Style Rotation

Global Markets ResearchQuantitative Strategy Quantitative Strategy North America United States 18 April 2012 Portfolios Under Construction Uncertainty and Style Dynamics Uncertainty, style dynamics and factor rotation strategies Reseach Summary Uncertainty and episodic shifts in risk appetite can rotate style factors towards unwanted and even dangerous risk profiles. We investigate the drivers and dynamics behind these stealth rotations and show how to monitor, control and even profit from them. Team Contacts Miguel-A Alvarez Risk appetite changes and involuntary style rotation We perform a forensic analysis of recent factor behavior to show how sharp and rapid shifts in risk appetite can rotate factors away from their steady state compositions. Indeed, as we show in the case of Momentum these rotations can unexpectedly position factors towards stocks that have been overbought and away from stocks that have been severely oversold. Vigilant monitoring and understanding factor dynamics We propose a simple and effective way to monitor factor dynamics in the face of uncertainty and strong changes in risk aversion. We also investigate style dynamics in the past to analyze factor shifts in past episodes of market uncertainty. VRP to the rescue We use the variance risk premium (VRP) to take a proactive stance in the face of continuing macroeconomic uncertainty and recurring style shifts. We devise two simple factor-timing strategies based on the VRP. We find these strategies to be highly effective at switching; especially during episodes of shifting risk appetite (risk-on/risk-off). Strategist (+1) 212 250-8983 [email protected] Yin Luo, CFA Strategist (+1) 212 250-8983 [email protected] Rochester Cahan, CFA Strategist (+1) 212 250-8983 [email protected] Javed Jussa Strategist (+1) 212 250-4117 [email protected] Zongye Chen Strategist (+1) 212 250-2293 [email protected] Sheng Wang Strategist ( +1) 212 250-8983 [email protected] Deutsche Bank Securities Inc. Note to U.S. investors: US regulators have not approved most foreign listed stock index futures and options for US investors. Eligible investors may be able to get exposure through over-the-counter products. Deutsche Bank does and seeks to do business with companies covered in its research reports. Thus, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report. Investors should consider this report as only a single factor in making their investment decision. DISCLOSURES AND ANALYST CERTIFICATIONS ARE LOCATED IN APPENDIX 1.MICA(P) 146/04/2011. 18 April 2012 Portfolios Under Construction Table of Contents Letter to our readers ......................................................................... 3 Uncertainty continues…............................................................................................................ 3 Risk appetite and style dynamics – a recent synopsis ................... 4 Risk-on, risk-off and recent style dynamics ............................................................................... 4 Recent factor performance ....................................................................................................... 6 The Beta connection ................................................................................................................. 8 Factor Dynamics and Regimes ............................................................................................... 15 VRP and style rotation .................................................................... 16 Variance risk premium and risk appetite changes ................................................................... 16 Implementing the VRP strategy .............................................................................................. 18 VRP for style rotation .............................................................................................................. 19 References ........................................................................................ 