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    Qir 2013q1 us Qir 2013q1 us Document Transcript

    • FIRST QUARTER 2013 Profitability and Expected Returns For more than three decades, Dimensional Fund Advisors has managed investment strategies that consistently focus on the dimensions of expected returns. 2 Research Update: Average Returns, B/M, Profitability, and Growth 4 Research Update: Expected Profitability: A New Dimension of Expected Returns 8 Research Update: Applying Direct Profitability to US Large Caps 12 What’s New at Dimensional 13 Appendix In this issue of the Institutional Review, we discuss: • How the dividend discount model identifies expected profitability as a dimension of expected returns • The development of direct profitability as a persistent and pervasive proxy for expected profitability • The potential value added from incorporating the expected profitability dimension into investment strategies focused on a universe of US large cap stocks The material in this publication is provided solely as background information for registered investment advisors and institutional investors and is not intended for public use. Unauthorized copying, reproducing, duplicating, or transmitting of this material is prohibited. Dimensional Fund Advisors LP is an investment advisor registered with the Securities and Exchange Commission. Expressed opinions are subject to change without notice in reaction to shifting market conditions. All materials presented are compiled from sources believed to be reliable and current, but accuracy cannot be guaranteed. This article is distributed for educational purposes, and it is not to be construed as a recommendation of any particular security, strategy, or investment product.
    • Markets and Fair Prices In a “pure” market economy, cooperation among individuals is achieved entirely through voluntary exchange. In its simplest form, such an economy consists of a number of individual households—a collection of Robinson Crusoes, as it were. Each household uses the resources it controls to produce goods and services that it exchanges for goods and services produced by other households, on terms mutually acceptable to the two parties to the bargain. It is thereby enabled to satisfy its wants indirectly by producing goods and services for others, rather than directly by producing goods for its own immediate use. The incentive for adopting this indirect route is, of course, the increased product made possible by division of labor and specialization of function. Since the household always has the alternative of producing directly for itself, it need not enter into any exchanges unless it benefits from it. Hence, no exchange will take place unless both parties do benefit from it. —Milton Friedman Price Theory (New York: Aldine Publishing Company, 1976), 5.
    • R E SE A R CH U PDATE Eugene Fama Director and Consultant, Dimensional Fund Advisors Ken French Director and Consultant, Dimensional Fund Advisors Average Returns, B/M, Profitability, and Growth There is lots of evidence that average returns on stocks are related to the book-to-market equity ratio. There is mounting evidence that profitability adds to the description of average returns provided by the book-to-market ratio, and there is (less consistent) evidence that investment is an additional dimension of average returns. The logic for why these three variables are related to average returns centers on the dividend discount model, which is the simplest valuation model for the price of a stock. In the dividend discount model, the market value of a share of a firm’s stock is the present value of expected future dividends per share, (1) where Mt is the share price at time t, E(Dt+τ) is the expected dividend per share in period t+τ, and r is (approximately) the long-term average expected stock return or, more precisely, the internal rate of return on expected dividends. With clean surplus accounting (all earnings flow through the income statement), the time t dividend, Dt, is equity earnings per share, Yt , minus the change in book equity per share, dBt = Bt – Bt-1. The dividend discount model then becomes, (2) Dividing by time t book equity gives us, (3) Equation (3) makes three statements about expected stock returns. First, consider a set of given values of expected future earnings and expected changes in book equity (both measured relative to a fixed value of current book equity). In other words, fix the values of everything in equation (3) except the current stock price, Mt , and the expected stock return, r. Then the equation tells us that a lower stock price Mt (and thus a lower market-to-book ratio, Mt/Bt) implies a higher expected stock return, r. (Note that r is actually in the denominator of equation (3).) Equivalently, a higher bookto-market equity ratio, Bt /Mt, implies a higher expected stock return, r. This is the rationale for using the book-to-market ratio as a proxy for expected return. The next prediction of equation (3) is that for fixed values of Bt , Mt , and expected growth in book equity due to reinvestment of earnings, more profitable firms—specifically, firms with higher expected earnings relative to current book equity— have higher expected returns. In slightly different words, suppose we fix the price Mt and the values of everything else in equation (3) except for expected future earnings and the discount rate (the expected stock return). Then the equation tells us that higher expected future earnings imply higher expected stock returns. This is the motivation for tests of a positive relation between expected stock returns and expected profitability. The final implication of equation (3) is that for fixed values of Bt , Mt , and expected future earnings growth, higher expected growth in book equity due to reinvestment of earnings implies lower expected returns. In other words, if we fix the price Mt and the values of everything in equation (3) except expected future investment and the discount rate r, then the equation tells us that that higher expected future investments imply a lower expected stock return, r. This is the rationale for a negative relation between expected stock returns and expected investment. Why has it been so difficult to document profitability and investment effects in average returns? The only directly observable variables in equation (3) are the current stock price, Mt , and the current book value of equity, Bt , which means we also know the book-to-market ratio, Bt /Mt. This is perhaps why it has been relatively easy to document the See “Appendix: Standardized Performance Data and Disclosures” for how to obtain complete information on performance, investment objectives, risks, advisory fees, and expenses of Dimensional’s funds. 2
