Country-Specific ETFs: An Efficient Approach to Global Asset Allocation

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1 EDHEC RISK AND ASSET MANAGEMENT RESEARCH CENTRE promenade des Anglais Nice Cedex 3 Tel.: +33 (0) research@edhec-risk.com Web: Country-Specific ETFs: An Efficient Approach to Global Asset Allocation May 2006 Joëlle Miffre Professor of Finance, EDHEC Business School

2 Abstract The article shows that country-specific exchange-traded funds (ETFs) enhance global asset allocation strategies. Because ETFs can be sold short even on a downtick, global strategies that diversify risk across country-specific ETFs generate efficiency gains that cannot be achieved by simply investing in a global index open- or closed-end fund. Besides, the benefits of international diversification can be achieved with country-specific ETFs at low cost, with low tracking error and in a tax-efficient way. For all these reasons, country-specific ETFs may be considered serious competitors to traditional country open and closed-end funds. Keywords: Country-Specific ETFs, Global Asset Allocation, Short-Selling EDHEC is one of the top five business schools in France. Its reputation is built on the high quality of its faculty (110 professors and researchers from France and abroad) and the privileged relationship with professionals that the school has cultivated since its establishment in EDHEC Business School has decided to draw on its extensive knowledge of the professional environment and has therefore focused its research on themes that satisfy the needs of professionals. 2 EDHEC pursues an active research policy in the field of finance. The EDHEC Risk and Asset Management Research Centre carries out numerous research programmes in the areas of asset allocation and risk management in both the traditional and alternative investment universes. Copyright 2009 EDHEC

3 1. Introduction Exchange-traded funds (ETFs) are shares that closely track the performance of an index. They offer, in one trade, the benefits of diversification and index tracking 1 at low cost. The first ETF, the SPDR, was launched in 1993 and was designed to passively mimic the S&P500 index. Since then the variety of indices that ETFs track has kept expanding, ranging from equity and bond indices worldwide to style and sector portfolios. As an investment vehicle, ETFs have become so popular that Fuhr (2001) estimated that most days, two or three ETFs are on the list of the top five most actively traded stocks in the AMEX. ETF assets under management grew at a 132% average annual rate from 1995 to 2000, with a combined value in the US that stands at $240 billion today (Investment Company Institute, 2005). ETFs are effective tools for investment, risk and tax management. They are frequently used for passive investment, short-term speculative trading, tactical asset allocation, hedging and arbitrage. Alternatively, asset managers use them to carry out market-neutral strategies, to time the market, implement sector and style rotation strategies or diversify a domestic portfolio across countries and global industries. In a way similar to futures, they can also be used to simulate full investment of small mandates into stocks. At times, the most liquid ETFs are even substitutes for the cash necessary to meet redemption. In the end, the many advantages of ETFs lead Gastineau (2001) to the conclusion that in the year ahead we would expect nearly all equity index funds to have an ETF share class. Developments in this field have already occurred. For example, Vanguard, an established leader in the passive fund management industry, announced in January 2004 that it was expanding its own class of ETFs to include US style and sector MSCI benchmarks. This article focuses on 16 ishares MSCI country funds (Barclays Global Investors country-specific ETFs). Like country open- and closed-end index funds, country-specific ishares increase meanvariance efficiency. However, unlike country index funds, country-specific ETFs can be sold short even on a downtick. This paper shows that the ability to short ETFs even on a downtick enables US investors to increase the mean-variance efficiency of their portfolio; namely, achieve higher average returns for a given level of risk or, alternatively, lower risk for a given average return. These efficiency gains are not available to investors in global open- or closed-end index funds, as the latter cannot be sold short. Relative to their country open- and closed-end fund siblings, country-specific ishares offer the additional advantages of being tax-efficient, of having low tracking error and low costs. All these reasons explain why they have become an integral part of an asset manager s global asset allocation strategy and serious competitors to the traditional open and closed-end country funds. The remainder of the paper is organized as follows. The paper first presents the advantages of country-specific ishares relative to conventional country funds. I then introduce the methodology, the dataset and the empirical results, highlighting the efficient gains that can only be achieved with country-specific ETFs. 2. Country-Specific ETFs Versus Open and Closed-End Country Funds Like conventional country index funds, ishares MSCI country funds present in one trade the benefits of international diversification and index tracking at a low cost. However country-specific ETFs offer investors four additional benefits that cannot be obtained by merely trading open or closedend country funds. First, unlike open-end funds, which trade only at 4:00pm NAV (net asset value), ishares trade on a continuous basis and are exempt from the uptick rule. They can therefore be sold short at any time during the trading day. These two features, continuous trading and short-selling, make them particularly attractive in down markets, as investors then very much value the possibility 1 - Academic research suggests that it is nearly impossible to consistently beat the market (Jensen, 1968; Malkiel, 1995; Gruber, 1996; Carhart, 1997; Frino and Gallagher, 2001). 3

