HOW CAN FACTORS BE COMBINED?

Transcription

1 HOW CAN FACTORS BE COMBINED? Top-down and Bottom-up Approaches to Multi-Factor Index Construction Padmakar Kulkarni, Abhishek Gupta, Stuart Doole June 2018 MSCI.COM PAGE 1 OF 27

2 CONTENTS Executive Summary... 3 Introduction... 4 A Bottom-Up Approach to Multi-Factor Investing... 6 Target Factor Selection...7 Factor Exposure Constraints...8 Investability Constraints...9 Portfolio Risk Target Rebalancing Frequency The Diversified Multiple-Factor Index Regional Performance Comparing the Top-Down and Bottom-Up Approach Conclusion References Appendix MSCI.COM PAGE 2 OF 27

3 EXECUTIVE SUMMARY Making allocations to individual factors typically requires strong investment beliefs, as factor returns have been cyclical in nature. When weighing the pros and cons of different multifactor indexed approaches, institutional investors often evaluate bottom-up and top-down options. We consider the attractions of both, using a bottom-up approach to build a multifactor index from stocks that are favorably exposed to the value, size, quality and momentum factors, compared with an alternative top-down approach combining single factor indexes. We compare the two approaches in terms of their level of exposure to the target factors as well as their capacity and investability profiles. Our analysis showed that both approaches demonstrated outperformance versus a marketcap-weighted parent index during our study period. In the context of multi-factor index construction, the top-down allocation offered the advantages of simplicity, relatively higher capacity and lower active risk. However, the exposure to the target factors was muted due to a dilution effect caused in part by offsetting stock weights across the single factor portfolios. On the other hand, our bottom-up approach, which uses optimization, did not have this same dilution effect. Instead, it showed higher and more persistent overall exposure to the target factors. As a result, both risk and return was attributed more to the target factors as desired. However, the level of active risk may be higher in the bottom-up approach and relative capacity may be lower. The choice between these approaches may depend most on the particular needs of the institutional investor. Further, within our bottom-up multi-factor construction approach, we analyze the sensitivity of the performance and characteristics of the index to varying levels of risk exposure and investability constraints, as well as the choice of rebalancing frequency. While removing constraints entirely resulted in large unintended active exposures and sacrificed index investability, a tight set of constraints restricted exposure to intended factors and interfered with performance. The index methodology of the MSCI Diversified Multiple-Factor Index is designed to strike a balance between these considerations. MSCI.COM PAGE 3 OF 27

4 INTRODUCTION MSCI has written extensively on capturing factor exposures, the challenges of factor investing and its place in the institutional investment process. 1 Through this work, we have identified a core set of six persistent equity risk factors (value, momentum, quality, low volatility, size and yield) 2 and constructed investible factor indexes on each of these risk factors. The MSCI Factor Indexes have demonstrated long term outperformance compared to their market-capitalization-weighted parent indexes (Exhibits 1 and 2). Exhibit 1: Performance of MSCI Single Factor Indexes Relative to MSCI World Index 1 See Capturing Factor Premia. (Lee et al., 2014) and the three-part series Foundations of Factor Investing. (Bender et al., 2013), Deploying Multi-Factor Index Allocations in Institutional Portfolios. (Bender et al., 2013) and IndexMetrics An Analytical Framework for Factor Investing. (Kassam et al., 2013). More recently, Factor Investing and ESG Integration. (Melas et al., 2016) reviewed approaches to, and the impact of, integrating ESG into factor strategies. 2 MSCI FaCS is a factor classification standard that provides a framework for evaluating, implementing and reporting factor allocations across six persistent equity risk premia factors value, momentum, quality, low volatility, size and yield. MSCI.COM PAGE 4 OF 27

5 Exhibit 2: Summary Statistics for MSCI Single Factor Indexes since 1975 Investors could passively replicate factor indexes as a strategic allocation alongside their passive market-cap investments or rotate in and out of single factor index based funds, based on their view of the market or the economic cycle. Factors have separate return drivers and have been subject to different performance cycles. The evident cyclical nature of these factors may create a long drag on performance in periods when the factor is out of favor. This historical variation in performance has motivated investors to consider combining different factors to achieve smoother performance. The low correlations between factor indexes (Exhibit 3) suggest that selectively combining factors could have reduced the overall volatility of the combined index during the period since Correlations calculated here span more than 40 years of historical performance, are robust and statistically plausible. Exhibit 3: Active Return Correlations for MSCI Factor Indexes since For many investors, the smoother return pattern of a multi-factor index relative to single factor indexes may be appealing. However, there are several choices that can be made to tailor an investment solution in the context of multi-factor indexes. Two fundamental choices are the selection of factors and the weighting of those factors. The choice of factors 3 Note that the MSCI factors are represented by the following indexes: Size by Equal Weighted, Yield by High Dividend Yield, Value by Enhanced Value and Low Volatility by Top 300 Volatility-Tilt prior to 1988 and by Minimum Volatility afterwards. Parent Index is MSCI World. MSCI.COM PAGE 5 OF 27