23 Page 2 Deutsche Bank Securities Inc. 18 April 2012 Portfolios Under Construction Letter to our readers Uncertainty continues… Last year saw a series of events that caused havoc for some investors. We look at the effect of these de-risking and subsequent re-risking episodes not only in the context of factor performance. Lastly. but also on the dynamics underlying factor rotation.1 while the performance of many conventional quantitative factors ended in positive territory in 2011. 1 Our conversations with clients lead us to believe that performance across large cap quantitative funds was more mixed than overall good. the numbers were not nearly as robust as those across the full spectrum of US stocks. We will link much of the outperformance of conventional quantitative factors over 2011. Indeed. Page 3 . What actually happened in 2011? Why where quantitative factors working so robustly again and what happened to quants in January 2012 when many saw significant drawdowns in performance? In this report. Rocky. quantitative investors generally outperformed while fundamental managers struggled1. in that. we use the variance risk premium (VRP) together with the analysis on style factor dynamics under changes in risk-appetite to devise two simple factor rotation schemes that is adept at picking factors that will outperform during these episodes. or driving some factors to take very concentrated risk exposures. Miguel. 2 Deutsche Bank Securities Inc. Yin. we aim to address these questions and much more. while at the same time dumping riskier stocks. these episodes can reposition factors and may sometimes make different factors across different style more correlated. especially that corresponding to the de-risking episode in the summer. We will see that aside from driving significant levels of performance. General consensus has it that 2011 saw a reversal from past years. See Luo et al. The large cap universe was somewhat of a different story and while our factors did show overall positive performance over 2011. John & Sheng. our own long/short equity QCD Model Portfolio 2 had an IR of 2. while others found themselves well-positioned and outperformed. This low-risk outperformance during the summer and fall was driven by the de-risking episode caused by a worsening of the European sovereign debt crisis. to the outperformance of low Beta stocks and significant underperformance of high Beta stocks during this period. 2012 “QCD Model: DB Quant Handbook” for a detailed model methodology. Javed. The crisis induced fears of recession sending investors overwhelmingly towards safety and out of risk in which they piled into quality and other low-risk stocks. Thanks. US DBQS universe. this characterization is sufficient to gauge risk preferences within the aggregate equity universe. while periods of decreasing risk appetite would generate negative portfolio returns. Indeed. loosely. the return to this kind of portfolio should be positive. A simplistic yet effective means to quantify the demand for higher/lower risk is via the return to a portfolio that is long high-risk stocks and short low-risk stocks. but a conventional means is to use stock Beta4. However. Figure 1 shows the monthly and cumulative return to a portfolio that is long the top decile of stocks ranked by Beta and short the bottom decile for our DBQS universe5 since January 2011. 5 Our DBQS universe is covers a similar universe to the Russell 3000. The figure also shows the return to the market capitalization portfolio which correlated strongly with risk appetite especially during strong episodes of changing risk appetite. Figure 1: US Monthly & Cumulative return to high minus low Beta Portfolio (Decile 10 minus Decile 1) and Market Portfolio (capitalization weighted).3 Stock risk can be measured in many different ways. .18 April 2012 Portfolios Under Construction Risk appetite and style dynamics – a recent synopsis Risk-on. we will use stock Predicted Beta from the Axioma medium horizon risk model. “risk-on/risk-off” along with “re-risking/de-risking” will be the defining labels for market behavior over this tumultuous period. Therefore. risk-off and recent style dynamics One of the determining drivers of investor risk appetite is economic sentiment. Measuring changes in risk-appetite Risk appetite can be interpreted. decreases in