    • R E SE A R CH U PDATE relation between Bt /Mt and average returns. In contrast, we do not know the sequence of expected future earnings or the sequence of expected investments in (3), and empirical work requires that we use proxies that are informative about the expected future values of interest. This is the stumbling block in research on the links between expected returns and profitability or investment. Recent papers seem to do a better job on proxies for expected profitability, and this is the basis for Dimensional’s interest in widening the scope of their estimates of expected returns to allow for profitability effects. Academic empirical work so far has not identified proxies for expected investment that uncover the relations between average returns and investment in a robust way. In the future this may be the source of additional enhancements to Dimensional’s product line. But it’s too early to even hazard a guess on this one. See “Appendix: Standardized Performance Data and Disclosures” for how to obtain complete information on performance, investment objectives, risks, advisory fees, and expenses of Dimensional’s funds. 3
    • R E SE A R CH U PDATE Gerard O’Reilly, PhD Head of Research and Vice President, Dimensional Fund Advisors Savina Rizova, PhD Vice President, Dimensional Fund Advisors Expected Profitability: A New Dimension of Expected Returns For more than three decades, Dimensional has excelled at identifying academic findings that can be used to benefit our clients’ portfolios. Financial economists uncover many variables that appear to drive differences in average returns. When determining which of those variables should be considered a dimension of expected returns, we require that they: 1. Be sensible 2. Be persistent, pervasive, and robust 3. Allow the cost-effective capture of higher expected returns Financial economics suggests that expected profitability should be related to expected equity returns.1 Controlling for other dimensions of expected returns, such as relative price and market capitalization, more profitable firms should have higher expected returns than less profitable firms. This paper develops a reliable proxy for expected profitability. We show how this proxy has been persistently and pervasively related to average returns. We test whether that relation is empirically robust to the proxy’s construction and whether profitability can be used in the design of investment solutions. TAKING ACADEMIC RESEARCH TO PRACTICE To capture the profitability dimension, one would need a reliable and robust proxy for expected profitability. Because a firm’s profitability tends to be persistent through time, measures of current profitability are likely to be good proxies for expected profitability. Table 1 shows regressions of future profitability on current profitability using different measures of profitability that range from the bottom of the income statement (net income scaled by book value) to the top of the income statement (sales scaled by book). These regressions include firm size and relative price (as measured by the Table 1 Regressions of Future Profitability on Current Profitability, Controlling for Size and Relative Price (1975–2012) beta3 YEAR 1 t(beta3) R2 YEAR 2 beta3 t(beta3) R2 YEAR 3 beta3 t(beta3) R2 YEAR 7 beta3 t(beta3) R2 Panel A: Profitability Defined as Sales/Book Large 0.88 55.37 0.84 0.82 35.46 0.73 0.81 31.00 0.65 0.73 28.61 0.42 Small 0.83 45.67 0.65 0.75 33.66 0.50 0.69 32.87 0.41 0.55 27.95 0.26 All 0.84 50.12 0.68 0.76 37.29 0.54 0.71 36.05 0.45 0.58 28.03 0.28 Panel B: Profitability Defined as (Net Operating Income Before Amortization and Depreciation)/Book Large 0.77 36.93 0.70 0.66 23.40 0.51 0.64 22.54 0.39 0.55 18.91 0.20 Small 0.70 28.60 0.47 0.57 20.10 0.30 0.49 19.99 0.23 0.32 22.38 0.12 All 0.71 32.98 0.52 0.59 22.82 0.35 0.51 22.85 0.28 0.36 27.21 0.16 Panel C: Profitability Defined as (Net Operating Income Before Amortization and Depreciation minus Interest Expense)/Book Large 0.72 37.08 0.66 0.58 26.82 0.46 0.53 30.19 0.33 0.42 21.22 0.14 Small 0.68 24.16 0.42 0.53 16.02 0.26 0.45 15.89 0.20 0.29 16.40 0.11 All 0.70 27.81 0.48 0.55 18.56 0.32 0.47 17.96 0.25 0.31 18.91 0.15 Panel D: Profitability Defined as Net Income/Book Large 18.74 0.41 0.32 11.05 0.26 0.32 9.15 0.17 0.25 7.31 0.06 Small 0.52 15.84 0.22 0.37 10.02 0.12 0.28 10.58 0.08 0.14 10.45 0.04 All 4 0.45 0.53 17.95 0.26 0.38 11.27 0.15 0.30 11.51 0.10 0.17 13.05 0.06 Profitability(t+y) = a + beta1 Ln ME(t) + beta2 Ln BTM(t) + beta3 Profitability(t). Past performance is no guarantee of future results. Source: Dimensional using CRSP and Compustat data. CRSP data provided by the Center for Research in Security Prices.
    • R E SE A R CH U PDATE book-to-market ratio) as explanatory variables, effectively controlling for existing dimensions of expected returns. Using data on US stocks from 1975 to 2012, these regressions show that all the profitability coefficients are economically large and statistically reliable. In addition, this simple regression model that uses current profitability can explain from 22% to 84% of the variation in next year’s profitability, depending on the variable used to measure profitability.2 Even more impressive, perhaps, is that current profitability can explain between 4% and 42% of the variability of profitability seven years into the future. As Table 1 shows, various measures of profitability appear to do a good job of forecasting future profitability. Moreover, Table 2 shows that, after controlling for size and relative price, these measures of profitability yield large and reliable spreads in average returns. Table 2 US Profitability Premiums, 1975−2012 High Low H – L Annualized Average Return (%) 15.99 11.70 4.29 Annualized Standard Dev. (%) 18.77 18.63 7.34 Panel A: Profitability Defined as Sales/Book t-statistic 3.61 Panel B: Profitability Defined as (Operating Income before Depreciation and Amortization)/Book Annualized Average Return (%) 16.43 11.54 4.89 Annualized Standard Dev. (%) 18.01 20.73 8.66 t-statistic 3.48 Panel C: Profitability Defined as (Operating Income before Depreciation and Amortization minus Interest Expense)/Book Annualized Average Return (%) 17.03 11.70 5.33 Annualized Standard Dev. (%) 17.27 21.14 9.03 t-statistic 