6 and weights may be driven by the investment belief in the factor, conviction over its future performance potential or the factor exposures embedded in other equity allocations through active and passive mandates. The two approaches to constructing a multi-factor index discussed in this paper are the topdown combination of factor indexes, which simply allocates to single factor indexes, and the bottom-up selection of stocks based on their individual factor exposures (using optimization). We compare these two approaches in the following sections. A BOTTOM-UP APPROACH TO MULTI-FACTOR INVESTING In this section, we describe our flagship bottom-up approach to constructing a multi-factor index. Exhibit 4 describes the index methodology used to construct the MSCI Diversified Multiple-Factor (DMF) Index and illustrates the range of choices to be made in this style of multi-factor index construction approach. We use a target risk optimization that maximizes exposure to four factors (value, size, momentum and quality) subject to a constraint on the total risk of the portfolio. In addition to the total risk constraint, we impose several other exposure and investability constraints, as detailed in Exhibit 4. Exhibit 4: MSCI Diversified Multiple-Factor Index Methodology Summary 4 Details on the Barra GEMLT model can be found in the Appendix or in greater detail, in Morozov et al. (2015) 4 The detailed index methodology may be found at MSCI.COM PAGE 6 OF 27

7 TARGET FACTOR SELECTION The choice of target factors is an important consideration in the design of a diversified multifactor index. Extensive academic research has shown that six factors value, momentum, quality, low volatility, size and yield have historically earned a persistent long-term premium. Bender et al. (2013) provide a detailed discussion of the economic rationale and empirical evidence associated with these factors. Some of these factors have been used extensively in quantitative investment strategies and explain part of the long-term portfolio performance of fundamental active investors (Frazzini et al., 2013). Exhibit 5: Summary Statistics for Bottom-up Multi-factor Index Simulations Exhibit 5 shows that the bottom-up multi-factor approach to index construction produced consistent historical performance across different choices of target factors. The active exposures to the style factors in each combination were consistent with the choice of target factors. Each multi-factor combination aims to maximize the equal-weighted average of the target factor index-level scores, subject to risk and investability constraints. The familiar two factor quant approach of combining value and momentum produced a historical information ratio (IR) of Combining value, momentum and quality led to a significant improvement, with the IR rising to The MSCI DMF Index, which targets four factors (value, size, momentum, quality) had the highest historical IR. Adding volatility to the four-factor mix improved the Sharpe ratio with a marginally lower historical IR (1.21) and markedly lower beta. Finally, incorporating both volatility and dividend yield factors into the target mix raised the yield of the index but produced a lower IR of The cyclical index (value, size, MSCI.COM PAGE 7 OF 27

8 momentum) underperformed the four-factor (DMF) setup, and the defensive index (quality, volatility, yield) generated the lowest realized risk. The MSCI Diversified Multiple-Factor Index targets exposure to size, value, momentum and quality. The volatility and yield factors are not included. The index aims to deliver variance similar to the market: if we were to add low volatility we would generally obtain below market risk. Yield was not included: it exhibited higher historical active correlation with value and quality (see Exhibit 3) and can be decomposed into those influences. 5 As highlighted earlier, the construction of the MSCI Diversified Multiple-Factor Index involves constraining exposures and investability as well as total risk. Further, the MSCI Diversified Multiple-Factor Index follows a semi-annual rebalancing schedule. In this section, we validate the importance of having constraints in the index construction methodology as well as evaluating the sensitivity of the performance and characteristics of the bottom-up construction to the level of constraints. FACTOR EXPOSURE CONSTRAINTS We illustrate four different variants to the DMF index to assess the impact of exposure constraints on its performance and characteristics. The first variant (Low Exposure) constrains non-target factor active exposures to a maximum of 0.1 cross-sectional standard deviations and limits active country and sector weights to less than 2%. The second variant is the DMF index itself, which sets the constraints at 0.25 for non-target factors and at 5% for countries and sectors. The third variant (High Exposure) relaxes these two constraints to 0.5 and 10% respectively. We also run a simulation without imposing any exposure constraints. 5 See Harvesting Equity Yield Understanding Factor Investing. (Wei et al., 2015) MSCI.COM PAGE 8 OF 27

