Russell Research. Alpha forecasts: Liquid assets Australia

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1 Russell Research By: Leola Ross, Ph.D., CFA, Senior Investment Strategist January 2013 Vivek Sondhi, Ph.D., Senior Research Analyst Steve Murray, Ph.D., CFA, Director, Asset Allocation Strategies Alpha forecasts: Liquid assets Australia Integrating active management into Russell s strategic planning assumptions Forecasts are fundamental building blocks in investment management. Russell has long had a well-established set of beta assumptions calculated with a globally consistent and rigorous methodology. 1 In this paper, we move toward completing our suite of forecasts by adding excess returns, tracking error and correlation assumptions for all liquid asset classes. The need for integrated forecasts is clear given Russell s strong advocacy of active management and our offering of several asset classes wherein the excess returns associated with active management is a critical component of our clients expected total returns. Active management may represent anywhere from 15% to 60% of the total returns expected from an asset class; therefore, forecasting the potential returns from active management is a meaningful endeavor. In this paper we offer, for all asset classes that have monthly pricing and returns data, a rigorous, robust, logically consistent alpha forecast methodology. These forecasts will help Russell s clients to make more accurate and appropriate investment decisions. Introduction Primary factors in forecasting alpha are the construction of reasonable assumptions about active manager skill and an ability to identify strong skill. In this note we implicitly assume that an organization such as Russell can identify skilled fund managers with some degree of success. When it succeeds in doing so, a manager of managers can deliver to its investor clients excess returns with lower tracking error than a single manager is likely to deliver. The skill we are capturing is from the fund manager; Russell often identifies it ex ante via the! The beta forecasts may be made available to Russell clients. Russell Investments // Alpha forecasts: Liquid assets / p 1

2 evaluation of disaggregated ranks in such areas as securities selection, sector/country/strategy tilt, research, transaction execution, multi-asset portfolio construction, risk management and business practice. In this note we focus our attention on forecasting a modest or typical expectation of well-selected active managers. Certain active products may have different returns expectations or targets based on the products mandates. In building a multi-asset portfolio, returns are only a part of the story. One must also consider the volatility of those returns and how they correlate to all other components of the portfolio. Russell currently has a full set of forecasted returns, volatilities and correlations for the beta (or passive) returns. To integrate forecasted skill (for achieving excess returns or alpha), we forecast excess returns, tracking error and correlations with all other excess returns and all other betas. The end result of this exercise is a placemat essentially, a full-page table displaying all of these forecasts. This paper is the first of a two-paper series documenting Russell s alpha expectations for different asset classes considered in Russell s multi-asset model portfolio framework. By fully integrating alpha risk and return forecasts with the more established beta forecasts, we enhance portfolio modelling through a consistent basis for treatment of all asset classes. Herein we set out our alpha forecasts for liquid assets. The forthcoming part two will establish forecasts for unlisted assets. Methodology EXCESS RETURNS AND TRACKING ERROR ASSUMPTIONS To create our forecasts of excess returns, we utilize Russell s manager universes and hedge fund research (HFR) universes. 2 The first key assumption of our forecasts is that multiple active managers will be selected for a portfolio. The second key assumption is that our manager researchers can identify skilled active management with some degree of accuracy. The accuracy we implicitly assume is a modest 60%. What this means is that 60% of the managers selected will come from the top half of the distribution of managers, and 40% will come from the bottom half. 3 The third key assumption we make is that it takes many draws from the universe to get useful information. For example, in the case of a single universe fund simulation, such as U.S. bonds, we sample four-manager portfolios from the U.S. Active Core Fixed universe 5,000 times to create 5,000 simulated portfolios. In the case of a multiple universe fund simulation, such as U.S. equities, we sample three managers from the Russell U.S. Large Cap Market Oriented Equity universe, three from the Russell U.S. Large Cap Value Equity universe and three from the Russell U.S. Large Cap Growth Equity universe. Again, we sample 5,000 times to create 5,000 simulated portfolios each containing nine managers in total. The number of managers in each portfolio is meant to correspond to a typical number of managers in Russell funds in the same asset class. We sample from the universes to simulate the portfolios for each five-year period noted in Table 1. 4 " 3 # HFR universes are used for hedge fund forecasts. The 60% accuracy rate is an average across the simulated portfolios. The resulting excess return we observe with a 60% accuracy rate is modest and reasonable relative to what Russell has experienced in its manager rankings. Only funds that are in the universe for the entire five years may be included in a simulated portfolio. Russell Investments // Alpha forecasts: Liquid assets / p 2