3.64 Panel D: Profitability Defined as Net Income/Book Annualized Average Return (%) 16.10 12.32 3.78 Annualized Standard Dev. (%) 17.78 21.22 8.19 t-statistic 2.84 Past performance is no guarantee of future results. Source: Dimensional using CRSP and Compustat data. CRSP data provided by the Center for Research in Security Prices. We sort stocks into two size groups. Large is defined as the top 90% of the US total market capitalization. Small is the bottom 10%. Within each size group, we sort stocks into three relative price groups (each representing one-third of the market capitalization). Similarly, we sort stocks into three profitability groups. We compute the value-weighted returns of six high-profitability size/relative price indices (large/low relative price, large/medium relative price, large/high relative price, small/low relative price, small/medium relative price, and small/high relative price). The monthly return of the high-profitability index is the simple average of the monthly returns of the six high-profitability size/relative price indices. We use an analogous procedure to compute returns for the low-profitability index. We rebalance once per year. To summarize, Tables 1 and 2 show that after controlling for size and relative price, different profitability measures perform well in forecasting future profitability and generating spreads in average returns.3 Moreover, scaling profits by assets (rather than book) yields similar results. It is sensible to expect that, if current profitability is related to expected profitability, current profitability should be related to average returns. This is exactly what we observe in the data: a strong empirical relation between current profitability and future profitability and average returns. Further, our empirical observations are robust to many different measures of current profitability. This finding is important when informing expectations of what will drive expected returns and how we can use the information in current profitability to build robust portfolios—that is, portfolios that deliver reliable results under a wide variety of market conditions. Finally, the high- and low-profitability portfolios presented in Table 2 are rebalanced annually. The differences in average returns based on annual rebalancing, along with the persistence of firm-level profitability, suggest a level of turnover that allows for the cost-effective capture of this dimension of expected returns. CHOOSING A PROXY—DIRECT PROFITABILITY To incorporate profitability in investment solutions, we need to consider a number of selection criteria. The profitability measure should (1) exclude nonrecurring items of profitability, (2) be comprehensive, and (3) be comparable across sectors. The first requirement suggests that measures at the bottom of the income statement, such as net income to book, are inappropriate because they are often affected by extraordinary items, discontinued operations, unusual charges to depreciation and amortization, and other items that are unlikely to persist in the future. Going to the top of the income statement (sales – cost of goods sold scaled by book or assets) also seems inappropriate because it does not satisfy the second requirement. (The profitability measure needs to be comprehensive.) Major operating expenses, such as staff compensation, are classified as cost of goods sold (COGS) in See “Appendix: Standardized Performance Data and Disclosures” for how to obtain complete information on performance, investment objectives, risks, advisory fees, and expenses of Dimensional’s funds. 5
    • R E SE A R CH U PDATE some industries and as selling, general, and administrative (SGA) expenses in others. Moreover, the breakdown between COGS and SGA expenses is often vague and arbitrary. Therefore, a comprehensive profitability measure needs to take into account both COGS and SGA expenses. However, the current profits measure should be comparable across sectors. For financials, the main cost of doing business is the cost of borrowing, which implies that interest expense needs to be incorporated into the profitability measure. If the numerator of the profitability measure reflects leverage, however, the denominator should also reflect it. Therefore, the natural choice for the denominator should be book value. Moreover, valuation theory also suggests scaling profits by book equity. Hence, the most appropriate proxy for expected profitability is sales minus COGS minus SGA minus interest expense, scaled by book equity. This proxy has strong support, both empirical and theoretical. In accounting terms, it is operating income before depreciation and amortization minus interest expense, scaled by book equity. We refer to this variable as direct profitability. DIRECT PROFITABILITY PREMIUM— PERSISTENT AND PERVASIVE Empowered with a robust proxy for expected profitability, we can explore the pervasiveness and persistence of the expected profitability dimension across countries and regions. Table 3 presents the historical performance of high and low direct profitability stocks in the US, non-US developed markets, and emerging markets. In the US and non-US developed markets, the monthly return on the high or low direct profitability indices is computed in the manner described above for Table 2. In emerging markets, the monthly return on the high direct profitability index is the simple average of the monthly returns of three value-weighted high direct profitability indices (high profitability and low, medium, and high relative price). We use an analogous procedure to compute the returns of the emerging markets low direct profitability index. Thus, the returns on the high and low direct profitability indices are constructed to control for size and relative price effects. Table 3 shows high direct profitability stocks outperform low direct profitability stocks in all three regions (US, non-US developed, and emerging markets). In the US, the annualized average return on high direct profitability stocks is 17.03% vs. 11.70% for low direct profitability stocks from 1975 to 2012. The direct profitability premium is 5.33% per year and is statistically reliable (t-statistic of 3.64). In non-US developed markets, the average annualized return on high direct profitability stocks is 10.15% vs. 4.69% for low direct profitability stocks from July 1991 to December 2012. The direct profitability premium in non-US developed markets is 5.46% per year and is also statistically different from zero (with a