A Factor Approach to Smart Beta Development in Fixed Income

THE JOURNAL OF SUMMER 2015 VOLUME 6 NUMBER 1 www.iijii.com ETFs, ETPs & Indexing The Voices of Influence iijournals.com A Factor Approach to Smart Beta Development in Fixed Income ARNE STAAL, MARCO CORSI, SARA SHORES, AND CHRIS WOIDA

A Factor Approach to Smart Beta Development in Fixed Income ARNE STAAL, MARCO CORSI, SARA SHORES, AND CHRIS WOIDA ARNE STAAL is a managing director at BlackRock in London, U.K. arne.staal@blackrock.com MARCO CORSI is a director at BlackRock in London, U.K. marco.corsi@blackrock.com SARA SHORES is a managing director at BlackRock in San Francisco, CA. sara.shores@blackrock.com CHRIS WOIDA is a director at BlackRock in San Francisco, CA. chris.woida@blackrock.com As a bull market in bonds that lasted for more than three decades comes to an end, investors increasingly question the traditional means of accessing fixed income markets. Benchmark indexing has guided investments ever since the creation of the first bond indices in 1926 by Standard and Poor s. Existing benchmark indices are not without their faults however, as they may be impacted by diversification, liquidity, and transparency issues. These shortcomings have been of little concern while bond markets continued to deliver high risk-adjusted returns in an environment of steadily falling interest rates. In the last few years however, the prospect of rising rates in a low yield environment has sparked interest in new approaches to bond investing. At the same time, the notion of passively managed portfolios that move away from cap weighting often called smart beta has gained a strong foothold among equity investors. In the pages that follow, we examine how best to extend this way of thinking to fixed income. INEFFICIENCIES IN FIXED INCOME INDICES Since market value of debt is determined by both bond prices and notional amount outstanding, a cap-weighted index emphasizes positions in highly indebted issuers and potentially overvalued bonds, an investment approach that is in some ways counterintuitive and counterproductive. Debt issuance in such indices is highly concentrated in a relatively small number of market participants. Exhibits 1 and 2 below illustrate the lack of diversification in bond indices using the Barclays Global Treasury Index as an example. As of April 2015, treasury bonds from the United States and Japan together comprise 55% of the market value of an index that represents the sovereign debt of 38 countries. Even more strikingly, those same two countries represent almost two-thirds of the total risk in the index. A very similar observation can be made for corporate credit indices. Exhibit 3 shows that 10% of the issuers in the Barclays U.S. Corporate Index represent more than 55% of both the market value and the total risk of the index. Market-cap-weighted equity indices suffer from similar concentration issues, but arguably the implications for fixed income indices go beyond limited diversification. While market value of any instrument reflects the discounted sum of expected cash flows, and therefore is in essence forward looking, notional amount of debt outstanding is entirely backward looking in nature. Unlike equity indices, fixed income index composition often reflects historical issuance dynamics to a larger degree than A FACTOR APPROACH TO SMART BETA DEVELOPMENT IN FIXED INCOME SUMMER 2015

E XHIBIT 1 Barclays Global Treasury Index Country Weights E XHIBIT 2 Barclays Global Treasury Index Country Risk Contribution Source: Barclays as of April 2015. Source: Barclays as of April 2015. The index risk is represented by its ex-ante monthly volatility as of that date. E XHIBIT 3 Barclays U.S. Corporate Index Issuer Weights and Risk Contribution Source: Barclays as of April 2015 The issuers represented in the Barclays U.S. Corporate Index (about 2100 issuers) are partitioned into deciles based on weight and risk contribution. For each decile, the charts display its cumulative weight (left chart) and risk contribution (right chart). Notice that the partition into deciles is done by ranking the issuers according to their weight (left chart) or risk contribution (right chart); the list created in this way is then partitioned into 10 consecutive groups (each one a decile) containing an equal number of issuers. differences in forward looking valuations. While the primary users of benchmark indices, bond investors, aim to achieve high returns, issuer dynamics are based on a desire to minimize cost of capital by bond issuers. The result is that the high weights of dominant issuers can go hand in hand with relatively unattractive characteristics such as relatively low fundamental quality and yields. To illustrate, Exhibit 4 highlights the lack of relationship between benchmark weight and yield to maturity. Other problems stem from the market structure in fixed income. Benchmark indices aim to represent returns on markets for the average investor; in the end, the sum total of outstanding instruments has to add up to the sum total of investor positions. Unfortunately, unlike most equity benchmarks, broad fixed income indices are not directly investable because of liquidity challenges and limited price transparency. Many instruments contained in such indices are held in buy-to-hold SUMMER 2015 THE JOURNAL OF INDEX INVESTING