4 of either liquidating their ishares position or taking a short position with immediate effect. Like country-specific ETFs, closed-end country funds trade continuously throughout the day. However they can be sold short only on an uptick and this, as we will see, leads to efficiency losses relative to ETFs. Second, ishares MSCI country funds (and ETFs in general) are typically acknowledged as a taxefficient alternative to investing in mutual funds. They are created through a process called creation in kind, whereby exchange specialists, market makers and arbitrageurs buy ETF shares by depositing with the fund the index constituents. Similarly, ETFs can be liquidated through a process called redemption in kind, whereby large market participants receive the portfolio of underlying shares, rather than cash, in exchange for ETF shares. The ETF typically redeems in kind the stocks with high unrealized capital gains and keeps the stocks with low unrealized capital gains. Because of this, it is unlikely that the ETF will have to sell high capital gain stocks in the future and, thus it is unlikely that its investors will be forced to pay capital gain taxes (Gastineau (2001, 2002); Poterba and Shoven (2002)). In contrast, to raise cash for redemptions, traditional mutual funds sell shares that may have appreciated relative to their initial cost. As a result, redeeming clients realize taxable capital gains. Third, country-specific ETFs are also well-known for their low tracking error relative to conventional closed-end funds. Because they can create new shares or redeem existing ones at NAV on a daily basis, arbitrageurs ensure close tracking and transparency with regards to the fund constituents. Harper, Madura, and Schnusenberg (2003) confirm this by showing that country-specific ETFs have returns that are not statistically different from the underlying MSCI returns and offer higher Sharpe ratios than competing closed-end funds. Chang and Swales (2003) also show that because of creation in kind, redemption in kind, and their open-end structure, country-specific ETFs trade at lower discounts to NAV than country closed-end funds. This ultimately reduces the risk that investors sell their ETFs shares at too low a price relative to NAV. On the other hand, it increases the probability that investors buy ETFs shares at a more expensive price than otherwise similar closed-end funds. Finally, country-specific ETFs are cheaper than closed-end country funds. Chang and Swales (2003) report that the average expense ratio on ishares MSCI country funds is only 0.87%, while the average expense ratio on country closed-end funds is 1.59%. None of the closed-end funds studied had expense ratios cheaper than their ETF counterparts. Looking at the universe of ETFs and not just at ishares, we find that expense ratios can be as low as 0.09% a year for the most liquid ones. This compares favourably to the fact that a typical index fund charges up to 0.5% a year, while the expense ratio of an active fund can be as high as 2% (Poterba and Shoven (2002), Kostovetsky (2003)). To summarize, ishares MSCI country funds, unlike their open and closed-end country funds siblings, can be sold short even on a downtick, are tax-efficient, have low tracking error and low cost. This paper also argues that ETFs offer efficiency gains in as much as they increase average returns for a given level of risk or, alternatively, decrease portfolio risk for a given return. These incremental risk-adjusted return are not available to investors in global closed and open-end index funds, as the latter cannot be sold short. This does not mean that we should all use them as our preferred investment vehicle. Small investors, who cannot afford to create country-specific ETFs in kind and are used to investing small amounts periodically, typically find the brokers commissions and bid and ask spreads of ETFs too expensive. They would rather invest in no-load country open-end index funds. Because they carry no commissions, mutual funds are cheaper and more flexible to small investors, who very much value the possibility of defining a transaction size that perfectly matches their resources. 4