t-statistic of 5.30). Finally, in emerging markets, high direct profitability stocks earned an annualized average return of 13.50%, while low direct profitability stocks earned an annualized return of 7.38% from July 1995 to December 2012. Hence, the direct profitability premium in emerging markets is 6.12% per year with a t-statistic of 4.79. Overall, Table 3 reveals that the premium associated with direct profitability is pervasive across stock markets. Is the direct profitability premium also persistent through time? Figure 1 provides a positive answer. This figure plots the difference in annualized five-year rolling returns between high and low direct profitability stocks for the US, non-US developed markets, and emerging markets. In all three regions, we see that high direct profitability stocks outperformed low direct profitability stocks throughout most of the period. In short, the direct profitability premium is both pervasive across markets and persistent through time. Table 3 Summary Statistics for the Direct Profitability Premium US Market 1/1975–12/2012 Non-US Developed Markets 7/1991–12/2012 Emerging Markets 7/1995–12/2012 High DPB Low DPB H – L High DPB Low DPB H –  L High DPB Low DPB H –  L Annualized Average Return (%) 17.03 11.70 5.33 10.15 4.69 5.46 13.50 7.38 6.12 Annualized Standard Deviation (%) 17.27 21.14 9.03 17.36 18.57 4.77 23.88 25.65 5.35 — — 3.64 — — 5.30 — — 4.79 t-statistic Past performance is no guarantee of future results. Asset class and profitability filters were applied to data retroactively and with the benefit of hindsight. Returns are not representative of indices or actual portfolios and do not reflect costs and fees associated with an actual investment. Source: Dimensional using CRSP, Compustat, and Bloomberg data. CRSP data provided by the Center for Research in Security Prices. 6
    • R E SE A R CH U PDATE Figure 1 Rolling Five-Year Returns for Direct Profitability Premium 18 United States 16 Non-US Developed 14 Emerging Markets Return (%) 12 10 8 6 4 2 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981 -4 1980 -2 1979 0 Past performance is no guarantee of future results. Asset class and profitability filters were applied to data retroactively and with the benefit of hindsight. Returns are not representative of indices or actual portfolios, and do not reflect costs and fees associated with an actual investment. Source: Dimensional using CRSP, Compustat, and Bloomberg data. CRSP data provided by the Center for Research in Security Prices. CONCLUSION Financial economics shows that higher expected profitability is related to higher expected returns, holding all else fixed. Thanks to recent research advances, we now have a robust proxy for expected profitability—direct profitability. Using this proxy, we find that high direct profitability stocks tend to outperform low direct profitability stocks across stock markets and over time, which is consistent with financial economics. The direct profitability premium is pervasive and persistent. Moreover, our analysis indicates that profitability can be used in investment strategies to improve their expected returns while maintaining their broad diversification. Therefore, expected profitability is a new dimension of expected equity returns that we can use to build better investment solutions. In a separate paper, we show how, by incorporating all three dimensions of expected equity returns (company size, relative price, and expected profitability) in the structure of a portfolio, we can use the information in all three dimensions to improve the reliability of the outcome. Put another way, we can increase the probability of achieving higher expected returns vs. the market or an asset class. Dimensional has managed strategies that consistently focus on the dimensions of expected returns for more than three decades. We began in 1981 with small cap strategies. We have launched large, small, and core strategies investing in the US, developed, and emerging markets—fully integrated strategies that target more than one dimension of expected returns. Our strategies have targeted the dimensions of expected returns for our clients in a cost-effective way over many market cycles. By efficiently balancing competing premiums, keeping turnover low, and trading with patience and flexibility, we have consistently added value relative to our peers. Incorporating direct profitability into our strategies is the natural next step toward building better investment solutions for our clients. REFERENCES 1. See, for instance, Fama, Eugene F., and Kenneth R. French. “Average Returns, B/M, Profitability, and Growth.” Dimensional Fund Advisors’ Quarterly Institutional Review 8, no. 1 (2013): 2–3. 2. For comparison, in cross-sectional regressions of firm returns on firm characteristics, the explained variation is usually 1%−2%. 3. Tables 1 and 2 focus on the US market because it has the longest available history. Results for non-US markets are qualitatively similar. Past performance is no guarantee of future results. Past performance is no guarantee of future success. There is no guarantee strategies will be successful. See “Appendix: Standardized Performance Data and Disclosures” for how to obtain complete information on performance, investment objectives, risks, advisory fees, and expenses of Dimensional’s funds. 7
    • R E SE A R CH U PDATE Gerard O’Reilly, PhD Head of Research and Vice President, Dimensional Fund Advisors Savina Rizova, PhD Vice President, Dimensional Fund Advisors Applying Direct Profitability to US Large Caps Using the direct profitability measure described by Gerard O’Reilly and Savina Rizova (2013),1 this is the first in a series of papers that explore the value added by incorporating the expected profitability dimension in investment strategies. We examine how profitability can be applied to a universe of US large cap stocks using two large cap indices. Figure 1 Applying Profitability to Large Caps IMPROVING EXPECTED RETURN (a) Selecting the firms with higher direct profitability, lower price-to-book ratios, or smaller market capitalization among large caps—that is, the firms with higher expected returns. One example of this approach is a strategy that only includes large cap value stocks to enjoy the equity premium plus a share of the value premium. (b) Including all large cap stocks but deviating from market capitalization weights to emphasize the dimensions of higher expected equity returns. That is, overweighting firms with higher direct profitability, lower