, we report prots from dynamic trading strategies. Specically, we consider a dynamic

trading strategy (DTS1) where the portfolio weight is restricted to be between 0 and 1 and another where

the portfolio weight is restricted to be between 0 and 1.5 (DTS2)—the rationale for this strategy and the

Table 4

Prots from passive trading strategies. In this table, we report prots from passive trading strategies for each of the 15 countries for

which we discovered evidence for return predictability. Prots are generated for three passive trading strategies: (a) one in which

investors investor 100% in the market (column 2); (b) one in which investors invest 50% in the market and 50% in a risk-free treasury

bill rate (column 3); and (c) one in which investors invest 0% in the market and 100% in a risk-free treasury bill rate (column 4). For

each strategy, prots together with its standard deviation (SD) are reported. *, **, and *** denote statistical signicance at the 10%,

5%, and 1% levels, respectively.

100% 50% 0%

Mean SD Mean SD Mean SD

Argentina −5.798*** 5.826 −2.797*** 2.898 0.205*** 0.167

Bangladesh −2.583*** 1.117 −1.194*** 0.589 0.195*** 0.163

Brazil −13.446*** 4.652 −6.622*** 2.329 0.203*** 0.167

Chile −1.623*** 2.036 −0.715*** 1.006 0.193*** 0.168

China −0.941 8.938 −0.408** 4.502 0.126*** 0.159

Egypt −3.177*** 5.143 −1.508*** 2.564 0.161*** 0.146

Kenya −2.506 0.683 −1.170 0.356 0.166*** 0.141

Lebanon −0.647** 2.050 −0.238* 1.049 0.172*** 0.157

Malaysia 3.615*** 0.632 1.910*** 0.338 0.206*** 0.168

Oman 3.309*** 3.007 1.735*** 1.518 0.160*** 0.142

Pakistan 0.499** 1.914 0.336*** 0.968 0.173*** 0.144Peru −3.517*** 1.163 −1.669*** 0.612 0.178*** 0.149

S. Africa −6.232*** 2.651 −3.032*** 1.292 0.168*** 0.155

Tunisia 0.486*** 0.759 0.133*** 0.397 0.180*** 0.173

Venezuela 1.474*** 2.476 0.818*** 1.187 0.162*** 0.149



12/19

construction of portfolio weights are explained in the next section. Panel A reports results for the case of

no transaction cost, while results in Panel B are based on a transaction cost of 1%. The main results can be

summarised as follows. First, investors in all countries earn statistically signicant prots regardless of the

restrictions on the portfolio weight.

Second, the most protable countries turn out to be Oman, Malaysia, China and Venezuela, where

returns are between 1.4 and 3.7% per month. In the other countries studied, investors earn less than 1% per

month. The weakest returns are experienced by investors in Bangladesh, Brazil, South Africa, Peru, and

Kenya, where returns are in the range of 0.16 to 0.18% per month. Third, with DTS2, the opportunities for

prots increase, and the largest gains are experienced by investors in Oman, Malaysia and China. On the

other hand, no gains in returns are made by investors in Bangladesh, Brazil, Kenya, Peru, and South Africa.

A nal observation we make here relates to the Sharpe ratio. The Sharpe ratio, dened as the mean

excess return on the portfolio relative to the standard deviation of the portfolio return, is reported in the

last column of Table 5. We notice that, generally, Sharpe ratios from DTS1 are higher than from DTS2; the

exception is Chile. This reects the attractiveness of a dynamic trading strategy without borrowing (DTS2).

Similarly, when we compare Sharpe ratios of passive trading strategies (not reported here) with those

from dynamic trading strategies, we nd that dynamic trading strategies are relatively more attractive for

investors.

Table 5

Prots from dynamic trading strategies. In this table, we report prots from two dynamic trading strategies: one in which portfolio

weights are restricted to be between 0 and 1 (no short selling), and one in which limited short-selling and borrowing are allowed.

The results are divided into two panels. Results contained in Panel A are based on zero transaction cost, while results in Panel B take

into account a 1% transaction cost. The nal columns also contain the results on Sharpe ratio, dened as the mean excess return on a

portfolio relative to the standard deviation of the portfolio return.