E XHIBIT 4 Weights vs. Yields in the Barclays Global Treasury Index Source: Barclays as of April 2015 The chart displays the cumulative weight and yield to maturity for each country included into the Barclays Global Treasury Index. portfolios, are traded in low volume, and most are traded over-the-counter rather than on exchanges. This makes traditional fixed income indices challenging to replicate in investor portfolios and imprecise tools for the purpose of measuring actual investment outcomes. FROM FACTORS TO SMART BETA The increased concern about the shortcomings of passive fixed income strategies benchmarked to traditional indices coincides with a renewed focus across the investment industry on understanding the drivers of return and risk across asset classes. During the credit crisis of 2008, many actively managed investments experienced steep losses in line with risky asset classes, bringing increased scrutiny to portfolio allocations. Confronted by this lack of diversification across active and passive investments, researchers and market participants sought to analyze active returns and understand true drivers of performance. The results solidified the understanding that a large part of active returns can be explained by a set of well-established, systematic investment strategies (see Ang et al. [2009] for a study on the performance of the Norwegian Government Pension Fund). Well-known examples include portfolio managers exploiting FX carry (long high-yielding currencies, short low-yielding currencies), active bond managers extending the duration of their portfolios beyond the benchmark (accessing the term premium in fixed income), credit managers overweighting high yield bonds (accessing the credit carry risk premium), equity managers increasing the weight of value stocks in their portfolios (earning the equity value premium), commodity funds exploiting roll congestion strategies (long deferred contracts, short front contracts), and hedge funds harvesting the short volatility premium (by selling delta-hedged options). To illustrate the extent of these systematic exposures in actively managed fixed income portfolios, we perform a principal components analysis 1 on the excess returns of a universe of 655 active European bond managers. The results of this analysis are shown in Exhibit 5; the first two factors generated by the principal component analysis explain about 75% of the A FACTOR APPROACH TO SMART BETA DEVELOPMENT IN FIXED INCOME SUMMER 2015

E XHIBIT 5 Explaining the Risk of a Universe of Active European Bond Managers Source: Lipper database as of February 2015. The principal component analysis is run over monthly returns since January 2004 to February 2015. Funds with less than five years history have been excluded from the analysis (the universe includes EUR denominated active funds (excluding absolute returns) classified as bond funds by Lipper). active risk 2 of such managers, suggesting that most of their excess returns are driven by common exposures in their portfolios. Not surprisingly, we calculate that the first factor is very highly correlated with longer term interest rates, indicating yield extension of active portfolios beyond the benchmark in an attempt to generate additional term premium return. The second factor appears related to the slope of the interest rate curve while the third seems to capture spread exposure. Kahn and Lemmon [2014] suggest similar results for actively managed U.S. bond portfolios through direct regression of active performance on bond and credit factors. The interest and credit rate exposures identified above are examples of factors; commonality in returns across instruments with similar underlying exposures to the forces that determine investment outcomes. Both passive and active investments are exposed to these forces. In many cases, factors capture risk premiums (and therefore generate excess returns by providing exposure against an identified and priced economic risk), but they can also represent behavioral phenomena or pricing pressures driven by market structure. In the same manner that long-only market-capitalization-weighted investments in risky assets capture the overall market risk premium (or beta ), these strategies capture alternative sources of returns available in the markets. Indices that deviate from market-capitalization weighting to explicitly capture exposure to one or more of these factors can be thought of as providing access to a form of alternative, or smart, beta. The concept of factor investing and smart beta has not been widely adopted in every asset class. Equity investors have embraced non-cap-weighted index strategies and factor investing to the greatest extent. At least since the publication of the seminal paper on common factors in equity returns by Fama and French [1992, 1993] and Carhart [1997], equity investors have been aware of the importance of systematic factors such as valuation (as measured, for example, by book-to-market ratios), quality (as measured, for example, by the stability of earnings and dividend policies), size (as measured by market capitalization), momentum (as measured by relative past price performance), or risk anomalies (as measured by the outperformance of low-risk portfolios, compared to high-risk portfolios). While the existence of these factors has been widely recognized for many years (and often provide the building blocks for actively managed strategies), only recently have investors sought to achieve exposure to these factors through a more passive and rules-based approach. Fixed income has always been a highly analytical and structured asset class, but investors have not yet widely started to investigate factor-based approaches to bond investing with particular investment objectives. The considerable inefficiencies in benchmark fixed income indices and the relative lack of transparent pricing of bond instruments suggest there is a myriad of opportunities for better passive fixed income solutions. By identifying the drivers of risk and return for the asset class, we can design strategies with clear objectives to meet specific outcomes and seek better risk-adjusted returns. SUMMER 2015 THE JOURNAL OF INDEX INVESTING