price-tobook ratios, and lower market capitalization within the large cap universe. Overweighting some firms requires underweighting others. In this example, these are mega cap firms with lower direct profitability trading at higher relative prices. Strategies can be designed to focus on only one of those approaches (selection or weighting) or both (selection and weighting). The focus of this paper is security weighting. We will create a weighting schema that seeks to enhance expected returns without excluding any of the companies in the US large cap universe. The security weights are tied to all of the dimensions of higher expected returns among equities rather than a subset. The weighting schema balances exposures to each of these dimensions to increase the reliability of outcomes. Figure 1 illustrates the approach. US Large Cap Size Suppose we use a market capitalization-weighted index of US large cap securities as our base case. We know the dimensions of expected returns among equities are expected profitability, relative price, and company size. Thus, we can systematically improve the expected returns by: Direct Profitability Relative Price • Focuses on large cap companies, generally defined In a well-posed weighting schema, a security’s target weight as the top 1,000 by market cap should be strongly linked to its current price. Up-to-the• Increased focus on securities with higher expected returns minute news and changes in expectations are reflected in (higher profitability, lower relative price, and mid market cap) current prices. Breaking the link (or having a weak link) between target weight and current price exposes a strategy to unnecessary risk and leads to a poorly posed weighting schema. Examples of this include weighting schemas that equally weight or rank weight securities. A well-posed weighting schema can allow a security’s target weight to deviate from its market cap weight to increase expected portfolio returns. Such deviation, however, should be measured and controlled. One way of achieving this is to make a security’s target weight proportional to its market weight. The target weight can be greater than or less than its market cap weight, but the level of over- or underweight is relative to its market capitalization weight in the eligible universe. In the example in Figure 1, the relative over- or underweighting is a function of direct profitability, price-tobook, and market cap. The eligible universe is US large cap stocks. Having the right security weighting schema also helps avoid unnecessary turnover by continuously and gradually See “Appendix: Standardized Performance Data and Disclosures” for how to obtain complete information on performance, investment objectives, risks, advisory fees, and expenses of Dimensional’s funds. 8
    • R E SE A R CH U PDATE adjusting target weights with changes in prices. The right weighting schema also provides a very robust form of risk control. By construction, it explicitly controls the maximum and minimum relative over- and underweights of every security with respect to their market cap weights. EMPIRICAL EVIDENCE To explore the potential value added from focusing on all three dimensions of expected equity returns in US large cap stocks, we compare the performance of the Russell 1000 index to two simulated indices. These indices include a similar set of firms to the Russell 1000. They overweight (underweight) securities with higher (lower) expected returns relative to their market cap weight. The Large Cap 2 simulated index has an increased exposure to firms with higher direct profitability, lower price-to-book, and midcap companies compared to the Large Cap 1 index. The Large Cap 1 and Large Cap 2 indices use a measured and controlled weighting schema to increase exposure to higher expected return firms. Figure 2 plots the premium and tracking error of Large Cap 1 and Large Cap 2 vs. the Russell 1000 index from 1979 to 2012. The figure shows that, as we move from Large Cap 1 to Large Cap 2, we obtain a higher premium vs. the Russell 1000. Consistent with financial economics, the stronger the focus on the dimensions of higher expected returns, the higher the average return for an index. Figure 2 Premiums and Tracking Errors of US Large Cap Indices Tilting toward Higher Expected Returns Stocks Premium vs. Russell 1000 (%) 1.6 1.4 Large Cap 2 1.2 1.0 Large Cap 1 0.8 0.6 0.4 0.2 0.0 0.0 0.5 1.0 1.5 2.0 Tracking Error vs. Russell 1000 2.5 To examine in more detail the effect of tilting toward securities with higher expected returns in a large cap index, we report in Table 1 the historical performance of the two indices plotted in Figure 2 (Large Cap 1 and Large Cap 2) vs. the Russell 1000 index, as well as the Russell 1000 Growth and Russell 1000 Value indices.2 From 1979 to 2012, the annualized compound return for the Russell 1000 was 11.46% vs. 12.42% for Large Cap 1 and 12.94% for Large Cap 2. By focusing on the dimensions of higher expected returns, one could have improved the performance of a plain, large cap index by more than 1% per year. The higher average returns would have been achieved with a very similar standard deviation to the Russell 1000 but with smaller declines over a rolling 12- or 36-month window. For example, the worst annualized rolling 36-month return for both Large Cap 1 and 2 was -14.45% vs. -16.21% for the Russell 1000. Finally, Large Cap 1 and 2 do not outperform at the expense of narrower market coverage. They hold a similar number of securities to the Russell 1000 (see Table 2). This broad market coverage helps control the annualized tracking error and periods of underperformance vs. the Russell 1000. For Large Cap 1 and 2 vs. the Russell 1000, the tracking error was 1.29% and 2.12%, respectively, while the maximum rolling one-year underperformance was 3.81% and 6.55%, respectively. For comparison, the annualized tracking errors for the Russell 1000 Value and Growth indices were greater than 4.5%, while the maximum rolling one-year underperformance was close to 20%. The results in Table 1 and Figure 2 suggest that by structuring a large cap index to focus consistently on the dimensions of higher expected equity returns, one could improve expected portfolio returns and still maintain broad diversification. Table 2 illustrates that broad diversification. It shows characteristics of the Large Cap 1 and 2 indices, along with the Russell indices. It is evident from these data that Large Cap 1 and 2 are well diversified across securities and sectors. The size, price-to-book, and direct profitability characteristics illustrate how the Large Cap 1 and 2 indices balance the three complementary dimensions of expected returns to add value. This is the result of using a measured and controlled weighting schema. A similar technique could be used to increase the expected returns of a small cap or marketwide index. Past performance is no guarantee of future results. Indices are not available for direct investment. See “Appendix: Standardized Performance Data and Disclosures” for how to obtain complete information on performance, investment objectives, risks, advisory fees, and expenses of Dimensional’s funds. 9