9 Exhibit 6: Investigating the Impact of Factor Exposure Constraints on Multi-factor Indexes Exposure constraints aim to ensure that the DMF index does not have high exposure to country, sector or non-target style factor risk that we do not expect to contribute systematically to the accrual of the targeted factor premia over time. Exhibit 6 illustrates that historical performance and characteristics remained fairly constant under the different sets of exposure constraints. On the other hand, entirely removing exposure constraints resulted in active sector positions as high as 14% and active country bets as high as 18%. These relatively high levels of unintentional country and sector active weights may at times significantly affect the risk and performance of such a multi-factor index. INVESTABILITY CONSTRAINTS Next, we analyze the impact of imposing investability constraints upon the performance and characteristics of the multi-factor index. To assess this impact, we examine four multi-factor indexes where we impose different sets of investability constraints while leaving all other constraints and parameters constant. Specifically, we simulated the following: 1. High Capacity, with the simulation settings: Maximum Index Inclusion Factor = 5x, Maximum Active Stock Position = 1%, Annual One-Way Turnover Budget = 20% 2. The DMF Index, with the settings: Maximum Index Inclusion Factor = 10x, Maximum Active Stock Position = 2%, Annual One-Way Turnover Budget = 40% 3. Low Capacity, with the settings: Maximum Index Inclusion Factor = 20x, Maximum Active Stock Position = 4%, Annual One-Way Turnover Budget = 80% MSCI.COM PAGE 9 OF 27

10 4. Unconstrained, where we do not impose any investability or turnover constraints Exhibit 7: Investigating the Impact of Investability Constraints on Multi-factor Indexes Exhibit 7 shows that, as expected, performance and active exposures to the target factors deteriorated when we impose tight investability constraints and improved when we relaxed those constraints, with the Sharpe ratio of 0.50 for the High Capacity index rising to 0.57 for DMF and 0.60 for Low Capacity. Without any such constraints, the Sharpe ratio dropped to Exhibit 8: The Impact of Investability Constraints on Multi-factor Capacity and Investability Exhibit 8 shows the impact of different levels of investability constraints on the investability and capacity characteristics of the multi-factor index. Indeed, the High Capacity index where we imposed tighter investability constraints required only a notional two days to complete MSCI.COM PAGE 10 OF 27

11 95% of trading during index reviews for USD 10 billion, compared to 4 days for the MSCI DMF Index and 7.4 days for the Low Capacity version. On the other hand, entirely removing investability constraints resulted in a highly concentrated sample (31 stocks annual one-way turnover of 121%, which would take many days to trade and hold on average 5% of float market cap. Clearly, such an index would likely be extremely difficult to manage in practice, especially given the fund size typically associated with institutional portfolios. The investability constraints for the MSCI DMF Index aim to combine investability with acceptable concentration, capacity, liquidity and turnover characteristics, without sacrificing target factor exposures (and hence historical performance) too much. RISK TARGET We now assess the impact of constraining overall risk on the performance and characteristics of the multi-factor index, by varying the risk target to 90% (Low Risk target), 100% (DMF) and 110% (High Risk target) of the parent index risk. Exhibit 9: Investigating the Impact of Portfolio Risk Targets on Multi-factor Indexes Exhibit 9 shows that increasing the overall risk target produced a small improvement in historical performance (IR). The choice of market-like risk also facilitates the use of DMF index-based funds in an allocation or substitution framework. As the risk target is lowered, the index is naturally more defensive in risk terms and some value exposure is lost. REBALANCING FREQUENCY We now assess the impact of three different rebalancing frequencies: annual, semi-annual and quarterly. Historical performance and characteristics of the simulated indexes (Exhibit 10) remained generally steady under changing rebalancing frequencies with constant annual MSCI.COM PAGE 11 OF 27