WHAT DRIVES FIXED INCOME RETURNS? To evaluate smart beta approaches in fixed income, it is important to have a clear understanding of what drives fixed income returns. Investment processes in both equity and fixed income have relied on factor representations of risk for many decades, but only since the credit crisis has explicit outcome-oriented factor investing become of broad interest to institutional investors. The underlying economic forces that result in these factors are very different depending on the instruments under consideration: equity factors are mostly understood through deep empirical analysis of pricing behavior in a long history of academic investigation and fixed income factors are best understood starting from more formal valuation approaches. Merton [1984] showed that equity can be viewed as a long call option on firm asset value and bonds can be viewed as a short put option on firm asset value. This elegant argument highlights that the essence of fixed income investing is lending money in return for promised cash flows, coupons and principal; both the potential loss and gains are limited. This is very different from equity investing. The essence of equity investing is a promise of unknown cash flows with unlimited upside potential. The main implication is that fixed income investing in many ways is much more structured than equity investing, it is easier to value fixed income instruments relative to each other with precision because the impact of idiosyncratic differences between firms has relatively less impact on valuation. To illustrate this insight, we decompose the risk of individual constituents of two indices representative of the U.S. equity and IG corporate debt market: the S&P Composite 1500 Index and the Barclays U.S.-only Corporate Index. For each constituent (bond or stock), we calculate the ratio of idiosyncratic (i.e., company specific) risk relative to systematic (i.e., factor driven) risk. Exhibit 6 depicts the distribution of this ratio across index constituents and shows that it is less disperse and more concentrated around lower levels for bonds than for equities. The average idiosyncratic risk for equities as a percentage of systematic risk is about two times larger than for corporate bonds, or in other words, a much larger percentage of bond risk is explained by factors. The two main questions in fixed income valuation are: 1. What are the expected bond cash flows and how do markets value future payments in the absence of default risk? 2. What is the risk that one or more of the expected bond cash flows will not materialize and how should I be compensated for taking that risk? The answers to these questions determine the two main types of factors in fixed income markets: duration risk (the yields represented by a treasury term structure) and credit spreads (the additional yield required to hold a risky bond rather than a government guaranteed bond). Depending on the complexity of the instrument, convexity and optionality might be further systematic E XHIBIT 6 Distribution of the Relative Idiosyncratic Risk for U.S. Equities and IG Corporate Bonds Source: Barclays, Bloomberg, and Blackrock as of April 2015. The chart displays the density function of the ratio idiosyncratic risk over systematic risk across the constituents of the S&P 1500 Composite Equity Index and the Barclays U.S. Corporate Index). A FACTOR APPROACH TO SMART BETA DEVELOPMENT IN FIXED INCOME SUMMER 2015

pricing factors, but they tend to be of lesser importance in broad portfolios. Market structure phenomena such as IG versus HY segmentation underpin other systematic factors such as the fallen angels effect, but while interesting as isolated strategies, they tend to be of lesser importance in high-capacity multi-factor approaches. Exhibit 7 illustrates factor decomposition of fixed income returns. Both academic research and industry practitioners often view rates term structures as being driven by three main risk factors: level, slope, and curvature. Together these factors explain most of the return and risk of risk-free bonds with different maturities. They can be defined through statistical analysis or more directly as a combination of key rates (e.g., ten minus two year rates to represent the slope). Sometimes it is useful to further decompose these nominal factors into real rate and inf lation components (effectively doubling the number of factors under consideration). Exhibit 8 shows the R-square of rolling linear regression of the Barclays U.S. Treasury Index versus the changes in the 10-year rate; the chart suggests that about 90% of the index returns are explained by the 10-year rate movements. While duration factors are well understood and visible in any market that has a liquid risk-free term E XHIBIT 7 Mapping Fixed Income Factors Source: Blackrock. (1) FX means Exchange Rates For illustrative purposes only. E XHIBIT 8 Explaining the Returns of the Barclays U.S. Treasury Index Source: Barclays as of April 2015. The chart shows the R-square of a rolling linear regression of the Barclays U.S. Treasury Index monthly returns vs. the changes in the one-year rate (24-month window). SUMMER 2015 THE JOURNAL OF INDEX INVESTING