    • R E SE A R CH U PDATE Table 1 Emphasizing Higher Direct Profitability, Lower Price-to-Book, and Small Market Capitalization within US Large Cap Stocks Large Cap 1 Large Cap 2 Russell 1000 Russell 1000 Growth Russell 1000 Value 12.42 12.94 11.46 10.55 11.97 0.96 1.48 — -0.91 0.51 15.78 15.72 15.60 17.61 14.95 Minimum Rolling One-Year Performance -42.34 -42.50 -43.62 -45.64 -47.35 Annualized Minimum Rolling Three-Year Performance -14.45 -14.45 -16.21 -25.64 -17.32 1.29 2.12 — 4.65 4.86 Maximum Rolling One-Year Underperformance (vs. Russell 1000) -3.81 -6.55 — -21.41 -18.59 Annualized Max. Rolling Three-Year Underperformance (vs. Russell 1000) -1.22 -3.87 — -10.55 -10.23 Data in USD as of 03/31/2013 Large Cap 1 Large Cap 2 Russell 1000 Russell 1000 Growth Russell 1000 Value Weighted Average Market Cap 91457 87359 94420 93712 95074 Aggregate Price-to-Book 2.02 2.11 2.07 3.84 1.47 Weighted Average Direct Profitability 0.36 0.39 0.35 0.47 0.25 Number of Securities 974 974 990 574 695 Consumer Discretionary 14% 14% 12% 17% 8% Consumer Staples 10% 10% 10% 13% 7% Energy 12% 14% 10% 4% 16% Financials 12% 10% 13% 3% 23% Healthcare 12% 11% 12% 13% 12% Industrials 12% 12% 11% 13% 9% Information Technology 18% 18% 17% 29% 7% Materials 5% 6% 4% 4% 4% REITs 0% 0% 3% 2% 4% Telecommunication Services 3% 3% 3% 2% 3% Utilities 4% 3% 4% 0% 7% 100% 100% 100% 100% 100% Annualized Compound Return Relative Premium (vs. Russell 1000) Annualized Standard Deviation Annualized Tracking Error (vs. Russell 1000) Table 2 Characteristics as of 03/31/2013 Total See “Appendix: Standardized Performance Data and Disclosures” for how to obtain complete information on performance, investment objectives, risks, advisory fees, and expenses of Dimensional’s funds. 10
    • R E SE A R CH U PDATE CONCLUSION When designing investment solutions, it is important to remember that prices are a reliable source of information, and that structure and implementation drive performance. The indices above illustrate how an investor can improve the expected return of large caps by (1) using multiple dimensions of expected returns and (2) overweighting firms with higher expected returns using a measured, controlled, low-turnover weighting schema that incorporates current price. Reliability of outcomes is increased by using diverse sources of value added (expected profitability, relative price, and size) and broad diversification across securities and sectors. Low turnover, combined with that broad diversification, allows for a disciplined and patient approach to trading that controls implementation costs. Large Cap 2—Dimensional created this index in January 2013 from data provided CRSP and Compustat. The index focuses on large cap companies and more strongly overweights higher expected return securities (higher profitability, lower relative price, and mid market cap). Profitability is measured as operating income before depreciation and amortization minus interest expense scaled by book. The index excludes REITs. The index is rebalanced annually, and back-tested performance results assume reinvestment of dividends and capital gains. Filters were applied to data retroactively and with the benefit of hindsight. Returns are not representative of actual portfolios and do not reflect costs and fees associated with an actual investment. Actual returns may be lower. It is not possible to invest directly in an index, which is unmanaged. In this way, such a solution would seek to add value for investors across all aspects of the investment process. REFERENCES APPENDIX Large Cap 1—Dimensional created this index in January 2013 from data provided by CRSP and Compustat. The index focuses on large cap companies and overweights slightly higher expected return securities (higher profitability, lower relative price, and mid market cap). Profitability is measured as operating income before depreciation and amortization minus interest expense scaled by book. The index excludes REITs. The index is rebalanced annually, and back-tested performance results assume reinvestment of dividends and capital gains. Filters were applied to data retroactively and with the benefit of hindsight. Returns are not representative of actual portfolios and do not reflect costs and fees associated with an actual investment. Actual returns may be lower. It is not possible to invest directly in an index, which is unmanaged. 1. O’Reilly, Gerard, and Savina Rizova. “Expected Profitability: A New Dimension of Expected Returns.” Dimensional Fund Advisors’ Quarterly Institutional Review 8, no. 1 (2013): 4–7. 2. Because the Russell Indexes start in 1979, our analysis here also starts in 1979. Past performance is no guarantee of future success. There is no guarantee strategies will be successful. See “Appendix: Standardized Performance Data and Disclosures” for how to obtain complete information on performance, investment objectives, risks, advisory fees, and expenses of Dimensional’s funds. 11