Optimal μ (0–1) Optimal μ (0–1.5) Sharpe ratio

Mean SD Mean SD μ (0–1) μ (0–1.5)

Panel A: No transaction cost (optimal μ )Argentina 0.641*** 1.405 0.695*** 1.720 0.310 0.285

Bangladesh 0.174*** 0.147 0.174*** 0.147 −0.145 −0.145

Brazil 0.178*** 0.149 0.178*** 0.149 −0.167 −0.167

Chile 0.273*** 0.416 0.306*** 0.573 0.193 0.198

China 2.123*** 4.100 2.630*** 5.769 0.487 0.434

Egypt 0.680*** 1.507 0.838*** 2.108 0.344 0.321

Kenya 0.161*** 0.145 0.161*** 0.145 −0.032 −0.032

Lebanon 0.435*** 0.888 0.510*** 1.253 0.296 0.270

Malaysia 3.536*** 0.638 4.998*** 1.181 5.217 4.058

Oman 3.667*** 2.292 5.235*** 3.444 1.53 1.479

Pakistan 0.732*** 0.816 0.797*** 1.008 0.685 0.619

Peru 0.161*** 0.141 0.161*** 0.141 −0.124 −0.124

S. Africa 0.179*** 0.161 0.179*** 0.161 0.071 0.071

Tunisia 0.535*** 0.638 0.658*** 0.932 0.557 0.513Venezuela 1.405*** 1.764 1.900*** 2.684 0.705 0.648

Panel B: With 1% transaction cost (optimal μ )

Argentina 1.278** 4.492 1.549** 5.717 0.239 0.235

Bangladesh 0.171*** 0.141 0.171*** 0.141 −0.173 −0.173

Brazil 0.178*** 0.149 0.178*** 0.149 −0.167 −0.167

Chile 0.226*** 0.304 0.237*** 0.370 0.108 0.118

China 2.367*** 4.506 3.087*** 6.562 0.497 0.451

Egypt 0.179*** 0.155 0.179*** 0.155 0.114 0.114

Kenya 0.161*** 0.145 0.161*** 0.145 −0.032 −0.032

Lebanon 0.321*** 0.675 0.367** 0.981 0.222 0.199

Malaysia 3.204*** 0.566 4.480*** 1.109 5.299 3.854

Oman 2.967*** 2.139 4.221*** 3.189 1.312 1.274

Pakistan 0.857*** 0.912 0.941*** 1.126 0.751 0.682Peru 0.161*** 0.141 0.161*** 0.141 −0.124 −0.124

S. Africa 0.179*** 0.161 0.179*** 0.161 0.071 0.071

Tunisia 0.253*** 0.337 0.265*** 0.402 0.218 0.211

Venezuela 1.125*** 1.652 1.512*** 2.498 0.583 0.540



13/19

4.4. Evidence from a utility-based measure

In this section, following Rapach et al. (2010), Campbell and Thompson (2008), and Marquering and

Verbeek (2004), and Narayan et al. (2013)we consider a relatively robust approach in testing the

economic signicance of our results. More specically, we undertake a utility-based measure of economic

signicance, where realized utility gains are computed for a mean-variance investor on a real-time basis. A

mean-variance investor has the following utility function:

E t r t þ1

−

1

2γ Var t r t þ1

: ð10Þ

Such that, given a portfolio of π t + 1 for the risky asset, the utility simply becomes:

r f ;t þ1 þ π t þ1E t r m;t þ1

n o−

1

2γπ

2t þ1 þ Var t r m;t þ1

n o ð11Þ

where r t + 1 is the excess return on the portfolio or security, r f ,t + 1 is the risk-free rate of return, r m,t + 1 is

the market excess return, Var t is the rolling variance of the risky asset, γ is the risk aversion factor, and

π t + 1 is the investor's portfolio weight in period t + 1, computed as follows:

π t þ1 ¼

E t r t þ1

γ Var t r m;t þ1

n o : ð12Þ

We constrain the portfolio weight on stocks to lie between 0% and 150% each month. We also generate

results for portfolio weights of −50% to 150% but do not report the results here, as Campbell and