structure available, credit factors are not so well defined or understood. For corporate bonds, credit spreads reflect the risk premium the market demands for bearing default (and to a lesser extent recovery) risk of the firm as well as factors related to liquidity, tax and other market frictions (see Collin-Dufresne et al. [2001] for a study of the drivers of credit spreads). This gives us a starting point for understanding the types of factors that are potentially captured in credit spreads: bond characteristics, fundamental information, and technical signals could all play a role in defining corporate credit factors. Fixed income practitioners most often view credit factors as defined based on sectors and ratings (i.e., high yield financials, investment grade utilities) rather than the more quantitative fundamental and technical approaches taken in equities. To what extent the quantitative approach to (style) factor construction applies in credit is an open question and an active area of research in both academia and industry. Independent of how to define corporate credit factors, historically their application in the context of long-only (or unleveraged) investing is hindered by the relatively low excess returns generated by taking on credit spread risk relative to duration risk, and the relatively small performance differences between corporate bonds in low spread environments. Exhibit 9 displays the evolution of the cross-sectional dispersion of both corporate bond returns (in excess over durationmatched treasury bonds) and equity returns. Cleary there is much less variation in performance between the bonds than the equities. The potential for creating diversified returns through systematic bond selection relative to benchmark positions without application of leverage or long-short approaches is markedly less than in equities, apart from short periods of market turbulence during which spreads and returns across bonds diverge to a greater extent. Which factors matter most for bonds? The relative importance of duration factors versus spread factors depends on the relative size in potential changes in interest rates versus potential changes in spreads for different types of bonds. For indices with low credit spreads (investment grade bonds) duration factors dominate, for high yield bonds credit spreads become more relevant. To provide some intuition, we determine how much of the return on a broad corporate bond index can be attributed to risk-free term structure factors and how much can be attributed to credit spread factors. Exhibit 10 shows the risk and return decomposition for the Barclays Euro Aggregate Index (results for the U.S. or Global versions are similar). Not surprisingly, risk is primarily driven by duration factors in broad bond indices. In some periods, excess credit returns even contribute negatively to total index risk as the negative correlation with rates changes provides a degree of hedging. From a return perspective, fixed income investors that benchmark to market-cap-weighted indices rely on interest rates as the primary driver of both risk and return E XHIBIT 9 Sector Return Dispersion in U.S. Equities and IG Corporate Bonds Source: Barclays and Bloomberg. The chart displays the cross sectional standard deviation of monthly returns across the 24 GICS Industry Groups 3 of the S&P 500 Index and the 18 Industry Groups 4 of the Barclays U.S. Corporate Index. The two classification levels have been chosen in order to have a comparable numbers of sectors for the two indices. A FACTOR APPROACH TO SMART BETA DEVELOPMENT IN FIXED INCOME SUMMER 2015