5 3. Methodology The methodology aims at constructing portfolios with long positions in US stocks and long or short positions in ishares MSCI country funds. Following Sharpe (1964), I calculate the portfolio s weights ω that maximize the portfolio s expected Sharpe ratio. The optimisation problem is subject to two constraints. The first constraint ensures that the sum of the weights equals one (It is needed to guarantee that the mandate is fully invested). The second constraint forbids or allows short-selling up to a certain level K. In mathematical terms, the optimization problem reads as follows max ω ( ) R f σ P E R P = max ω ω' R R f ω' V ω N subject to ω i =1 i =1 and ω i K for i =1,...,N. K is positive for ishares MSCI country funds if short-selling of ETFs is not allowed or is negative for ishares MSCI country funds if short-selling of ETFs is allowed. ω is a N-vector of portfolio weights, R is a N-vector of mean returns, σ P is the portfolio standard deviation and V is a covariance matrix. The resulting portfolio is called the optimal risky portfolio. The optimization problem is first solved for a portfolio containing the S&P500 and long positions in ishares. Then, asset managers can combine risk-free borrowing and lending with the optimal risky portfolio and define the asset allocation that matches their client s preferences for risk and expected return. Unlike traditional asset classes, ETFs can be sold short. For this reason, I relax, in a second step, the assumption of no ETF short-sale and define the characteristics of the optimal portfolio when ω ETF 0.05, ω ETF 0.20 and ω ETF 1. The choice of a 20% short interest was dictated by the fact that short ETF positions typically range from 10 to 40% of the ETF market capitalisation (Gastineau (2002)). To test the sensitivity of the results to the short-sale restriction, this paper looks at the case of i) an investor who limits his short ETF interest to 5% and ii) a hedge fund manager who considers up to 100% short positions in any ETF. 4. Data The data include end-of-month total returns on the S&P500 index over the period April 1996 to January The purpose of the analysis is to test how sensitive the efficient frontier of an USbased investment is to the inclusion of 16 ishares MSCI country funds. These include four ishares from Asian-Pacific countries (Australia, Hong Kong, Malaysia and Singapore), ten ishares from European countries (Austria, Belgium, France, Germany, Italy, the Netherlands, Spain, Sweden, Switzerland and the UK) and two ishares from North-American countries (Canada and Mexico). The US one-month Treasury-bill is used as a proxy for the risk-free asset. Since means, standard deviations and correlations change over time, I calculate summary statistics for the assets over increasing samples ranging from April 1996-April 1999 to the whole dataset. It is indeed commonly acknowledged that in periods of recession, mean returns fall, while standard deviations and correlations rise (Solnik, Boucrelle, and Le Fur (1996), Longin and Solnik (2001), Dahlquist and Harvey (2001)), inducing changes in optimal asset allocations. Table 1 presents summary statistics (annualized mean, standard deviation and expected Sharpe ratio) for the different asset classes, where the statistics are averaged over these increasing samples. Figure 1 plots the annualized standard deviations against the annualized average returns. It is easy to see from this graph and table 1 that the S&P500 performs better than any ishare on a risk-adjusted basis. The South-East Asian ETFs are among the poorest performers. This is expected since the sample covers the Asian and Russian crises of 1997 and

6 Table 1 - Summary statistics The means, standard deviations and correlations are annualized and measured over increasing samples ranging from April 1996-April 1999 to April 1996-January 2004 in increments of one observation at a time. The table presents averages of the mean returns, averages of the standard deviations and averages of the correlations over these increasing samples. Figure 1 - Risk and average return: averages across increasing samples Table 1 also presents of the return correlations between the S&P500 index and the countryspecific ishares. As with the means and standard-deviations above, the correlations are measured over increasing samples and the average correlations are reported in table 1. As expected, the correlations are higher with the North American and UK ETFs (average correlation of 0.75) and lower with the Pacific Asian ETFs (average correlation of 0.58). These correlations are less than one, suggesting, as in Solnik (1974), that the partial segmentation of capital markets leaves ample room for risk reduction through international diversification. The high standard deviations in the correlations between the S&P500 and some ishares (in particular, those of Sweden, Austria, Germany and Italy) confirm that correlations did change over time and therefore that optimal asset allocations are also time-dependent. 6