    • W H AT’S N E W AT D I M E N S I O N A L Dimensional News EUGENE FAMA RECEIVES BEST PERSPECTIVE AWARD FROM CFA INSTITUTE Eugene Fama—the Robert R. McCormick Distinguished Service Professor of Finance at the University of Chicago Booth School of Business, where he has taught since 1963, and a director and consultant at Dimensional— has received the 2012 Best Perspective Award from the CFA Institute “for the timeliest and most thoughtprovoking opinion article.” Fama was recognized for “An Experienced View on Markets and Investing,” published in the November/December issue of the Financial Analysts Journal. The article is available at http://www.cfapubs.org/doi/pdf/10.2469/faj.v68.n6.1. NEW DIMENSIONAL RESEARCH ON LISTED PRIVATE EQUITY AND GLOBAL DIVIDENDS Research Analyst Wes Crill and Vice President Marlena Lee, PhD, authored “When Private Equity Goes Public,” a new white paper that looks at the performance of the small segment of private equity investments traded on standard exchanges. Using data for an index of listed private equity (LPE), as those investments are known, the authors find that the performance of LPEs is well explained by controlling for market capitalization and relative price exposures. The paper is available at https://my.dimensional.com/insight/papers_ library/101833/. Stanley Black, PhD, an associate in the Research group at Dimensional, authored “Global Dividend-Paying Stocks: A Recent History,” a new white paper that uses global dividend data from 1991 to 2012 to address many of the issues investors need to consider when investing in dividend-paying stocks. What are the costs of investing only in firms that pay dividends or only in firms with high dividend yields? What is the tradeoff between diversification and higher dividend yield? How predictable are dividend payments? The paper is available at https://my.dimensional.com/insight/papers_ library/101581/. NEW DIMENSIONAL STRATEGIES LAUNCHED Dimensional Fund Advisors launched four new portfolios in late December 2012: • DFA US Large Cap Growth (DUSLX, CUSIP 233-20G-281) • DFA US Small Cap Growth (DSCGX, CUSIP 233-20G-273) • DFA International Large Cap Growth (DILRX, CUSIP 233-20G-265) • DFA International Small Cap Growth (DISMX, CUSIP 233-20G-257) For more than 30 years, we have incorporated research on the sources of expected return and their impact on portfolios, first with market capitalization and then with relative price, as measured, for instance, by price-to-book ratios. Over the years, there have been numerous enhancements to the ways we structure and implement our portfolios. Now, new research has identified a way to capture a dimension of returns related to expected profitability. While it has always been intuitive that a company’s future profitability is related to expected returns, it is not directly observable. We have made major advancements with research in this area and have found a proxy that is robust and can be applied to portfolio management. The addition of this new dimension has opened the door to the four new growth strategies. For more information about these or any other Dimensional strategies, please contact your regional director. See “Appendix: Standardized Performance Data and Disclosures” for how to obtain complete information on performance, investment objectives, risks, advisory fees, and expenses of Dimensional’s funds. 12
    • A PPE N D I X STANDARDIZED PERFORMANCE DATA As of March 31, 2013 One Year Five Years 10 Years Since Inception Inception Date US Core Equity 1 Portfolio1 15.78 7.19 — 6.10 9/15/2005 US Core Equity 2 Portfolio1 17.44 7.21 — 6.00 9/15/2005 US Vector Equity Portfolio 18.69 7.27 — 5.90 12/30/2005 US Micro Cap Portfolio 18.03 8.73 12.14 11.97 12/23/1981 US Small Cap Portfolio 18.23 10.23 12.73 10.39 3/19/1992 US Small Cap Value Portfolio 22.12 8.50 13.61 12.15 3/2/1993 — — — 12.25 12/20/2012 US Targeted Value Portfolio1 21.27 8.90 13.77 11.89 2/23/2000 US Large Cap Value Portfolio 22.53 6.13 10.43 9.77 2/19/1993 — — — 9.79 12/20/2012 US Large Company Portfolio 13.83 5.86 8.54 3.41 9/23/1999 Enhanced US Large Company Portfolio 14.48 6.46 8.49 7.37 7/2/1996 International Core Equity Portfolio1 9.90 0.10 — 4.26 9/15/2005 International Vector Equity Portfolio1 9.32 — — 3.61 8/14/2008 10.31 2.15 13.46 6.78 9/30/1996 13.03 1.67 14.37 7.43 12/29/1994 — — — 8.13 12/20/2012 6.63 -2.31 11.27 6.47 2/15/1994 — — — 4.55 12/20/2012 10.39 -0.48 9.73 5.77 7/17/1991 Emerging Markets Core Equity Portfolio1 3.79 3.40 — 11.29 4/5/2005 Emerging Markets Small Cap Portfolio 8.71 6.22 20.01 13.93 3/5/1998 Emerging Markets Value Portfolio 2.27 0.99 20.10 13.03 4/1/1998 Emerging Markets Portfolio 2.63 2.34 17.52 8.10 4/25/1994 6.00 — — 5.38 8/23/2010 — — — 12.78 11/1/2012 Global Allocation 25/75 Portfolio1 5.49 4.89 — 4.98 12/24/2003 Global Allocation 60/40 Portfolio1 9.75 5.30 — 6.38 12/24/2003 13.85 4.59 — 7.41 12/24/2003 12.89 — — 18.10 11/14/2011 Average Annual Total Returns (%) US Equity Portfolios 1 US Small Cap Growth Portfolio1,2 US Large Cap Growth Portfolio1,2 1 Non-US Equity Portfolios International Small Company Portfolio1 International Small Cap Value Portfolio International Small Cap Growth Portfolio 1,2 International Value Portfolio International Large Cap Growth Portfolio 1,2 Large Cap International Portfolio World ex US Value Portfolio 1 World ex US Targeted Value Portfolio 1,2 Global Portfolios Global Equity Portfolio21 Selectively Hedged Global Equity Portfolio 1 1. The net expense ratio applies to the indicated funds and takes into account a contractual management fee waiver and expense reimbursement agreement that currently is scheduled to remain in place through February 28, 2014. Please refer to the prospectus for more complete information on advisory fees, expense reimbursements, and other expenses. 2. Anticipated expenses for the full fiscal year. See “Appendix: Standardized Performance Data and Disclosures” for how to obtain complete information on performance, investment objectives, risks, advisory fees, and expenses of Dimensional’s funds. 13
    • A PPE N D I X STANDARDIZED PERFORMANCE DATA As of March 31, 2013 One Year Five Years 10 Years Since Inception Inception Date Tax-Managed US Small Cap Portfolio 18.37 7.41 11.61 8.96 12/15/1998    After Taxes on Distributions 18.10 7.24 8.75 8.75    After Taxes on Distributions and Sale of Fund Shares 10.81 5.84 7.58 7.58 Tax-Managed US Targeted Value Portfolio 22.30 7.55 12.46 10.01    After Taxes on Distributions 21.63 7.31 11.86 9.55    After Taxes on Distributions and Sale of Fund Shares 13.61 5.96 10.60 8.57 Tax-Managed US Equity Portfolio1 14.29 6.07 8.89 6.24    After Taxes on Distributions 13.85 5.73 8.61 5.99 Average Annual Total Returns (%) Tax Managed Portfolios2    After Taxes on Distributions and Sale of Fund Shares 12/11/1998 9/25/2001 8.55 4.77 7.34 5.09 Tax-Managed US Marketwide Value Portfolio 22.77 6.64 10.91 6.30    After Taxes on Distributions 22.34 6.33 5.99 5.99    After Taxes on Distributions and Sale of Fund Shares 13.34 5.21 5.14 5.14 Tax-Managed International Value Portfolio 6.12 -1.89 11.18 5.90    After Taxes on Distributions 5.77 -2.15 10.68 5.43    After Taxes on Distributions and Sale of Fund Shares 4.36 -1.04 9.91 5.15 TA US Core Equity 2 Portfolio1,2 17.35 7.37 — 3.64     After Taxes on Distributions 16.93 7.10 — 3.38     After Taxes on Distributions and Sale of Fund Shares 10.29 5.82 — 2.84 TA World ex US Core Equity