3 Table 1. Five-year periods with and without GFC 5 Sample period Five-year data periods including GFC Sample period Five-year data periods excluding GFC 1 July 2000 through June July 2000 through June July 2001 through June July 2001 through June July 2002 through June July 2002 through June July 2003 through June July 2003 through June July 2004 through June x GFC July 2004 through June July 2005 through June x GFC July 2005 through June 2008, July 2009 through June July 2006 through June x GFC July 2006 through June 2008, July 2009 through June 2011 In forecasting potential excess returns and tracking error, we initially estimate values for broadly defined asset categories that is, we assume all equity categories have the same excess returns and tracking error levels; we assume that all fixed income categories have the same excess returns and tracking error levels, and so forth. We call these initial broad asset class forecasts defaults. These defaults are shown in Table 2. To get a default forecast of potential excess returns and tracking error for a broad asset class, we segregate the data into four buckets: Fixed, Equity, Real Assets and Hedge Funds. For each broad asset class, we get a sense of the central tendency by looking at the collection of median simulations across all sub-asset classes within the broad asset class. These defaults allow us to understand which of the underlying asset classes may exhibit different behaviors than the broad category. Table 2. Default forecasts by broad asset class Excess returns assumptions Tracking error assumptions Fixed 0.50% 1.50% Equity 1.50% 3.50% Real Asset 1.50% 3.75% Hedge Funds 4.00% 3.25% CORRELATIONS Correlations are a critical component in excess-returns forecasts. Just as all betas have some degree of co-movement, it is quite possible that an alpha will exhibit some correlation to its own beta, to some other beta, or even to some other alpha. Therefore, to complete the forecasts, we must be prepared to comment on how those forecasted excess returns may interact with everything else in the portfolio. Similar to our methodology for evaluating excess returns and tracking error, we estimate 5,000 correlations for all sub-asset-class pairs. Conventional wisdom and common practice assume that excess returns are not correlated with each other or with underlying beta returns series. It is useful to test the robustness of this wisdom to ensure that it continues to be a reasonable guide, or whether some modification is needed. Conventional wisdom and common practice assume that excess returns are not correlated with each other or with underlying beta returns series. It is useful to test the robustness of this wisdom to ensure that it continues to be a reasonable guide, or whether some modification is needed. $ GFC is an abbreviation for Financial Crisis, the period from approximately July 2008 through June 2009, during which global markets suffered significant losses. We compare results with and without this time period to understand how strongly this unprecedented environment may have influenced results. Russell Investments // Alpha forecasts: Liquid assets / p 3

4 Therefore, we also start with a null hypothesis that the correlation of an alpha to any other alpha or to any beta is zero. We structure our hypothesis testing at two levels. In the first level, we test each combination of broad asset classes as shown in Table 3. Across all pairs within a broad asset class, we look at the average of the median correlations and compare them with steps away from zero of For example, the average of the median correlations for fixed income alpha (FI Alpha) to fixed income beta (FI Beta) is Because this median is positive but not greater than 0.2, we consider our default assumption for FI Alpha to FI Beta to be zero. By contrast, in the case of fixed income alpha to equity beta (EQ Beta), we find that the average of the median correlations is Because this number surpasses the thresholds of 0.2 and 0.4 but not 0.6, we use a default assumption of 0.4. We report the default correlation assumption for each asset class combination in Table 3. Table 3. Simulated correlations between asset class alphas and betas 7 FI Alpha EQ Alpha RA Alpha HF Alpha FI Alpha EQ Alpha RA Alpha HF Alpha FI Beta EQ Beta RA Beta HF Beta Fixed income (FI); Equity (EQ); Real assets (RA); Hedge fund (HF) Note that only three broad asset category pairs meet the threshold of 0.4, three meet the 0.2 threshold, and three also meet the -0.2 threshold. The three category pairs meeting the 0.4 threshold involve fixed income alpha, as does one of the pairs meeting the 0.2 threshold. We considered why it might be the case that fixed income alpha is typically more correlated with itself, equity beta and real asset beta than with any other type of alpha. We hypothesize that much fixed income alpha is associated with credit bets, which will be reflected in both equity and some real asset categories. With broad asset class default excess returns, tracking error and correlations established, we are now ready to exhibit the alpha placemats, which show the details for each sub-asset class. (Our analysis for unlisted investments has not yet been completed, but will be added to these displays once it becomes available.) 6 7 We select the 0.2 step because we want to avoid creating a false sense of precision. These numbers are estimates, not exact expectations. For example, consider the FI Alpha/FI Alpha forecasted correlation of 0.4. This particular correlation forecast indicates that when evaluating the alphas from pairs of independently selected portfolios (from our simulations) we fail to reject that the alphas are correlated at the 0.4 level. As a second contrasting example, consider the FI Alpha/FI Beta forecasted correlation of 0.0. This forecast indicates that when evaluating FI Alpha to the FI Beta (a market index), we reject any non-zero correlation. Russell Investments // Alpha forecasts: Liquid assets / p 4