Thompson argue that the most realistic weight is 0–1.5. In particular, they state that such a weight

restriction prevents “the investor from shorting stocks or taking more than 50% leverage …” (p. 1525). In

other words, if forecasts for excess returns are good, investors may borrow up to 50% of their investment

value and invest in risky assets.10

The average utility level, ex post, becomes:

Û ¼ 1

T

XT −1t ¼0

r t þ1−1

2γπ

2t þ1Var t

: ð13Þ

The average utility is computed for the dynamic portfolio as well as for the passive portfolio. This

allows us to compare the utilities from both types of portfolios. We are, as a result, able to obtain the

maximum fee an investor will be willing to pay for holding the dynamic portfolio over the passive one.The results on average utilities for the three passive and the two dynamic trading strategies for three

different levels of risk aversion (γ = 3, 6, 12) are reported in Table 6. The results are organised as follows. In

Panel A, we report utilities obtained from the passive strategies, while in Panel B we report utilities obtained

from the two dynamic strategies. A number of importantndings emerge from this analysis. First, if one only

considers utilities from passive strategies, investors in 10 countries end up with negative utilities. This implies

that investors would prefer not to invest in those markets. However, if investors use a dynamic trading

strategy, all 10 countries which had negative utilities end up with positive utilities. Therefore, the difference in

utilities obtained from dynamic trading strategies and passive trading strategies is positive, thus always

favouring dynamic strategies. The investor, therefore, would be willing to pay more per month to hold a

dynamic trading strategy rather than a passive trading strategy. As we saw earlier, doing so leads to greater

prots for investors. Under a range of different scenarios, we showed that investors make relatively moreprots from dynamic trading strategies compared to passive trading strategies.

10 Detailed results on prots and utilities for a portfolio with restricted weights of between −0.5 and 1.5 are available upon request

from the corresponding author.



14/19

Second, we notice that when investors were allowed to borrow 50% of the value of their investment

value (DTS2), average utilities are slightly higher than those obtained from passive trading strategies.

Finally, we observe that results in favour of dynamic trading strategies are robust to different levels of risk.

In evaluating trading strategies, it is natural to allow for transaction costs, which is something we have

ignored so far. To see whether average utilities still favour dynamic strategies over passive strategies, we now

re-estimate all economic gains for the 15 countries and report the results in Table 7. Following the vast

literature that uses transaction costs in estimating prots, we consider transaction costs of 0.5% (medium)

and 1% (high). For simplicity and to conserve space, here we only compare the dynamic portfolios with only

one passive strategy—one that invests 50% in a risky asset and 50% in a risk-free asset. The results from

alternative passive strategies are broadly similar, and detailed results are available from the corresponding

author upon request. The choice of the 50:50 strategy is ideal here as it generally gives higher average utilities

compared to a strategy that invests 100% in the market, as we saw from the results reported in Table 6.

The following features of the economic gains in the presence of transaction costs stand out and deserve

a mention. First, we notice that for seven of the 15 countries the economic gains from the dynamic strategy

Table 6Average realized utilities for passive strategies without transaction cost. In this table, we report the average realized utilities

associated with passive (Panel A) and dynamic trading strategies (Panel B). We use three different risk aversion parameters and

consider two passive and two dynamic trading strategies. The results are based on a case where there is zero transaction cost.