E XHIBIT 10 Euro Aggregate Index Rolling Risk and Return Contribution Source: Barclays and Blackrock. Charts based on the monthly returns of the Euro Aggregate Index from Oct 2001 to Jan 2015. Risk is measured by rolling 24-month realized volatility. Return is measured by the 12-month cumulative return. in their portfolio. Credit spreads have added little on average, but do show cyclicality based on the market environment. DESIGNING FIXED INCOME SMART BETA An understanding of the factors that drive fixed income returns provides the building blocks for systematically considering smart beta approaches in fixed income. So far, alternatives to market cap indices have mostly been proposed on an ad hoc basis. For example, GDP-weighted or fiscal-strength-weighted treasury indices aim to provide alternative approaches that break the link between issuance, price, and investment exposure. Both approaches capture some notion of the issuer s ability to pay its debt but neither represent a clear investment objective. Alternative approaches in corporate credit are often addressed from a quality perspective based on fundamental screening, but do not typically encompass a multi-factor approach, nor balance credit factors relative to rates and other factors. We believe that smart beta in fixed income indices should provide investable solutions that aim to deliver on clear objectives. We consider three categories for potential objectives: 1. Better diversification. 2. Improved risk versus return profiles. 3. Precision exposure to isolated fixed income factors. In the first case, we choose exposure to fixed income factors based on a diversification scheme that intends to balance risk impact from different exposures. Indirectly this can lead to improved risk-return profiles over time but the primary objective is to make sure the index is not mainly exposed to a very limited number of return drivers. In the second case, we aim to embed performance related investment objectives in the index, related for example to valuation measures. In the third case, we isolate fixed income factors to provide building blocks to be used in portfolio construction. We give examples of possible smart beta approaches in all three categories. Example 1: Macro Factor Diversification in Aggregate Indices We are at an inflection point in fixed income markets in which the potential for rising rates in the U.S. and strong global demand for safe assets can potentially lead to scenarios in which duration can no longer be relied upon as a sole return driver. Historically, credit spreads have contributed relatively little to the total returns and risk of benchmark indices, but they have often provided offsetting returns to duration driven performance. This suggests there are untapped diversification benefits in both risk (which could be lowered through appropriately scaled offsetting positions) and returns (which could be higher on average as different macroeconomic exposures will deliver at different points in the business cycle). We investigate if we SUMMER 2015 THE JOURNAL OF INDEX INVESTING

can meaningfully improve the robustness of passive fixed income strategies and reduce exposure to potentially rising interest rates by seeking more equal allocations to the two main factor dimensions in bond indices. A straightforward way of doing so would be to seek to balance credit and interest rate exposure in a core bond portfolio based on their relative risk contributions. To illustrate, we rebalance the components of the Barclays U.S. Aggregate Index in order to target an equal risk contribution from rates and credit spread. The strategy is constructed as a basket of U.S. Corporate Bonds (IG and HY), MBS, Treasury and Treasury Futures, where the weights are determined on a monthly basis to balance spread risk and duration risk. To test the effectiveness of this approach we decompose the risk and the return of the strategy over the last five years by identifying the contribution coming from the rates exposure and the contribution coming from the credit spread exposure. The results are summarized in Exhibit 12 and highlight that the risk-balanced strategy has a better diversified profile in terms of risk and return contribution than the Barclays U.S. Aggregate Index. Exhibit 13 compares the performance of the risk-balanced strategy to the Barclays U.S. Aggregate Index: the risk-balanced strategy exhibits returns very close to the U.S. Aggregate Index but with a lower volatility, higher yield, and balanced contributions to both risk and return from credit and duration factors. Exhibit 11 depicts the E XHIBIT 11 Historical Allocation of the Risk-Balanced Strategy Source: Barclays and Blackrock as of March 2015. E XHIBIT 12 Risk and Return Breakdown Source: Barclays and Blackrock. Charts based on the monthly returns of the Barclays U.S. Aggregate Index and the risk-balanced strategy from Jan 2010 to Mar 2015. Left chart: the risk is measured by 24-month realized volatility (annualised) averaged over the last five years. A FACTOR APPROACH TO SMART BETA DEVELOPMENT IN FIXED INCOME SUMMER 2015

historical allocation of the risk-balanced strategy across the different buckets Example 2: Yield-to-Quality Weighting in Global Treasury Indices As discussed, the current allocation of the Barclays Global Treasury Index appears concentrated in countries with relatively high debt burdens, which is sometimes combined with unattractive yields (e.g., Japan). A more E XHIBIT 13 Performance Comparison Source: Blackrock and Barclays. Figures calculated over the period from Dec 1991 to Mar 2015. Returns and volatility are annualised Yield to maturity is as of March-2015 Index performance is shown for illustrative purposes only. One cannot invest directly in an index. attractive index could be constructed by tilting the standard weights towards those countries exhibiting a more favorable risk-return profile. In essence, we are choosing our exposures to different country duration and spread factors to improve the yield-to-risk ratio of the overall index. Various indicators have been developed to construct a forward looking estimate for the credit risk of a country; in our example we use the Blackrock Sovereign Risk Index (see Introducing the BlackRock Sovereign Risk Index BlackRock Investment Institute June 2011), which has been published since 2011 and is based on a comprehensive list of relevant fiscal, financial, and institutional metrics. By combining the sovereign risk indicator with the country yield, we can rank the various countries based on their yield-to-risk tradeoff and then we tilt weights in the standard index towards the countries with better scores. Exhibits 15 and 16 show that as a result of this simple modification, the new portfolio exhibits better risk-adjusted return, yield, and drawdown than the standard Barclays Global Treasury Index. The duration levels are comparable. E XHIBIT 14 Comparison Between Standard and Tilted Weights Source: Barclays and Blackrock as of January 2015. E XHIBIT 15 Performance Comparison Between Standard Weights and Tilted Weights Source: Blackrock and Barclays. All figures are based on monthly returns since June 2011 to January 2015. Returns and volatility are annualised. Yield and Duration are as of January 2015. Index performance is shown for illustrative purposes only. One cannot invest directly in an index. SUMMER 2015 THE JOURNAL OF INDEX INVESTING