7 Altogether table 1 and figure 1 illustrate that, while country-specific ishares show poor performance as stand alone investments, their low correlations with the S&P500 make them good candidates for inclusion in a well diversified portfolio. 5. Results Optimal Asset Allocations Since the parameters of the optimization problem (means, standard-deviations and correlations) change over time, I calculate the optimal asset allocation over increasing periods, ranging from April 1996-April 1999 to April 1996-January 2004, in increment of one observation at a time. The optimal asset allocations are calculated at the end of each month from April 1999 until January Figures 2 to 5 present the average asset allocations of the optimal portfolios and the standard deviations of the optimal asset allocations (in parenthesis). Figure 2 looks at the case where the asset manager only takes long ETF positions, while figures 3 to 5 allow for up to 5, 20 and 100% short ETF interests respectively. Figure 2 - Average optimal asset allocation with long ETF positions (standard deviations in parentheses) Figure 3 - Average optimal asset allocation with up to 5% short ETF positions (standard deviations in parentheses) 7

8 Figure 4-Average optimal asset allocation with up to 20% short ETF positions (standard deviations in parentheses) Figure 5-Average optimal asset allocation with up to 100% short ETF positions (standard deviations in parentheses) Figure 2 shows that an investor maximizes his expected Sharpe ratio by investing half of his wealth in the S&P500. This result is intuitive as the United States represents roughly 50% of world market capitalisation. 2 The remaining half is invested in the ishares of Spain, Italy, the UK, Sweden, Canada, Mexico and France (in order of decreasing importance). The other nine ETFs have a zero weight. A glance at figure 1 can explain the relative importance of each asset. As expected, the assets that lie in the North-West of the graph have higher weights than the dominated assets in the South-East of figure 1. Note also that the relatively high standard deviations of the optimal asset allocations in parentheses (especially for the S&P500 and the ishare of Spain) indicate that the optimal asset allocations are quite volatile and time-dependent. This is in line with the idea that the world economy underwent a recession over part of the period covered. As a result, average returns decreased, standard deviations and correlations increased, leading to optimal asset allocations that were also changing. 8 Figure 3 allows for short ishares positions of up to 5%. Per dollar invested, the optimizer recommends short-selling $0.44 and investing the proceeds in the S&P500 and the ishares of Spain, 2 - International Federation of Stock Exchanges

9 Italy, the UK, France, Canada and Sweden (in order of decreasing importance). The ishares that are sold short are those of Australia, Austria, Belgium, Germany, Hong Kong, Malaysia, Mexico, the Netherlands, Singapore and Switzerland. As expected, these ishares were dominated in figure 1 and got zero or near zero weights in figure 2. As such, they are good candidates as risk minimisers through short positions. Typically, the average short ETF position equals 20% of the ETF market capitalisation. With this in mind, figure 4 presents the average optimal asset allocation when short ETF interests are restricted to 20%. Now, per dollar invested, the investor increases his short position threefold relative to the 5% short case. The optimizer indeed recommends short-selling $1.38 and investing the proceeds in the asset classes that offer the best risk-adjusted mean returns in figure 1. Figure 5 considers the case of a hedge fund with short ishare positions of up to 100%. The average optimal asset allocation changes dramatically once again. Per dollar invested, an investor sells $6.84 short and invests the proceeds in the assets that offer relatively higher returns and/or lower risk (such as the S&P 500 and the ishares of Spain, France and Mexico). As in figures 3 and 4, the ishares that attract the shortest positions in figure 5 are those of Singapore, Germany, Switzerland, Austria, Australia, Belgium, Hong Kong and the Netherlands. These ETFs have relatively lower risk-adjusted returns in table 1. Therefore, selling them short and investing the proceeds in assets with relatively lower risk and higher return enhances meanvariance efficiency. A word of caution is warranted at this stage. This paper assumes that asset managers take the asset allocations the optimizer defines as face value. In reality, asset managers only consider the output of the optimizer as indication of portfolio weights. They typically recognize the limitations of optimizers 3 and combine the optimizer s quantitative approach with their own forecast of the relative performance of the different asset classes. This paper, on the other hand, reports only the solutions of the optimization problem. Because of this limitation, out-of-sample return forecasts are meaningless in this setting. 6. Mean-Variance Efficient Frontiers Figure 6 plots the mean-variance efficient frontiers with and without risk-free lending and borrowing of an US investor who considers i) the S&P500 only, ii) the S&P500 along with long and/ or short ishares positions over the whole sample. It is obvious from the graph that the inclusion of country-specific ETFs shifts the frontier of an US investor to the North-West. The results are even more dramatic when ETFs are sold short. Because long and short strategies with ETFs consistently dominate the US mean-variance efficient frontier, investing in country-specific ETFs enhances the asset allocation of an US investor. Figure 6 - Mean-variance efficient frontiers under different restrictions 3 - Mean-variance optimizers are very sensitive to small changes in the initial estimates of the return distribution. Besides, the estimates with the biggest measurement errors are likely to have the most prevalent influence on the optimal asset allocation. For this reason, mean-variance optimizers have been criticized as estimation error maximisers (Michaud (1989)). Attempts have been made to correct for these limitations, using, for example, Bayes-Stein estimators (Chopra, Hensel, and Turner (1993)) or by constraining the outputs (Sheedy, Trevor, and Wood (1999)). In the end, mean-variance optimizers cannot replace the informed judgement of an asset manager. The optimizer is simply a computational convenience that helps him translate his forecasts of assets returns, risks and correlations in an efficient portfolio. 9