Panel A: Passive trading strategies

γ = 3 γ = 6 γ = 12

100% market 50% market 100% market 50% market 100% market 50% market

Argentina −5.799 −2.774 −5.878 −2.779 −6.037 −2.547

Bangladesh −2.71 −1.265 −2.722 −1.266 −2.747 −1.077

Brazil −14.092 −6.925 −14.166 −6.925 −14.312 −6.638

Chile −1.808 −0.804 −1.846 −0.806 −1.923 −0.622

China −

3.567 −

1.202 −

4.829 −

1.281 −

7.355 −

1.047Egypt −3.943 −1.729 −4.307 −1.752 −5.035 −1.514

Kenya −2.554 −1.194 −2.559 −1.195 −2.570 −1.015

Lebanon −0.646 −0.207 −0.704 −0.210 −0.820 −0.018

Malaysia 3.561 1.874 3.556 1.874 3.547 2.030

Oman 3.305 1.789 3.179 1.781 2.927 1.932

Pakistan 0.624 0.413 0.578 0.410 0.486 0.570

Peru −3.739 −1.786 −3.746 −1.786 −3.761 −1.598

S. Africa −6.693 −3.233 −6.747 −3.236 −6.854 −3.002

Tunisia 0.51 0.335 0.502 0.335 0.487 0.484

Venezuela 1.198 0.699 1.162 0.697 1.091 0.861

Panel B: Dynamic trading strategies

γ = 3 γ = 6 γ = 12Optimal μ

(0–1)

Optimal μ

(0–1.5)

Optimal μ

(0–1)

Optimal μ

(0–1.5)

Optimal μ

(0–1)

Optimal μ

(0–1.5)

Argentina 0.632 0.685 0.624 0.674 0.607 0.653

Bangladesh 0.174 0.174 0.173 0.173 0.173 0.173

Brazil 0.178 0.178 0.178 0.178 0.178 0.178

Chile 0.269 0.299 0.266 0.292 0.258 0.278

China 1.872 2.279 1.621 1.929 1.118 1.228

Egypt 0.618 0.734 0.555 0.629 0.430 0.421

Kenya 0.161 0.161 0.161 0.161 0.161 0.161

Lebanon 0.422 0.485 0.409 0.459 0.383 0.408

Malaysia 3.532 4.990 3.528 4.983 3.520 4.968

Oman 3.584 5.091 3.501 4.929 3.334 4.605

Pakistan 0.724 0.787 0.717 0.777 0.702 0.758Peru 0.161 0.161 0.161 0.161 0.161 0.161

S. Africa 0.179 0.179 0.179 0.179 0.179 0.179

Tunisia 0.534 0.655 0.532 0.652 0.528 0.645

Venezuela 1.381 1.850 1.357 1.801 1.310 1.701



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(DTS2) are greater than the passive trading strategy, implying investor preference for holding dynamic

strategies rather than static strategies. To demonstrate this point, let us consider one specic case. For

Malaysia, for instance, we notice that with a 1% transaction cost, investors are willing to pay a fee for all

trading strategies, but investors prefer paying higher fees to maintain a dynamic trading strategy rather

than a passive one. Interestingly, at a higher transaction cost (1%)—a cost that most studies consider—the

passive strategy has a negative utility for 10 countries but dynamic strategy produces positive utilities.

This, again, reects the preference of investors for dynamic trading strategies.

For almost all countries, when we allow for a limited amount of borrowing in the presence of

transaction cost under a dynamic trading strategy, investor utility is maximised. The exception is Tunisia,

for which investor utility is higher under a passive trading strategy.

4.5. How bene cial is short-selling?

How benecial is short-selling? To address this question, in Table 7 (last three columns) we provide

country-specic information on short-selling. In particular, we identify; (a) whether short-selling is

practised, (b) start date of short-selling, and (c) imposition of any bans on short-selling. We believe thisanalysis is important for the following reason. In Table 8, we report results from two dynamic trading

strategies that allow for limited borrowing and short-selling. We consider two cases. In the rst case, we

allow for borrowing and short-selling of 5% and in the second case we allow for borrowing and

short-selling of 10%. From both strategies, we estimate prots and utility for each country in the presence

of transaction cost. What is clear from our results is that short-selling leads to relatively higher pro ts

compared to a strategy without short-selling. However, we are also aware that not all countries in our

sample allow for short-selling. From Table 7, we identify nine countries (Bangladesh, China, Egypt, Kenya,

Lebanon, Oman, Peru, Tunisia and Venezuela) where short-selling activities are prohibited. This means we

can now answer the question: How much more prot and utility gain could investors make in these

markets if short-selling (of either 5% or 10%) was indeed allowed?