12 turnover budget of 40%. 6 Given the lack of a consistent pattern over time or across different regions, the choice of semi-annual rebalancing for the MSCI Diversified Multiple-Factor Index was driven by its alignment with the rebalancing of the underlying market capitalization weighted parent index (with its associated data review). Exhibit 10: Investigating the Impact of Rebalancing Frequency on Multi-factor Indexes Finally, in Exhibit 11 we show performance attribution analysis using the Barra Global Equity Model (GEMLT). This analysis confirms explicitly that most of the historical risk and performance of the multi-factor index can be attributed to the targeted factors. Of the 528 bps of active returns for the MSCI World DMF Index, 453 bps can be attributed to the targeted factors. 6 This report may contain analysis of historical data, which may include hypothetical, backtested or simulated performance results. There are frequently material differences between backtested or simulated performance results and actual results subsequently achieved by any investment strategy. The analysis and observations in this report are limited solely to the period of the relevant historical data, backtest or simulation. Past performance whether actual, backtested or simulated is no indication or guarantee of future performance. None of the information or analysis herein is intended to constitute investment advice or a recommendation to make (or refrain from making) any kind of investment decision or asset allocation and should not be relied on as such. MSCI.COM PAGE 12 OF 27

13 Exhibit 11: Factor Attribution for the MSCI World Diversified Multiple-Factor Index Exhibit 12 shows the return contribution from target factors to other factors such as countries or stock-specific risks. As the investment horizon increased, the country and stockspecific return contribution declined, relative to the contribution from target factors. MSCI.COM PAGE 13 OF 27

14 Exhibit 12: Factor Return Contribution Increased with Time Horizon In summary, the choice of optimization parameters for the MSCI Diversified Multiple-Factor Index provided good exposure to the intended target factors while controlling for other risk exposures, without sacrificing historical performance and investability. THE DIVERSIFIED MULTIPLE-FACTOR INDEX REGIONAL PERFORMANCE The MSCI Diversified Multiple-Factor Indexes with parameters described in Exhibit 4 showed consistent performance across different regions and size segments (see Exhibit 13). In the standard size segment (large and mid-cap companies, as defined by the MSCI Global Investable Market Indexes methodology), historical IR was 0.84 for the MSCI World USA Index, 1.42 for the MSCI World ex USA Index, 0.71 for the MSCI Emerging Markets Index and 1.19 for the MSCI ACWI Index, which combines developed and emerging markets. In the small-cap segment, IR was 0.3 for the MSCI USA Small Cap Index and 1.30 for the MSCI World ex USA Small Cap Index. MSCI.COM PAGE 14 OF 27

15 Exhibit 13: Performance of DMF Indexes across Regions and Size Segments COMPARING THE TOP-DOWN AND BOTTOM-UP APPROACH A top-down approach to constructing a multi-factor index simply combines single factor indexes in any given proportion. The motivation behind the top-down combination is the flexibility to reflect changing investment views, relatively higher capacity and lower tracking error. However, the key compromise for the top-down approach is the dilution of factor exposure as the number of included factors increases and the cancellation of target exposures as factors with low correlations are combined (for example value with MSCI.COM PAGE 15 OF 27

16 momentum or quality). Stock-level multi-factor optimization opens the possibility of counteracting the exposure dilution that challenges the top-down approach. Constructing a multi-factor index using top-down and bottom-up approach produces different index characteristics (Exhibit 14). Exhibit 14: Key Features of Top-down and Bottom-up Approaches to Combining Factors Approach Top-down Factor-Mix Bottom-up Multi-factor (DMF) Objective Construction Factor Exposure and Performance Investability of Tracking Strategies Asset allocation across existing factor indexes Macro-Flexibility Simplicity Lower Factor Exposures Lower Absolute Return Lower Risk Adjusted Return Similar Beta, Similar Total Risk Higher Capacity Higher Liquidity Diversified; Low Concentration Security selection based on factor exposures Efficiency Controllability, Responsiveness Higher Factor Exposure Higher Absolute Return Higher Risk Adjusted Return Similar Beta, Similar Total Risk Sufficient Capacity and Liquidity Controlled Turnover Diversified; Moderate Concentration Exhibit 15 shows the historical performance of the MSCI World Diversified Multiple-Factor Index (bottom-up approach), which targets value, quality, size and momentum exposures, versus an equivalent four-factor multi-factor index (top-down approach) constructed by equally weighting the MSCI World Enhanced Value, MSCI World Sector-Neutral Quality, MSCI World Equal Weighted and MSCI World Momentum Indexes. Alighanbari et al. (2014) showed that equally weighting factors was highly effective historically and had superior performance to many rules-based dynamically weighted approaches after accounting for index turnover cost. MSCI.COM PAGE 16 OF 27