5 Placemats: Australia - Russell Forecasts for Excess Returns, Tracking Error, and Alpha-Beta Correlations Alpha sub-asset class across the top, Beta sub-asset class down the left side Aust Fixed Fixed Equity Property Other Cash Fixed Aust Equity Listed Property - H Unlisted Property Australia Unlisted Property Private Equity Commodities Listed Infrastructure - H Non Dir. Hedge Fund Excess Return 0.50% 0.50% 1.50% 1.75% 1.75% 1.50% 2.50% 1.50% 4.00% Volatility 1.50% 1.50% 3.00% 3.50% 3.50% 4.00% 4.00% 5.00% 3.25% Correlations Fixed Aust Fixed Cash Fixed Equity Property uity Listed Property - H Unlisted Property Australia Unlisted Property Private Equity Other Commodities Listed Infrastructure - H Non Dir. Hedge Fund Please note that all information shown is based on assumptions. The long-term expected excess returns employ proprietary projections of the active returns potential of each asset class. We estimate the long-term excess returns of an asset class or strategy by analyzing current market conditions and historical market trends. It is likely that actual returns will vary considerably from these assumptions, even for a number of years. References to future returns for either asset allocation strategies or asset classes are not promises or even estimates of actual returns a client portfolio may achieve. Asset classes are broad general categories which may or may not correspond well to specific products. For example, Russell's assumptions for hedge funds are based on nondirectional hedge funds and may not reflect important characteristics of directional hedge funds or other products that may fit under the broad label "hedge funds." Additional information regarding Russell's basis for these assumptions is available upon request. Opinions and estimates offered constitute our judgment and are subject to change without notice, as are statements of financial market trends, which are based on current market conditions. This material is not intended as an offer or solicitation for the purchase or sale of any financial instrument. The views and strategies described may not be suitable for all investors. Nothing contained in this material is intended to constitute legal, tax, securities or investment advice, nor an opinion regarding the appropriateness of any investment, nor a solicitation of any type. The general information contained in this publication should not be acted upon without obtaining specific legal, tax and investment advice from a licensed professional. Modelling as at December 2011 Russell Investments // Alpha forecasts: Liquid assets / p 5

6 Placemats: Australia - Russell Forecasts for Excess Returns, Tracking Error, and Alpha-Alpha Correlations Alpha sub-asset class across the top, Beta sub-asset class down the left side Aust Fixed Fixed Equity Property Other Cash Fixed Aust Equity Listed Property - H Unlisted Property Australia Unlisted Property Private Equity Commodities Listed Infrastructure - H Non Dir. Hedge Fund Excess Return 0.50% 0.50% 1.50% 1.75% 1.75% 1.50% 2.50% 1.50% 4.00% Volatility 1.50% 1.50% 3.00% 3.50% 3.50% 4.00% 4.00% 5.00% 3.25% Correlations Fixed Aust Fixed 1.00 Cash 1.00 Fixed Equity Property uity Listed Property - H Unlisted Property 1.00 Australia Unlisted Property 1.00 Private Equity 1.00 Other Commodities Listed Infrastructure - H Non Dir. Hedge Fund Please note that all information shown is based on assumptions. The long-term expected excess returns employ proprietary projections of the active returns potential of each asset class. We estimate the long-term excess returns of an asset class or strategy by analyzing current market conditions and historical market trends. It is likely that actual returns will vary considerably from these assumptions, even for a number of years. References to future returns for either asset allocation strategies or asset classes are not promises or even estimates of actual returns a client portfolio may achieve. Asset classes are broad general categories which may or may not correspond well to specific products. For example, Russell's assumptions for hedge funds are based on nondirectional hedge funds and may not reflect important characteristics of directional hedge funds or other products that may fit under the broad label "hedge funds." Additional information regarding Russell's basis for these assumptions is available upon request. Opinions and estimates offered constitute our judgment and are subject to change without notice, as are statements of financial market trends, which are based on current market conditions. This material is not intended as an offer or solicitation for the purchase or sale of any financial instrument. The views and strategies described may not be suitable for all investors. Nothing contained in this material is intended to constitute legal, tax, securities or investment advice, nor an opinion regarding the appropriateness of any investment, nor a solicitation of any type. The general information contained in this publication should not be acted upon without obtaining specific legal, tax and investment advice from a licensed professional. Modelling as at December 2011 Russell Investments // Alpha forecasts: Liquid assets / p 6