Portfolio 8.50 0.75 — 1.06     After Taxes on Distributions 8.18 0.48 — 0.79     After Taxes on Distributions and Sale of Fund Shares 5.59 0.78 — 1.02 Real Estate Securities Portfolio1,3 14.24 6.81 12.05 10.49 1/5/1993 International Real Estate Securities Portfolio1 26.87 1.26 — -1.43 3/1/2007 Global Real Estate Securities Portfolio1,3 18.84 — — 4.60 6/4/2008 15.95 6.72 — 7.05 3/12/2008 9.94 -0.52 — -0.29 3/12/2008 16.77 6.50 — 2.81 10/1/2007 — — — 9.39 11/1/2012 3.17 2.48 — 8.21 8/31/2006 1,2 12/14/1998 4/16/1999 10/4/2007 3/6/2008 Real Estate Portfolios Social and Sustainability Portfolios US Sustainability Core 1 Portfolio1 International Sustainability Core 1 Portfolio 1 US Social Core Equity 2 Portfolio 1 International Social Core Equity Portfolio4 Emerging Markets Social Core Equity Portfolio1 1. The net expense ratio applies to the indicated funds and takes into account a contractual management fee waiver and expense reimbursement agreement that currently is scheduled to remain in place through February 28, 2014. Please refer to the prospectus for more complete information on advisory fees, expense reimbursements, and other expenses. 2. Assumed highest marginal tax rate in effect for capital gains and ordinary income. Income from funds managed for tax efficiency may be subject to an alternative minimum tax and/or any applicable state and local taxes.  3. The Management Fees and Total (Gross) Expense ratios have been adjusted to reflect the estimated management fee to be paid by the portfolios for the fiscal year ended October 31, 2013, as a result of a management fee decrease from 0.30% to 0.17% for the Real Estate Securities Portfolio, and 0.35% to 0.27% for the Global Real Estate Securities Portfolio, effective February 28, 2012. 4. Anticipated expenses for the first full fiscal year. See “Appendix: Standardized Performance Data and Disclosures” for how to obtain complete information on performance, investment objectives, risks, advisory fees, and expenses of Dimensional’s funds. 14
    • A PPE N D I X STANDARDIZED PERFORMANCE DATA As of March 31, 2013 One Year Five Years 10 Years Since Inception Inception Date 0.70 1.58 2.29 5.29 7/25/1983 Two-Year Global Fixed Income Portfolio 0.83 1.84 2.34 3.79 2/9/1996 Short-Term Government Portfolio 1.36 3.48 3.33 5.76 6/1/1987 Five-Year Global Fixed Income Portfolio 3.65 4.43 3.86 6.05 11/6/1990 Intermediate Government Fixed Income Portfolio 4.20 5.45 5.16 7.13 10/19/1990 Inflation-Protected Securities Portfolio 6.24 6.19 — 7.43 9/18/2006 Short-Term Municipal Bond Portfolio1 0.68 2.05 2.13 2.18 8/20/2002 Intermediate-Term Municipal Bond Portfolio1 2.72 — — 1.19 3/1/2012 California Short-Term Municipal Bond Portfolio1 0.85 2.24 — 2.44 4/2/2007 California Intermediate-Term Municipal Bond Portfolio1 3.44 — — 3.44 11/29/2011 Selectively Hedged Global Fixed Income Portfolio 2.19 1.70 — 1.96 1/9/2008 World ex US Government Fixed Income Portfolio 6.57 — — 6.27 12/6/2011 Short-Term Extended Quality Portfolio 2.61 — — 4.84 3/4/2009 Intermediate-Term Extended Quality Portfolio1 6.99 — — 6.34 7/20/2010 Investment Grade Portfolio1 4.76 — — 6.63 3/7/2011 -1.39 — — -3.30 11/9/2010 Average Annual Total Returns (%) Fixed Income Portfolios One-Year Fixed Income Portfolio 1,2 1 1 1 1 Commodities Portfolio Commodity Strategy Portfolio1 1. The net expense ratio applies to the indicated funds and takes into account a contractual management fee waiver and expense reimbursement agreement that currently is scheduled to remain in place through February 28, 2014. Please refer to the prospectus for more complete information on risks, advisory fees, expense reimbursements, and other expenses. 2. Formerly the Five-Year Government Portfolio. See “Appendix: Standardized Performance Data and Disclosures” for how to obtain complete information on performance, investment objectives, risks, advisory fees, and expenses of Dimensional’s funds. 15
    • A PPE N D I X DISCLOSURES Performance data shown represents past performance. Past performance is no guarantee of future results, and current performance may be higher or lower than the performance shown. The investment return and principal value of an investment will fluctuate so that an investor’s shares, when redeemed, may be worth more or less than their original cost. To obtain performance data current to the most recent month end, access our website at www. dimensional.com. Consider the investment objectives, risks, and charges and expenses of the Dimensional funds carefully before investing. For this and other information about the Dimensional funds, please read the prospectus carefully before investing. Prospectuses are available by calling Dimensional Fund Advisors collect at (512) 306–7400 or at www.dimensional.com. Dimensional funds are distributed by DFA Securities LLC. Dimensional Fund Advisors LP is an investment advisor registered with the Securities and Exchange Commission. Risks include loss of principal and fluctuating value. Investment value will fluctuate, and shares, when redeemed, may be worth more or less than original cost. Small and micro cap securities are subject to greater volatility than those in other asset categories. International and emerging markets investing involves special risks such as currency fluctuation and political instability. Investing in emerging markets may accentuate these risks. Sector-specific investments focus on a specific segment of the market, which can increase investment risks. Fixed income securities are subject to increased loss of principal during periods of rising interest rates. Fixed-income investments are subject to various other risks, including changes in credit quality, liquidity, prepayments, call risk and other factors. Municipal securities are subject to the risks of adverse economic and regulatory changes in their issuing states. Real estate investment risks include changes in real estate values and property taxes, interest rates, cash flow of underlying real estate assets, supply and demand, and the management skill and creditworthiness of the issuer. Sustainability funds use environmental and social screens that may limit investment opportunities for the fund. Commodities include increased risks, such as political, economic, and currency instability, and may not be suitable for all investors.  The Portfolio may be more volatile than a diversified fund because the Portfolio invests in a smaller number of issuers and commodity sectors. The fund prospectuses contain more information about investment risks. BRO-QIR