Author:Huxley, Stephen J.
  1. Introduction

    Professional portfolio managers are attracted to emerging market stocks because of their returns. But they dislike them because of their volatility. The question then becomes: which policies should a country pursue to make itself more attractive to portfolio managers and gain a larger market share of the portfolio allocated to emerging markets (or within the portfolio of a mutual fund devoted entirely to emerging markets)? Will it be more successful if it takes steps to increase returns or decrease volatility by simply moderating its most extreme outlier returns? This paper seeks to answer such questions using Peru as an example.

    Admittedly, any country could be used as an example (for a complete list of emerging market countries, see IMF, 2015). Peru was chosen primarily because data was readily available, it has been an emerging market for some time due to its stability, it was one of the earliest South American countries to become connected with Europe (the original Inca capital was located in Cusco, Peru, when Pizzaro arrived), and, on a personal note, Peru is the country personally visited several times by one of the authors in recent years.

    The assumptions were also made that if 1) the results suggest that a moderation strategy would achieve the goal of capturing a larger market share and 2) policies were adopted that attained those results, other countries would not take immediate retaliatory steps to counter Peru. A larger study, perhaps for the future, would attempt to see if the strategy might work in other countries or if it is unique to Peru. Such a study would be questionable, of course, because it would require many speculations regarding lag effects, dynamic interactions, etc.

    Using data on annual returns for 1995-2014, we explore how portfolio managers would allocate their funds to Peru versus other emerging market stocks for three of the most popular investment strategies: A) maximize risk adjusted returns (Sharpe, 1994); B) maximize minimum gain (Young, 1998; Huxley and Burns, 2016); C) maximize absolute return (Zask, 2013). Each strategy is tested to see how sensitive the allocation to Peru stocks is within the emerging markets portion of a portfolio, assuming government policies were successful in efforts to increase returns overall or simply moderate extreme returns.

    This approach differs from much of the prior research in emerging markets investing. Prior research tended to focus on explaining returns using the Fama-French three factor model (and its variants) or predicting returns based on technical analysis. The literature on explaining returns tends to demonstrate that the size and value premiums discussed for U.S. stocks in the original Fama-French article (Fama-French, 1992) hold true for developed international and emerging markets stocks as well as US stocks (Fama and French, 2012), though with a few minor exceptions (Cakicia, Fabozzi, and Tana, 2013). Other examples include D'Arcangelis and Galloppo (2015), Foye, Mramor, and Pahor (2013), and Martin and Rey (2006). Wagner and Margaritis (2017) provide the largest empirical study to date, analyzing returns from 5,175 emerging market funds over the period 1992-2012.

    Examples of the literature on predicting returns include Ghysels, Palazzi, and Valkanov (2016), Li, Richarson, and Tuna (2014), Maio and Philip (2015), and Morck, Yu, and Yeung (2000). The focus on prediction presumably stems from the assumption by the researchers that investors follow an "active management" approach to investing, where predictions for market timing and individual stock selection drive investment decisions. The International Investment Funds Association (2016) reports that about $17 trillion was invested in mutual funds near the end of 2016, about two-thirds of which was in actively managed funds.

    But to assume active management is the only strategy followed by portfolio managers is to ignore the other 33% of portfolios managers who eschew active management in favor of "passive management." Passive portfolio managers do not try to time the market or pick hot stocks. They develop long term strategies to create portfolios that include all stocks that fit the profile they are seeking. They prefer index mutual funds, low fees, and a buy-andhold philosophy. Indeed, this is the strategy adopted by the investment company founded by French and Fama.

    The debate as to which approach, active vs. passive, provides better returns over the long run has been ongoing for years. But empirical evidence has been piling up against active management. See Sinquefield (1995) and IFA (2009) for academic illustrations of the evidence and debate over time. Examples of books would include Bernstein (2001), Bogle (1999), Clyatt (2005), Malkiel (1996), Murray and Goldie (2010), Siegel (2008), Sherden (1998), Swedroe (1998), Taleb (2007). For websites, a simple Google search of "active vs. passive management" yields over 45 million hits.

    Standard & Poor tracks the performance of actively managed funds against their index benchmarks in their SPIVA scorecard quarterly (S&P Dow Jones Indices, 2017). At the end of 2016, they reported that 82% of actively managed funds failed to beat their benchmarks over the past 15 years. According to the Wall Street Journal (Maxey and Dietrich, 2017), approximately $1.2 trillion has been withdrawn from actively managed funds and nearly all of it, $1.1 trillion, has moved to passively managed index funds since 2007. According to Moody's, passive investing will become larger than active investing by 2024 (Moody's, 2017).

    The bottom line is that analysis of global portfolio management behavior based on the presumption of passive management is as relevant as analysis based on the presumption of active management and is likely to become more so if current trends away from active management continue in the future.

  2. How Portfolio Managers View Investing in Emerging Markets

    For passive portfolio managers, getting the basic asset allocation correct is crucial. Unlike their active management peers, once their allocation strategy has been decided upon, they are pledged to stick to it. A typical sequence of decisions is: 1) choose how to divide the portfolio between stocks and bonds; 2) choose how to distribute the equity portion globally; and 3) allocate among various asset classes categorized in the size/style box (small cap, medium, or large cap, value, blend, or growth).

    For U.S. managers, the decision regarding the global dispersion generally divides the world into three types of markets: 1) United States, 2) Developed markets ex US, and 3) Emerging markets.

    Portfolio managers have much to choose from in terms of allocating their funds among the three broad categories. Total equity market capitalization for the world, including the $17 trillion in mutual funds, had reached about $46 trillion at the end of 2014 (Dimensional Fund Associates Matrix Book, 2015). This excludes the global bond market, which accounted for about another $36 trillion investment capital. The U.S. is by far the largest equity market, capturing 52% of the world's total. Developed markets outside the U.S. account for 35%. Emerging markets account for 12%. (The remaining 1% of publicly traded stocks fall outside these three categories and are located in a fourth category, "Frontier" markets, which are small, pre-emergent economies.)

    Some managers constrain their algorithms by making direct "macro" decisions on how much to allocate to each of the three global categories, such as 70% US, 20% Developed, and 10% Emerging, then selecting the asset classes within these constraints. Others let their algorithms run with no constraints, choosing whatever asset classes fits their strategy. They then make changes globally by shifting allocations to reach what they subjectively feel to be a more "reasonable" allocation globally. Mathematical models can never capture all the factors in making investment decisions, and few American portfolio managers would put 100% of their funds into emerging markets even if their algorithms said so.

    2.1 Data on Returns

    Exhibit 1 presents MSCI data on returns from 1995-2014 on 21 emerging markets in USD adjusted for currency changes (DFA, 2015).

    These data, compiled by Morgan Stanley for its MSCI, are the only returns data used for this study. The countries are grouped together loosely by color based on geographic proximity, then sorted by average annual stock returns within their geographic area (Peru is the only exception, listed in the first column). The overall average return for all countries is 17%, with an average minimum of -54%, an average standard deviation of 45%, and an average/standard deviation ratio of .37.

    Exhibits 2 and 3 compare returns for US, developed ex US, and emerging markets. It is clear that emerging markets have both the highest and most variable returns among the three global categories. Peru is shown separately. Its average return is above average, and its standard deviation is below the average for all emerging markets. This gives it a better than average chance of capturing the attention of emerging market managers.

  3. Global Portfolio Manager Strategies

    Most global managers utilize one of the following three strategies to achieve goals for their portfolios:

    Strategy A--Maximize Risk-Adjusted Return (Sharpe Ratio): This manager...

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