7 A Case study CASE STUDY: ADDING ALTERNATIVES TO A PORTFOLIO Now that we have established our forecasts for potential excess returns, tracking error and correlations, we turn our attention to seeing how they can enhance our ability to forecast the returns and volatility expectations of a typical portfolio both with and without alternative investments. We define our typical portfolio using the taxonomy of our Strategic Planning Assumptions 8 30% Australia Equity, 15% -Australia Equity, 15% -Australia, 20% Australia Composite Fixed Interest, 20% Fixed Interest with a 10-year planning horizon. We add in the excess returns, tracking error and correlations forecasts. We show these particular forecasts in Table 4, below. We show the expected returns and volatilities of the beta-only (or no skill ) scenario within this typical portfolio as Case 1 in Table 5 by using only the information in the Strategic Planning Assumptions. Our Case 2 then includes these assets along with 10% Real Assets (4% Commodities, 3% Listed Infrastructure and 3% Listed Real Estate) and 10% Low Direction Hedge Funds by displacing the three traditional asset classes proportionately, again with beta-only forecasts. In Case 3, we go back to the traditional assets, now with both beta and alpha forecasts for excess returns, tracking error and correlations. Finally, in Case 4, we consider 80% traditional assets along with 10% Real Assets and 10% Low Direction Hedge Funds, with beta and alpha forecasts. In examining the cases in Table 5, we see that adding alternatives to traditional assets without assuming an alpha component (Case 2) sends a mixed message. While alternatives reduce expected volatility, they also appear to be reducing returns expectations (in the comparison of Case 2 to Case 1). However, when we consider the excess returns associated with all asset classes, we see a different story. By including the alpha, we see that the expected returns are modestly enhanced, while the expected volatility is diminished (Case 4 vs. Case 2). This second depiction is a potentially more accurate view of the expected role of alternatives in portfolios. 8 Russell strategic forecasting publications are available upon request. Russell Investments // Alpha forecasts: Liquid assets / p 7

8 Table 4. Strategic planning assumptions for beta returns and volatilities, excess returns, tracking error, and correlations for case study 9 Betas Aust Fixed Fixed Commodities Listed Infrastructure G-REITs HFOF Low Directional 10 Year Arithmetic Return (Beta) Year Arithmetic Volatility (Beta) Correlation Matrix Betas Aust Fixed Fixed Commodities Listed Infrastructure G-REITs HFOF Low Directional Alphas Aust Fixed Fixed Commodities Listed Infrastructure G-REITs HFOF Low Directional All numbers are assumed gross of fees except those for HFoF Low D. Beta forecasts are as of December 31, Russell Investments // Alpha forecasts: Liquid assets / p 8

9 Table 4 continued (extends horizontally from page 8) Alphas Aust Fixed Fixed Commodities Listed Infrastructure G-REITs HFOF Low Directional 10 Year Arithmetic Return (Alpha) Year Arithmetic Volatility (Alpha) Correlation Matrix Betas Aust Fixed Fixed Commodities Listed Infrastructure G-REITs HFOF Low Directional Alphas Aust Fixed Fixed Commodities Listed Infrastructure G-REITs HFOF Low Directional Please note that all information shown is based on assumptions. The long-term expected excess returns employ proprietary projections of the active returns potential of each asset class. We estimate the long-term excess returns of an asset class or strategy by analyzing current market conditions and historical market trends. It is likely that actual returns will vary considerably from these assumptions, even for a number of years. References to future returns for either asset allocation strategies or asset classes are not promises or even estimates of actual returns a client portfolio may achieve. Asset classes are broad general categories which may or may not correspond well to specific products. For example, Russell's assumptions for hedge funds are based on nondirectional hedge funds and may not reflect important characteristics of directional hedge funds or other products that may fit under the broad label "hedge funds." Additional information regarding Russell's basis for these assumptions is available upon request. Opinions and estimates offered constitute our judgment and are subject to change without notice, as are statements of financial market trends, which are based on current market conditions. This material is not intended as an offer or solicitation for the purchase or sale of any financial instrument. The views and strategies described may not be suitable for all investors. Nothing contained in this material is intended to constitute legal, tax, securities or investment advice, nor an opinion regarding the appropriateness of any investment, nor a solicitation of any type. The general information contained in this publication should not be acted upon without obtaining specific legal, tax and investment advice from a licensed professional. Russell Investments // Alpha forecasts: Liquid assets / p 9