10 In an attempt to quantify the gains from investing in country-specific ETFs, I consider two representative clients with different degrees of risk aversion. The first one is relatively more risk averse and requires a standard deviation of only 10%. The second one is willing, if necessary, to borrow at the risk-free rate to achieve an average return of 25%. Table 2 reports the average return of client 1 s portfolio and the standard deviation of client 2 s portfolio under different restrictions. The table shows that we can systematically increase the average return and decrease the standard deviation, of a US-only portfolio by investing in country-specific ETFs. For example, for a standard deviation of 10%, allowing long positions in country-specific ETFs substantially increases average returns by 17%. This illustrates the benefits of portfolio diversification as previously reported by Levy and Sarnat (1970), Solnik (1974), or Odier and Solnik (1993). By diversifying internationally using ishares MSCI country funds, we eliminate country-specific risk and consequently enhance mean-variance efficiency. Table 2-Efficiency Test Because it is expensive, and even often impossible, to sell conventional country funds short, global investors could not in the past fully benefit from international portfolio diversification. However, the introduction of country-specific ishares and the possibility of selling them short have opened up new horizons. Figure 6 and table 2 clearly indicate that short selling ETFs enhances the benefits of international portfolio diversification even further. Compared to a US investment, short-selling up to 20% ishares increases the average return of a 10% risk US portfolio by 73%. Similarly, for an average return of 25%, an investor can reduce his risk by more than half if he takes short positions of up to 20% in ETFs. A more conservative investor, unwilling to short sell ETFs by more than 5%, can still enjoy lower risk than an investor who is only long ETFs. The efficiency gains are even more substantial for hedge funds; namely, when ishares are allowed to be sold short by up to 100%. Then, relative to as US-only investment, the average return of a portfolio with a 10% risk is increased by 130%, while the risk of a portfolio with a 25% average return is decreased by 69%. 7. Conclusions It is well known that country-specific ETFs offer the benefits of international portfolio diversification at a lower cost, with a lower tracking error and in a more tax-efficient way than passive open or closed-end country funds. This article focuses on another important feature of country-specific ETFs, short-selling. It argues that this feature enhances the benefits of diversification even further and makes ETFs a more efficient approach to global asset allocation. The results indicate that US investors, who take long and short positions in ishares MSCI country funds, can achieve higher average returns for a given level of risk or, alternatively, lower risk for a given average return. On the other hand, global index funds cannot be sold short on a downtick. As a result, they are poorer portfolio diversifiers: their inclusion in a portfolio leads to asset allocation that is inefficient relative to country-specific ETFs. Since stock index futures can also be sold short at any time during the trading day, similar efficiency gains could be achieved with stock index futures. Unlike country-specific ETFs however, stock 10

11 index futures are not tax friendly, they need to be rolled over before maturity, they are marked to market and they are subject to margin calls. These features could make them less profitable and more burdensome to long-term investors. ETF futures 4 were introduced in November 2002, making it possible now for investors to benefit from the tax advantages of ETFs and the leverage of futures all at once. In the not too distant future we can reasonably expect more financial innovation in this area. The introduction of futures on country-specific ETFs would then make it possible to replicate the efficiency gains of short-selling mentioned in this paper, without sacrificing the tax efficiency of country-specific ETFs. 4 - US examples include the DIAMONDs futures and the ETF futures that track the Nasdaq 100, Russell 1000, Russell 2000 and Russell 3000 indices. Similar financial innovation took place in Europe with the Euro Stoxx 50, DAX and SMI ETF futures. 11

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