risk appetite generate greater demand for lower risk assets and will cause their prices to increase relative to those of higher risk. Increases in risk appetite corresponds to greater demand for risk causing prices of riskier assets to increase relative to those of lower risk. as the demand for risky assets. Page 4 4 Unless otherwise noted. Deutsche Bank Securities Inc. as we will see. 20% Monthly Return 15% Beta (Q10-Q1 spread) Monthly Return (LHS) Market Portfolio Monthly Return (LHS) Beta (Q10-Q1 spread) Cumulative Return (RHS) Market Portfolio Cumulative Return (RHS) re-risking 50% 40% 30% 10% 20% 5% 10% 0% 0% -5% -10% -10% -20% -15% -20% Cumulative Return 25% -30% de-risking -40% Source: Axioma. Deutsche Bank Quantitative Strategy 3 We are aware that this definition and characterization of the demand for risk is oversimplified. the enduring global macroeconomic instability and the uncertainty it propagates to financial markets has been a major catalyst for the abrupt and sharp reversals experienced in risk appetite since the financial crises. When risk appetite increases. Conversely. S&P. Russell. While we are unsure of the full resolution of Europe’s economic troubles. Many investors take the spread to be a “fear index”. we do agree that the likelihood of a US recession has decreased since the summer. It is simply the spread between Libor and the three-month US Treasury Bill6. Figure 2: Global ex-US Monthly & Cumulative return to high minus low Beta Portfolio (D10 – D1) and Market Portfolio (cap weighted). Figure 3 and Figure 4 show that indeed the TED spread and the return to global Beta have a strong inverse relationship. Therefore. these episodes can be interpreted as investors shifting towards Size in a stronger way than they are shifting away from higher Beta. it is the result of the Market having a much larger Size bias than the Beta portfolio.e. The subsequent re-risking episodes in October and more recently in January correspond to what many are calling a resolution to “tail risk” in Europe. we expect that changes in the TED spread should be inversely related to the returns to the Beta portfolio. we find that risk appetite in the rest of the world had similar behavior during 2011 and beginning of 2012 (Figure 2).com/rate/TED_Spread Page 5 . S&P. Two good examples are April 2011 and March 2012. Last.wikinvest. 6 Deutsche Bank Securities Inc. i. Russell. S&P BMI Global ex-US universe. 25% Monthly Return 15% 10% re-risking 40% 30% 20% 5% 10% 0% 0% -5% -10% -10% -20% -15% de-risking -20% Cumulative Return 20% 50% Beta Monthly Return (RHS) Market Portfolio (LHS) Beta Cumulative Return (RHS) Market Portfolio Cumulative Return (RHS) -30% -40% Source: Axioma.18 April 2012 Portfolios Under Construction The negative returns to Beta and the market during August and September of 2011 correspond to sharp declines in risk appetite (aka de-risking) brought along in most part by the worsening European risk landscape as Italian sovereign yields increased to dangerous levels. Fundamentally. Higher levels of the TED spread are associated with higher risk aversion (lower risk appetite) and vice versa. we note from Figure 1 that positive returns to the market portfolio do not always coincide with larger returns to higher Beta stocks. A risk explanation for this phenomenon would lie in the generally accepted notion that Beta is not the sole driver of systematic return. Deutsche Bank Quantitative Strategy Other measures of risk appetite Another measure that is commonly used to look at risk appetite in general is the TED spread. Therefore. positive market return does not necessarily translate to increases in risk appetite. Risk appetite in other markets Outside the US.. http://www. S&P. a naïve categorization detects two groups of factors. while underperforming during the re-risking in October 2011 and subsequent to December 2011. A first set (Figure 6) which outperformed during the summer/fall de-risking episode. which we detail below. we will work with decile spread portfolios. The second set (Figure 7) underperformed or were flat during the summer de-risking and slightly outperformed (or were flat) during the subsequent