We summarise the pro

ts and utility from dynamic trading strategies with and without short-selling inTable 9. Column 2 contains prots and utility from a strategy of no short-selling, while the corresponding

results from dynamic strategies with 5% and 10% short-selling are reported in columns 3 and 4,

Table 7

Average realized utilities with transaction cost. In this table, we report the average realized utilities associated with a 50:50 passive

trading strategy (column 2) and the two dynamic trading strategies (column 3). The utilities are based on a risk aversion parameter

of six. The results are based on a case where there are transaction costs of 0.5% and 1%. It should be noted that naked short-selling has

been prohibited in Brazil, Chile, Malaysia, and South Africa.

Passive trading

strategies

Dynamic trading strategies Is short-selling allowed?

50% market Optimal μ (0–1) Optimal μ

(0–1.5)

Start date Imposition of

ban on short-selling

0.50% 1% 0.50% 1% 0.50% 1%

Argentina 1.275 −2.77 −2.938 −3.351 1.231 1.175 Yes 1999 Pre-1999

Bangladesh 0.212 −1.265 0.175 0.171 0.175 0.171 No Never allowed Always

Brazil 1.186 −6.921 −4.882 −5.179 0.178 0.178 Yes Since inception Never imposed

Chile 0.624 −0.802 −1.028 −1.393 0.278 0.227 Yes 1999 Pre-1999

China 21.195 −1.139 −6.216 −8.786 2.018 1.878 No Never allowed Always

Egypt 6.113 −1.711 −0.31 −0.418 0.194 0.178 No Never allowed Always

Kenya 0.089 −1.194 0.093 0.071 0.161 0.161 No Never allowed Always

Lebanon 0.967 −0.204 −8.907 −10.87 0.393 0.34 No Never allowed Always

Malaysia 0.083 1.874 3.609 3.095 5.312 4.471 Yes Pre 1997 Sept 97–Dec 06

Oman 2.136 1.795 −3.511 −2.714 4.57 3.921 No Never allowed Always

Pakistan 0.782 0.415 1.151 0.825 1.309 0.919 Yes Since inception Never imposedPeru 0.144 −1.785 0.079 0.07 0.161 0.161 No Never allowed Always

S. Africa 0.876 −3.23 −3.089 −3.547 0.179 0.179 Yes Since inception Never imposed

Tunisia 0.133 0.335 0.363 0.234 0.443 0.263 No Never allowed Always

Venezuela 0.57 0.701 0.684 0.628 1.689 1.398 No Never allowed Always



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respectively. The main ndings are as follows. In eight of the nine countries (except for China), the

introduction of limited short-selling of 5% relative to the dynamic trading strategy without short-selling

will lead to a rise in prots by between 8.4% in the case of Venezuela to 118% in the case of Egypt.

Bangladesh records a rise in prots of around 111%, followed by Kenya (104%), Oman (72.6%), Lebanon

(31.4%), Tunisia (15.8%), and Peru (14.7%). A similar trend in the rise in prots is noticed when a higher

level (10%) of borrowing and short-selling is allowed. When we consider utilities, we notice two things.

First, without short-selling, China, Egypt, Lebanon and Oman had negative utilities, reecting lack of

investor preference for dynamic trading strategies. However, with short-selling all utilities become

positive. Second, utilities increase with an increase in the magnitude of short-selling.

On the whole, our analysis suggests that allowing for limited short-selling and borrowing is likely to be

benecial to investors. We have considered our results based on limited short-selling scenarios which can

be extended to consider any specic amount of short-selling that countries would like to implement.

5. Concluding remarks

In this paper we contribute to the literature on the predictability of stock returns. Our analysisproduces four new ndings. First, we consider evidence of predictability for as many as 18 emerging

markets using time series data. This approach ensures that we are in a position to discern country

heterogeneity with respect to institutional and macroeconomic predictors. Our main nding here is that

for 12 countries institutions predict returns, while for nine countries macroeconomic variables predict

returns. The heterogeneity of countries is clear, as, for some countries institutions predict returns, for some

countries macroeconomic variables predict returns, while for others none of the factors are able to predict

returns. In all, we nd that for 15 out of the 18 developing countries studied, there is some evidence that

either institutions, macroeconomic variables, or a combination of those variables, predict excess returns.

Second, unlike the return predictability literature that considers nancial ratios as predictors, we nd that

in-sample evidence of predictability is corroborated by out-of-sample tests. It follows that while an investor

may not be prepared to accept return forecasts based on

nancial ratios, no such tension exists if the investoris willing to draw on information contained in combinations of macroeconomic and institutional factors.