17 Exhibit 15: Summary Statistics for Top-down vs Bottom-up Multi-factor Index Simulations Over the simulation period 7, both multi-factor indexes outperformed the parent capweighted index, but the bottom-up multi-factor index delivered a higher annualized return (10.7%) than the top-down multi-factor index (8.2%). The bottom-up index also produced higher return/risk, Sharpe and information ratios. This came with higher tracking error. The top-down construction, being a simple aggregation of single factor indexes targeting different factor exposures, can potentially include securities from an underlying component index that has a large negative and neutralizing exposure to the target factor of another component index. On the other hand, the bottom-up process has been much more efficient and has ensured that the resulting portfolio has included stocks with high exposures to the targeted factors and has excluded stocks that could have potentially neutralized each other. For illustration purposes, Exhibit 16 shows the active value and momentum factor exposures for the factor mix and DMF Indexes, as of Dec. 29, The size of the dot represents the weight of the security in the multi-factor portfolio. The scatter plot shows that the factor mix not only included securities with high exposure to value and momentum factors, but also those that had high value-low momentum, low value-high momentum and even low value-low momentum exposures. In contrast, the DMF Index allocated a significantly higher proportion of the portfolio to securities with high value and high momentum exposures. Allocation to low value-low momentum stocks was small and resulted from targeting parent index risk level as well as other optimization constraints. 7 The simulation period was Nov. 30, 1999 to Jan. 31, MSCI.COM PAGE 17 OF 27

18 Exhibit 16: Factor Exposures for Top-down vs Bottom-up Multi-factor Indexes In the top-down construction, as the over-weights and under-weights of stocks from the underlying component indexes were partially neutralized, the factor exposures suffered from dilution. The exposure to the target factor was further diminished by the averaging down of the exposures of the included factor indexes. The top-down approach offers a simple narrative on the factor allocation weights and on the drivers of performance. However, we can now see the reality is more complex in terms of allocations (given the intended and unintended factor exposures) and the details of a factor performance attribution like Exhibit 11. MSCI.COM PAGE 18 OF 27

19 Exhibit 17: Factor Dilution in a Top-down Combination of Multiple Factors Finally, Exhibit 18 shows that the bottom-up multi-factor index has generally exhibited higher active exposure to the targeted factors than the top-down multi-factor index. MSCI.COM PAGE 19 OF 27

20 Exhibit 18: Comparing Active Factor Exposures for Bottom-up and Top-down Multi-factor Indexes using MSCI FaCS (note higher scale for DMF) In terms of sector profile, the bottom-up multi-factor index shows greater deviations from the parent index than the top-down multi-factor index. Exhibit 19: Comparing Active Sector Exposures for Bottom-up and Top-down Multi-factor Indexes The top-down multi-factor index is a more diversified index, with three times the number of constituents and lower concentration than the bottom-up multi-factor index. Consequently, the top-down multi-factor index has generally showed relatively better investability and MSCI.COM PAGE 20 OF 27

21 capacity profiles. For example, during periodic index reviews for a replicating fund of USD 10 billion in size and based on a simple 20% participation in average daily volume, it might take close to one day to complete 95% of rebalancing trades for the top-down multi-factor index versus five days for the bottom-up multi-factor index. 8 This highlights that the cost of replicating a bottom-up multi-factor strategy may be higher than the top-down approach, particularly for large-scale implementation (based on these simple participation and average volume calculations). Exhibit 20: Capacity and Investability for Bottom-up and Top-Down Multi-factor Indexes 8 More details on the investability and capacity calculations can be found in Kassam et al. (2013). MSCI.COM PAGE 21 OF 27

22 CONCLUSION In this paper, we examined two different approaches to constructing multi-factor indexes the top-down factor-allocation approach versus the bottom-up factor-selection approach. These approaches produced multi-factor indexes that represented the performance of the targeted factors to varying degrees and with fairly different index characteristics. The top-down multi-factor index uses individual factor indexes as its underlying building blocks and aggregates the factor indexes in given proportions. The constituents weights in such an index reflect their exposure to the underlying factors. Ignoring the unintended factor exposures, the allocation to each factor is easy to describe. Key advantages of the top-down approach are thus simplicity and macro-flexibility. The construction approach allows investors to combine multiple factor indexes into a single index and frequently change allocations. A simple performance narrative can also be told. The bottom-up index approach, on the other hand, targets specific factor exposures in its construction. The optimization employed is designed to maximize the intended factor exposures while constraining unintended factor tilts. The clear advantages of this bottom-up multi-factor index are its efficiency (the risk-return tradeoff) and controllability. Moreover, the use of optimization in maximizing target factor exposures has meant the bottom-up multi-factor index has produced more persistent and controllable factor exposures, and historically stronger performance. The analysis highlighted the potential trade-offs in adopting either choice as an approach to multi-factor index construction and the key decisions investors may face in doing so. MSCI.COM PAGE 22 OF 27