re-risking episode. For practical purposes. we will analyze a subset of factors from our factor library which are conventionally used to represent investment styles. Other factors were mixed. Russell. unless otherwise noted. Figure 5: Factors and styles Style Value Momentum/Sentiment Quality Growth Factor Direction FY1 Dividend Yield + FY1 Earnings Yield + Price-to-Book -- Price-to-Sales -- Momentum 12-month + Momentum 6-month + FY1 EPS Revisions + FY1 EPS Dispersion -- ROE + YoY EPS Growth + EPS Growth (5yr) + Source: Deutsche Bank Two factor categories during 2011 Based on the evolution of cumulative factor performance over 2011 and the beginning of 2012. Deutsche Bank Quantitative Strategy Recent factor performance What effect did the strong decrease in risk appetite during the summer 2011 have on style factors? As uncertainty increases. Bloomberg LLP. Dividend Yield and Earnings dispersion. but rather to analyze factor shifts arising from changes in risk-regime changes. but we can also characterize defensive stocks via style factors such as ROE. . In addition. Sectors and industry matter. Bloomberg LLP.Portfolios Under Construction Figure 3: TED spread versus cumulative return to Global Beta factor portfolio (D10-D1) 60 Figure 4: Changes in TED spread versus monthly return to Global Beta factor portfolio (D10-D1) 20 10% TED Spread 50 15 -10% 30 -20% 20 Change in TED Spread TED Spread 40 Cumulative Return 0% 20% TED (change) Beta Monthly Return (RHS) 10 10% 5 0 0% -5 -10 Monthly Return 18 April 2012 -10% -30% 10 -15 Beta Cumulative Return 0 -40% Source: Axioma. Deutsche Bank Quantitative Strategy -20 -20% Source: Axioma. Russell. S&P. Figure 5 lists the styles and factors we will use in this analysis. Page 6 Deutsche Bank Securities Inc. The goal is not to analyze every factor. investors will eventually trade off riskier stocks that may not sustain their expected cash flows in a recession for more defensive stocks that are more resistant to economic contractions. 38% -0. To get a better sense of the link between factor performance and recent changes in risk appetite. Page 7 .65% -0. the factors depicted in Figure 7 showed to underperform during the de-risking episodes.27% 8.66% 11.79% 0. Deutsche Bank Quantitative Strategy Is risk appetite driving factor performance? In the last section. Deutsche Bank Securities Inc.41 Price-to-Book -6. Beta. Russell.18 April 2012 Portfolios Under Construction Figure 6: Cumulative return outperforming factors during the de-risking in summer 2011. when the magnitude of the return to Beta is large). S&P. the factors depicted in Figure 6 underperformed during the re-risking in October and subsequently in January and February of 2012.09 FY1 Earnings Yield 0.41% 4.91 Source: Axioma.75 YoY EPS Growth 5.68% 0.04 Market 11.11% 1.e. Russell. S&P. Russell. Figure 9 and Figure 10 compare the monthly returns of a subset of factors to our risk-appetite factor. Figure 8: Factor decile spread portfolio statistics over Jan 2011 – Mar 2012 Factor Average Return ROE Volatility Sharpe Ratio 9. Deutsche Bank Quantitative Strategy A closer look at factor performance in Figure 6 shows that the de-risking episode starting in August generated significant gains for each of the factors with exception to 12-month Momentum. The returns refer to the decile portfolio spreads.26% -0.62 Momentum 12-month 5.45 Price-to-Sales 1.87% 20. Russell 1000.29% 1.62% 0.90% 1.81% 15.18 FY1 EPS Dispersion 13.73% 12. 30% Cumulative Return 20% 25% FY1 EPS Dispersion ROE 20% Beta Monthly Return 25% 30% FY1 Dividend Yield Momentum 15% 10% 5% Price-to-Sales FY1 EPS Revision YoY EPS Growth 15% 10% 5% 0% 0% -5% -5% -10% -10% Source: Axioma.68% 0. S&P.85% 13.75% 0.73% 33. Russell 1000 Figure 7: Cumulative return for under-performing factors that during summer 2011. The negative (Figure 9) and positive (Figure 10) correlation between the factors and Beta is evident.35% 0. especially across periods when risk appetite experiences strong changes (i.61% 7.59% 7.08 Momentum 6-month -1.15 FY1 EPS Revisions 0. Figure 8 shows the performance to the other factors in our study. we saw how factor performance was strongly influenced by