E XHIBIT 16 Cumulative Performance and Rolling Yield Source: Barclays and Blackrock. Charts based on monthly returns from June 2011 to January 2015. Exhibit 14 shows a selection of the current country weights generated by the standard market capitalization weighting scheme and by applying our tilting rule. The main effect of the tilting is a shift of weight from Japan, Italy, and France towards the U.S., Germany, and South Korea. Example 3: Fallen Angels Provide Smart HY Exposure Fallen angels are high yield bonds rated as investment grade at the time of issuance that were subsequently downgraded. The downgrade event triggers the exclusion of these bonds from the main corporate indices and a subsequent sell off driven by benchmarked investors (trying to reduce tracking error or not allowed to hold securities below the investment grade). The result of this activity is pressure on the bonds price with associated underperformance of the fallen angels with respect to high yield bonds showing similar characteristics. Once the selling pressure instigated by the rating migration is over, the price tends to revert to the equilibrium level. This phenomenon has been extensively analyzed in academic and practitioner literature (see Ben Dor, Xu [2010] or Ellul et al. [2010] for more details) and provides an interesting opportunity to generate returns in excess of a standard high yield benchmark by buying fallen angels bonds after the downgrade and holding them over a long enough period to capture the reversal trend. By doing so we refine our exposure to HY credit spread factors. To illustrate, we test the performance of a strategy that invests in all the bonds included in the Barclays U.S. High Yield Index that were rated as investment grade at the time of their issuance. The bonds are market-capweighted and the basket is rebalanced on a monthly basis. Exhibits 17 and 18 compare the performance of the newly created strategy with the performance of the Barclays U.S. High Yield Index (used here as a benchmark). The fallen angels strategy outperforms the benchmark in terms of (risk-adjusted) returns with a slightly higher level of volatility and drawdown. The duration of the strategy is historically higher than for the benchmark and the yield to maturity is comparable (see Exhibit 19), but the outperformance is mainly driven by mean-reverting credit spreads of fallen angels, not different exposure to rates factors. CONCLUSION The risk and return of broad fixed income indices are driven predominately by interest rate (or risk free duration) factors. While historically investors have been well compensated for outsized exposures to interest rate risk, with interest rates near lower bounds across the developed world, the outlook for continued rewards from market-cap-benchmarked fixed income investing now looks less appealing. Rate dominated portfolios provide diversification versus equities and other risky asset classes in multi-asset portfolios, and will retain their value as a hedge in flight-to-quality environments. However, A FACTOR APPROACH TO SMART BETA DEVELOPMENT IN FIXED INCOME SUMMER 2015

E XHIBIT 17 Cumulative Performance of the Fallen Angels Strategy Source: Barclays as of April 2015. Chart based on monthly returns from August 1998 to March 2015. E XHIBIT 18 Performance of the Fallen Angels Strategy vs. the Barclays U.S. HY Index Source: Barclays as of April 2015. All figures are based on monthly returns since August 1998 to March 2015. Returns and volatility are annualised. Yield and Duration are as of March 2015. Index performance is shown for illustrative purposes only. One cannot invest directly in an index. E XHIBIT 19 Rolling Duration and Yield Source: Barclays as of April 2015. Charts based on monthly data since August 1998 to March 2015. we question the ability of conventional fixed income strategies to generate attractive long-term returns in the current macroeconomic environment on a stand-alone basis. Innovation in core fixed income investing may therefore be necessary to navigate increased uncertainty in the macroeconomic landscape. Smart beta fixed income approaches bring this innovation. They aim to better position passively oriented portfolios for an uncertain future. They seek to provide improved risk-adjusted returns by building portfolios to deliberately capture and diversify the sources of risk and return in fixed income. Time will tell if these promises will be fulfilled, but the opportunities are surely worth pursuing. SUMMER 2015 THE JOURNAL OF INDEX INVESTING