23 REFERENCES Alighanbari, M. and C.P. Chia. (2014). Multi-factor Indexes Made Simple: A Review of Static and Dynamic Approaches. MSCI Research Insight. Bender, J., R. Briand, D. Melas and R.A. Subramanian. (2013). Foundations of Factor Investing. MSCI Research Insight. Bender, J., R. Briand, D. Melas, R.A. Subramanian and M. Subramanian. (2013). Deploying Multi-factor Index Allocations in Institutional Portfolios. MSCI Research Insight. Doole, S., C.P. Chia, P. Kulkarni and D. Melas (2015) The MSCI Diversified Multi-Factor Indexes: Maximizing Factor Exposure While Controlling Volatility. MSCI Research Insight. Frazzini, A., D.Kabiller and L. Pedersen. (2013). Buffett s Alpha. NBER. Kassam, A., A. Gupta, S. Jain, R. Kouzmenko and R. Briand. (2013). Introducing MSCI IndexMetrics: An Analytical Framework for Factor Investing. MSCI Research Insight. Lee, J.H., O. Ruban and D. Melas. (2014). Capturing Factor Premia: Creating Investable Factor Portfolios through Optimization. MSCI Research Insight. Melas, D., Z.Nagy and P.Kulkarni. (2016). Factor Investing and ESG Integration. MSCI Research Insight. Morozov, A., S. Minovitsky, J. Wang and J. Yao. (2015). Barra Global Total Market Equity Model for Long-Term Investors Empirical Notes. MSCI Model Insight. Wei, Z., C.P. Chia and S. Katiyar. (2015). Harvesting Equity Yield Understanding Factor Investing. MSCI Research Insight. MSCI.COM PAGE 23 OF 27

24 APPENDIX When constructing a multi-factor index using the bottom-up approach, the risk model employed in the optimization process plays an important role in building the factor exposures and reducing the stock-specific contribution. The risk model can be leveraged to serve the dual purposes of targeting sources of risk that have historically generated premia and managing exposure to other sources of risk. MSCI launched the Global Equity Model for Long-Term Investors (GEMLT) in 2015, which built on the earlier versions of the risk model while incorporating many of the latest riskmodelling innovations. The introduction of new Systematic Equity Strategy factors as well as data and methodological enhancements to the existing factors, helped enhance the information content of the model and improve its accuracy for portfolio construction, risk forecasting and monitoring exposures. With 16 factors and 41 descriptors, the GEMLT model has a comprehensive global equity factor structure. Exhibit A1: Barra GEMLT Factor Structure 8 Groups, 16 Factors and 41 Descriptors Source: Barra Global Equity Model for Long-Term Investors (GEMLT) The benefits of using the GEMLT model for constructing the MSCI Diversified Multiple-Factor Indexes were welcomed by investors during an index consultation process in The MSCI Diversified Multiple-Factor Indexes were transitioned from using GEM2 9 to the GEMLT 9 See Doole et al. (2015). MSCI.COM PAGE 24 OF 27

25 model during the May 2018 Semi-Annual Index Review. With the new GEMLT model, the index construction benefits from its enhanced factor structure and improved risk forecasting. It also allows for better control of non-target factor exposures and reduces the contribution of country/industry factors to performance. In addition, it has resulted in faster decay of stock-specific contributions to return and risk. The approach is designed to enable close alignment of the target factor definitions with MSCI s factor classification standard (MSCI FaCS). MSCI.COM PAGE 25 OF 27

26 CONTACT US AMERICAS Americas * Atlanta Boston Chicago Monterrey New York San Francisco Sao Paulo Toronto EUROPE, MIDDLE EAST & AFRICA Cape Town Frankfurt Geneva London Milan Paris * ABOUT MSCI For more than 40 years, MSCI s researchbased indexes and analytics have helped the world s leading investors build and manage better portfolios. Clients rely on our offerings for deeper insights into the drivers of performance and risk in their portfolios, broad asset class coverage and innovative research. Our line of products and services includes indexes, analytical models, data, real estate benchmarks and ESG research. MSCI serves 99 of the top 100 largest money managers, according to the most recent P&I ranking. For more information, visit us at ASIA PACIFIC China North * China South * Hong Kong Mumbai Seoul * Singapore * Sydney Taipei * Thailand * Tokyo * = toll free MSCI.COM PAGE 26 OF 27

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