recent episodes of re-risking and de-risking.01% 10. Deutsche Bank Quantitative Strategy FY1 Earnings Yield Source: Axioma. while outperforming (albeit slightly in the case of Earnings Revision) during the episodes of re-risking. Conversely.63 EPS Growth (5yr) 11.97% 10.46% 0.09 Beta (D10-D1) -13.47 FY1 Dividend Yield 5. Similarly.61% 5. the relationship between Beta and different factors seem to vary widely even between factors representing the same styles. Deutsche Bank Quantitative Strategy Figure 9 and Figure 10 show that during the summer of 2011 to early 2012. we discuss how to measure the relationship between a factor and Beta in an accurate and timely manner. Beta return decile spreads. we have documented the link between Beta and style factors8. we will use two cross-sectional measures to characterize the link of a factor to Beta: „ Portfolio Beta „ Expected Correlation between the factor portfolio and the Beta portfolio The first is simply the Beta of the factor portfolio and is computed in the typical manner. In the following analysis. Deutsche Bank Quantitative Strategy 10% 8% 20% 6% 4% 10% 2% 0% 0% -2% -10% -4% Predicted Beta (LHS) -20% -6% FY1 Earnings Yield (RHS) -8% YoY EPS Growth (RHS) -30% FY1 Earn Yld. have large and rapid shifts. In those studies. One of the more important findings in that research underlined the need to include crosssectional information when computing exposures and correlations across factors.Portfolios Under Construction 30% 6% 4% 10% 2% 0% 0% -2% -10% -4% Predicted Beta (LHS) FY1 Dividend Yield (RHS) FY1 EPS Dispersion (RHS) -6% -8% -30% FY1 Div Yld. at times. the portfolio weighted average stock Beta. but their performance and their relationship with Beta was very different during this period7. However. not all factors were linked in the same way. However. YoY EPS Growth Monthly Return Figure 9: FY1 Dividend Yield and FY1 EPS Dispersion vs. 2010. we found that this exposure was dynamic and could. . 8 See Alvarez et al. In the next section. i. Russell 1000 -10% Source: Axioma. it is of little use for quantifying an accurate measure of the co-dependency between two factors. we found that style factors could possess an inherent exposure to Beta and pick up significant exposure to Beta during certain market regimes or which could have adverse effects on performance. Dividend Yield and Earnings Yield are both considered to be Value factors. “Portfolios Under Construction: Volatility=1/N”. S&P.e. FY1 EPS Disp. S&P. This will prove useful to determine ex-ante how certain style factors will perform during an episode of de-risking or re-risking. the Beta of a portfolio lends insight in that it will provide a sense of the direction of the comovement (positive Beta implies positive comovement and vice versa). Russell. The Beta connection In previous research. Moreover. Deutsche Bank Securities Inc. For example. Monthly Return 20% Beta Monthly Return 30% 10% 8% -20% Figure 10: FY1 Earnings Yield & YoY EPS Growth versus Beta return decile spreads. Russell. Page 8 7 In fact. In general. This is because the cross-sectional analysis provides a snapshot of the current factor composition and does not depend solely on past data that could be stale depending on factor dynamics. i. Russell 1000 Beta Monthly Return 18 April 2012 -10% Source: Axioma. the comovement between Beta and style factors was very strong.e. many investors regard Dividend Yield more representative of Quality than Value. We will show how Beta affects the performance of the factors. The expected correlation measure is quite powerful in that it yields timely and accurate forecasts of factor co-dependency. we will use the changes in factor correlations to Beta gain insight into the interactions and dynamic relationships between the factors. In the following sections. More importantly. In addition. it is a useful tool for monitoring and understanding factor dynamics as well as for applications in risk and portfolio construction. but is essential if accuracy is the primary objective. but more importantly how episodes of de-risking/re-risking dynamically rotate their risk exposures profiles.18 April 2012 Portfolios Under Construction The second metric is a forward estimate of the correlation between the factor portfolio and the Beta portfolio9. Page 9 . we analyze the exposures and correlation of each of the style factors to Beta. 9 This can be generalized to factor scores in general (not just decile spread portfolios) as we showed in Alvarez et al. This measure is somewhat more involved and requires a stock covariance matrix. 