Third, while the literature has considered the determinants of returns, suggesting that returns are

predictable using macroeconomic variables, there is no evidence of economic signicance of such predictability.

We undertake an extensive analysis of economic signicance through a mean-variance investor framework.

Table 8

Prots and utility from limited short selling (−0.05 to 1.05 and −0.1 to 1.1). In this table, we report prots and utility from dynamic

trading strategies where we conditionally allow for limited borrowing and short-selling. We consider two restrictions; one where

we allow for 5% borrowing and short-selling and the other where we allow for 10% short-selling and borrowing. We report the mean

prots, standard deviation, and utility associated with each of these two strategies, accounting for a 1% transaction cost. ** and ***

denote statistical signicance at the 5% and 1% levels, respectively.

Limited short-selling: −0.05 to 1.05 Limited short-selling: −0.10 to 1.1

Mean SD Utility Mean SD Utility

Argentina 2.057** 4.6576 1.8193 1.668** 4.569 1.4501

Bangladesh 0.551*** 0.1772 2.0091 0.361*** 0.1494 1.1096

Brazil 1.673*** 0.5446 1.672 0.926*** 0.319 0.9254

Chile 0.499*** 0.3655 0.491 0.362*** 0.3211 0.3554

China 1.673*** 0.5446 1.672 0.926*** 0.319 0.9254

Egypt 0.601*** 0.3074 0.5978 0.390*** 0.1991 0.3883

Kenya 0.494*** 0.1691 0.4941 0.328*** 0.1515 0.3276

Lebanon 0.517*** 0.7047 0.4978 0.422*** 0.6842 0.4044

Malaysia 3.469*** 0.6647 3.4627 3.338*** 0.6142 3.3324

Oman 3.273*** 2.2938 3.0918 3.121*** 2.2149 2.9535

Pakistan 0.920*** 0.9294 0.9024 0.889*** 0.9189 0.8721Peru 0.635*** 0.1593 0.6352 0.398*** 0.1454 0.398

S/Africa 0.842*** 0.3088 0.8412 0.511*** 0.2269 0.5105

Tunisia 0.332*** 0.3348 0.3301 0.293*** 0.3346 0.291

Venezuela 1.316*** 1.7725 1.2492 1.220*** 1.7107 1.1591



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More specically, we consider both passive and dynamic trading strategies (including one where limited

borrowing (50%) is allowed). We show that dynamic trading strategies offer higher returns for investors.

Moreover, we discover that allowing for limited borrowing allows investors to make relatively higher prots.

We also estimate investor utility, which merely signies the amount that an investor will be willing to pay to

maintain a particular trading strategy. We nd similar results when we allow limited short-selling of 5% and

10%. For the 15 countries for which returns are predictable, we compare investor utility from passive strategies

with that from dynamic ones. Our main conclusion is that investors have a predilection for dynamic trading

strategies.

Finally, we identify that there are nine countries in our sample where short-selling is prohibited. We

show that if limited borrowing and short-selling are allowed in these countries, then investors in most of

these countries will benet from higher prots and utilities from dynamic trading strategies.

In terms of directions for future research; given what we have discovered, it would seem ideal to

examine if institutional and macroeconomic factors that we consider for emerging country markets would

hold also for other developing country markets. It may well be the case that there are certain types of

developing markets where institutional factors matter more than macroeconomic factors for predicting

returns and vice versa. Understanding exactly which factors predict stock returns will guide potential

trading strategies for investors in these markets. A second avenue for extending the work we have

presented here is by modelling emerging market stock return predictability using a panel data predictive

regression model proposed recently by Westerlund and Narayan (2014b). There are several appealing

features of this panel data predictive regression model, which has implications for trading strategies as

Westerlund and Narayan (2014b) show. We recommend these issues for future research.

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Table 9

Impact of short-selling on prots and investor utility. This table reports the gains in prots and utilities for those nine countries

where no short-selling is allowed. The utility and prots, reported in column 2, are based on a strategy of no short-selling, while

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