ENDNOTES The opinions expressed are as of April 14, 2015 and are subject to change at any time due to changes in market or economic conditions. 1 Principal component analysis is a well-established statistical technique developed by Pearson [1901]. The analysis aims at simplifying a data set by identifying latent common factors driving the risk of a pre-defined universe. With respect to our problem this technique can be used to identify the factors driving the risk of the considered universe of European active managers. For a reference explaining the principal component analysis please see: a. Hotelling, Harold, 1933. Analysis of a Complex of Statistical Variables into Principal Components. Journal of Educational Psychology, 24(6 & 7), 417 441 & 498 520. b. Jolliffe, I. T., 2002. Principal Component Analysis. Second ed. Springer Series in Statistics. New York: Springer- Verlag New York. c. Pearson, Karl, 1901. On Lines and Planes of Closest Fit to Systems of Points in Space. Philosophical Magazine, Series 6, 2(11), 559 572. 2 As measured by the realized variance. 3 The 24 GICS Industry groups are: Energy, Materials, Capital Goods, Commercial and Professional Services, Transportation, Automobiles and Comp., Consumer Durables and App., Consumer Services, Media, Retailing, Food and Staples Retailing, Food, Beverage and Tobacco, Households and personal products, Health Care Equipment and Services, Pharma, Biotech and Life Science, Banks, Diversified Financials, Insurance, Real Estate, Software and Services, Technology Hardware and Equipment, Semiconductors and Semiconductors Equip., Telecom. Services, and Utilities. 4 The 18 Industry Groups used in the Barclays Index are: Banking, Brokerage Asset Managers Exchanges, Finance Companies, Insurance, REITS, Other Financial, Basic Industry, Capital Goods, Consumer Cyclical, Consumer Non-Cyclical, Energy, Technology, Transportation, Communications, Industrial Other, Electric, Natural Gas, and Other Utility. REFERENCES Ang, A., W.N. Goetzmann, and S. Schaefer. Evaluation of active management of the Norwegian Government Pension Fund Global, Report to the Norwegian Ministry of Finance, (2009). Carhart, M.M. On Persistence in Mutual Fund Performance. Journal of Finance, (1997). Collin, D., P. Robert, S. Goldstein and J.S. Martin. The Determinants of Credit Spread Changes. Journal of Finance, 56, (2001). Ellul, A., C. Jotikasthira, and C. Lundblad. Regulatory Pressure and Fire Sales in the Corporate Bond Market. Working Paper, University of North Carolina at Chapel Hill, March 2010. Fama, E.F., and French, K.R. The Cross-Section of Expected Stock Returns. Journal of Finance, (1992).. Common risk factors in the returns on stocks and bonds, Journal of Financial Economics, (1993). Introducing the BlackRock Sovereign Risk Index, Black- Rock Investment Institute, (2011). Kahn and Lemmon, Making smart decisions about smart beta. BlackRock Investment Institute (2014). To order reprints of this article, please contact Dewey Palmieri at dpalmieri@iijournals.com or 212-224-3675. Disclaimer This document contains general information only and does not take into account an individual s financial circumstances. An assessment should be made as to whether the information is appropriate in individual circumstances and consideration should be given to talking to a professional adviser before making an investment decision. The opinions expressed are as of April 2015 and may change as subsequent conditions vary. The information and opinions contained in this material are derived from proprietary and non-proprietary sources deemed by BlackRock, Inc. and/or its subsidiaries (together, BlackRock ) to be reliable, are not necessarily all inclusive and are not guaranteed as to accuracy. There is no guarantee that any forecasts made will come to pass. Any investments named within this material may not necessarily be held in any accounts managed by BlackRock. Reliance upon information in this material is at the sole discretion of the reader. Past performance is no guarantee of future results. All investments involve risk and may lose value. The value of your investment can go down depending upon market conditions. Fixed income investments are subject to risk including interest rate, credit, market and issuer risk. The material is not intended to provide, and should not be relied on for, accounting, legal or tax advice. Ben Dor, A., J. Xu, and A. Fallen. Characteristics, Performance, and Implications for Investors. Barclays (2010). A FActor ApproAch to SmArt BetA Development in FixeD income Summer 2015