2011 “Reviving Momentum: Mission Impossible?” Deutsche Bank Securities Inc. With exception to FY1 Dividend Yield. there was not much consistency between the correlations to Beta and factor performance. However. S&P. The top graph in Figure 11 shows the expected correlation to Beta for different value factors during 2011-2012. Russell 1000 Source: Axioma. we shed more light on the role that Beta plays in driving the divergence and convergence across Value style factor performance. We begin with a simple synopsis covering the more recent period. In this section. the chart shows that FY1 Earnings Yield and Price-to-Book have significant shifts in their correlation to Beta. These numbers suggests that the homogeneity between different value factors can be quite dynamic over time and across varying market conditions. . Also note the mixed performance during November and December 2011. Figure 11: Expected correlation with Beta (top chart) and performance (bottom chart) between Value factors during 2011-2012. For example.18 April 2012 Portfolios Under Construction Value Value factors did not exhibit similar cumulative performance during 2011-2012 (Figure 6 and Figure 7). But is Beta driving performance? The graph at the bottom of Figure 11 shows that Beta will overwhelm factor performance when both the correlation to Beta and the magnitude of the return of the Beta portfolio is significant. which saw relatively low magnitudes to the Beta portfolio returns. Deutsche Bank Quantitative Strategy Page 10 Deutsche Bank Securities Inc. The first half of 2011 saw varying levels of correlation across the different Value factors. In addition. Russell. Beta plays a more significant role in driving factor performance. during the de-risking/re-risking episodes in the second half of the year. note that during the first half of 2011 when Beta return magnitude was relatively low. the de-risking/re-risking episodes during the summer and fall of 2011 cause a series of shifts in Beta correlations culminating in convergence. FY1 Dividend Yield. Jan-12 Jan-11 Jan-10 Jan-09 Jan-08 Jan-07 Jan-06 Jan-05 Jan-04 Jan-03 Jan-02 Jan-01 Jan-00 Jan-99 Jan-98 Jan-97 Jan-96 Jan-95 Jan-94 Jan-93 Jan-92 Jan-91 Jan-90 Jan-12 Jan-11 Jan-10 Jan-09 Jan-08 Jan-07 Jan-06 Jan-05 Jan-04 Jan-03 Jan-02 Jan-01 Jan-00 Jan-99 Jan-98 Jan-97 Jan-96 Jan-95 Jan-94 Jan-93 Jan-92 Jan-91 -100% Jan-90 -2. the Dividend Yield factor can actually experience shifts in Beta exposure and correlation over time.5 0. Russell 1000 Figure 15: Expected Correlation with Beta: Price-toBook. the exposures and correlations to Beta can vary widely across value factors over different market regimes.5 -1. Deutsche Bank Quantitative Strategy Jan-12 Jan-11 Jan-10 Jan-09 Jan-08 Jan-07 Jan-06 Jan-05 Jan-04 Jan-03 Jan-02 Jan-01 Jan-00 Jan-99 Jan-98 Jan-97 Jan-96 Jan-95 Jan-94 Jan-93 Jan-92 Jan-91 Jan-90 Jan-12 Jan-11 Jan-10 Jan-09 Jan-08 Jan-07 Jan-06 Jan-05 Jan-04 Jan-03 Jan-02 Jan-01 Jan-00 Jan-99 Jan-98 Jan-97 Jan-96 Jan-95 Jan-94 Jan-93 Jan-92 Jan-91 -100% Jan-90 -2. Deutsche Bank Quantitative Strategy Last. S&P. FY1 Dividend Yield.5 -1.5 0.0 100% Price to Book Price to Book 80% Price to Sales Expected Correlation with Beta Portfolio Beta 0. The exposures and correlations show that the two factors exhibit a similar Beta profile over time with exception to the period after the technology bubble circa 2000 – 2003.5 FY1 Dividend Yield 60% 40% 20% 0% -20% -40% -60% -80% Source: Axioma.18 April 2012 Portfolios Under Construction If we analyze the past (Figure 12 through Figure 15). note that leading into the Financial Crisis of 2008. S&P. S&P. Price-to-Sales. This is due to the fact that the sell-off of risky stocks during this period was so robust that it even penetrated dividend paying stocks – mainly