10 Table 5. Case study results Return assumption (%) Volatility Assumption Case 1 Traditional Assets Only No Skill Case 2 80% Traditional / 20% Alternatives No Skill Case 3 Traditional Assets Only with Skill Case 4 80% Traditional / 20% Alternatives with Skill Summary and conclusions By explicitly forecasting betas and alphas, their volatilities and their correlations, the potential impact of active management is made clear. In the case of asset classes where active management is particularly important, such as real assets and hedge funds, understanding the impact of excess returns is essential to making good asset allocation decisions. This very important research outcome is illustrated by our case study. Without considering the skill component of active management, it is impossible for investors to properly evaluate the role of real assets and hedge funds in a portfolio. By forecasting and including excess returns, the importance of these asset classes is made more clear. Real assets and hedge funds can be both risk-reducing and, modestly, returns-enhancing additions to the overall portfolio. Our methodology is based on rich and deep universes from Russell and HFR. We then apply a sampling technique to create returns distributions based on Russell s knowledge of how portfolios are constructed, and apply statistical testing to develop modest, realistic expectations for excess returns, tracking error and correlations. The end result is a nearly complete system of forecasts with a full correlation matrix underlying it. Part II of this research will complete the forecasts by including unlisted asset classes. Without considering the skill component of active management, it is impossible for investors to properly evaluate the role of real assets and hedge funds in a portfolio. Acknowledgements This work was greatly expedited by the programming assistance of our colleagues at Amba Research in particular, Kayanthini Kandasamy, the principal programmer who worked tirelessly on this project. We are also grateful for advice and comments from Adam Goff, Jon Eggins, John Forrest, Rob Balkema, Jim Gannon, Mike Sylvanus, David Phillips, Vic Leverett, David Tenney, Patrick Rowland, Evgenia Gvozdeva, Graham Harman and Anne Lee. Finally, we acknowledge our intern David Bahr, who assisted in the creation of our placemats. Russell Investments // Alpha forecasts: Liquid assets / p 10

11 RELATED READING Christopherson, Jon, Zhuanxin Ding and Paul Greenwood (July 2001), The Perils of Success: The Impact of Asset Growth on Small-Capitalization Investment Manager Performance, Russell Research Commentary. Fjelstad, Mary, Steven Fox, Mark Paris and Michael Ruff (May 2004), The Role of High Yield and Emerging Market Debt for a U.S. Investor, Russell Research Commentary. Goetzmann, William, and Roger G. Ibbotson (1999), Offshore Hedge Funds: Survival and Performance, , Journal of Business, Vol. 72, Goodwin, Tom (June 2007), The Equity Risk Premium for Capital Markets Forecasts, Russell Viewpoint. Goodwin, Tom, and James Gannon (April 2009), Yield Curve Election for Valuation of PPA Funding Target Liabilities, Russell Practice Note. Ilkiw, John, and Steve Murray (July 2001), Establishing Higher Confidence Policy Exposures to Private Real Estate, Private Equity and Hedge Funds Using Two-Stage Asset Allocation, Russell Research Commentary. Russell strategic forecasting publications are available upon request. Russell Investments // Alpha forecasts: Liquid assets / p 11

12 For more information: Call Russell at or visit Important information Issued by Russell Investment Management Ltd ABN , AFS License (RIM). This document provides general information only and has not prepared having regard to your objectives, financial situation or needs. Before making an investment decision, you need to consider whether this information is appropriate to your objectives, financial situation or needs. This information has been compiled from sources considered to be reliable, but is not guaranteed. Some of the performance data shown does not take into account fees, charges or taxes and is not in any way an indicator of the net return to you as an investor. Some of the examples used in this report are based on hypothetical assumptions and have been included for illustrative purposes only. Any projections are based on reasonable grounds and have been determined by RIM to be relevant and reliable. However, these projections are not exact forecasts. Hypothetical back-tested performance is shown for illustrative purposes only and does not represent any actual performance. RIM does not represent that the hypothetical returns would be similar to actual performance had RIM actually managed a portfolio in this manner. Hypothetical, back-tested or simulated performances have many inherent limitations. Investors should not assume that they will have an investment experience similar to the hypothetical, back-tested or simulated performance shown. No representation is made that any portfolio will or is likely to achieve outcomes similar to those shown. In fact, there are frequently sharp differences between hypothetical, back-tested and simulated performance results and actual results subsequently achieved. This document is for WHOLESALE USE ONLY and is not intended to be viewed by retail investors. Copyright 2013 Russell Investments. All rights reserved. This material is proprietary and may not be reproduced, transferred, or distributed in any form without prior written permission from Russell Investments. First used: April 2012 MKT/5095/0113 R_RPT_RES_AlphaLiquid_V1F_1301 Russell Investments // Alpha forecasts: Liquid assets / p 12