financial stocks that had by that time become the riskier (higher Beta) of that group. Deutsche Bank Quantitative Strategy Deutsche Bank Securities Inc. Russell. Russell 1000 Figure 13: Expected Correlation with Beta: FY1 Earnings Yield. Deutsche Bank Quantitative Strategy Page 11 . Also note that in contrast to the behavior in Figure 11.5 Price to Sales 60% 40% 20% 0% -20% -40% -60% -80% Source: Axioma. Price-to-Sales. the Dividend Yield factor became more correlated with higher Beta stocks.0 Source: Axioma. For example.0 -1. Russell.0 100% FY1 Earnings Yield Expected Correlation with Beta Portfolio Beta FY1 Earnings Yield 80% FY1 Dividend Yield 0. In addition. we find that the dynamic relationship with Beta is a common theme across value factors. Russell 1000 1.0 Source: Axioma. Russell 1000 1.0 -0.0 -0. Russell. it is worthwhile to note that Figure 14 and Figure 15 lend insight into the relationship between Price-to-Book and Price-to-Sales factors. S&P. Russell. – Figure 14: Portfolio Beta: Price-to-Book.0 -1. Figure 12: Portfolio Beta: FY1 Earnings Yield. The historical correlation to Beta of both factors (Figure 18) shows that both the 12-month 10 See Alvarez et al 2011. Deutsche Bank Quantitative Strategy Note that correlations were significant leading into the de-risking. the correlation levels between 25-50% indicate that Beta will account for only a quarter to half of the variability of returns. A good illustration of their sensitivity to Beta can be seen during 2011-2012 depicted in Figure 16. In addition. First. Indeed. This rapid and strong rotation set up the factors to severely underperform during the re-risking episode in January 2012 as investors increased risk appetite buying oversold risky (high Beta) stocks and selling off overbought safe (low Beta) stocks. Figure 16: Expected correlation with Beta (top chart) and performance (bottom chart) of Momentum/Sentiment factors (Decile Spread Portfolios). stock-specific component) had positive performance over this period. we note that the correlation to Beta dropped off a cliff during the de-risking episodes in August and September for both 12-month Momentum and EPS Revisions. Russell 1000 Source: Axioma. Page 12 Deutsche Bank Securities Inc. we documented how this behavior could cause these strategies to accumulate overwhelming levels of exposure to Beta causing them to be vulnerable to strong and rapid shifts in risk appetite10. This indicates that their non-Beta component (e.18 April 2012 Portfolios Under Construction Momentum and Earnings Revisions In past research. . the dynamic nature of these strategies do not make them well suited to be analyzed from a time-series perspective and so cross-sectional measures are paramount to understanding their exposure to risk-appetite over time. both factors held up rather well. “Reviving Momentum. Russell. Mission Impossible?” and Alvarez et al 2010.g. S&P. “Neutralization and Beyond”. Last we note that a historical analysis of Beta exposure and correlation of both factors (Figure 17 and Figure 18) show that both factors exhibit very similar profiles to Beta over time. S&P. Deutsche Bank Quantitative Strategy However. Deutsche Bank Quantitative Strategy Jan-12 Jan-11 Jan-10 Jan-09 Jan-08 Jan-07 Jan-06 Jan-05 Jan-04 Jan-03 Jan-02 Jan-01 Jan-00 Jan-99 Jan-98 Jan-97 Jan-96 Jan-95 Jan-94 Jan-93 Jan-92 Jan-90 Jan-12 Jan-11 Jan-10 Jan-09 Jan-08 Jan-07 Jan-06 Jan-05 Jan-04 Jan-03 Jan-02 Jan-01 Jan-00 Jan-99 Jan-98 Jan-97 Jan-96 Jan-95 Jan-94 Jan-93 Jan-92 Jan-91 -100% Jan-90 -2. Deutsche Bank Quantitative Strategy Quality Typical factors used to characterize firm quality such as Return on Equity (ROE) and Earnings Dispersion also varied to some extent over 2011 and early 2012. Indeed. Russell.0 -1.5 -1.5 Source: Axioma. FY1 Earnings Dispersion (3M Avg).0 ROE ROE 80% FY1 EPS Dispersion Expected Correlation with Beta Portfolio Beta 0. S&P.5 Jan-91 Portfolio Beta 0.0 Source: Axioma.0 -0.5 -1. Russell.0 -1. S&P. this suggests that in aggregate.0 -0. Deutsche Bank Securities Inc. FY1 Earnings Dispersion (3M Avg). the historical analysis reveals that these factors can also exhibit different and varying Beta sensitivity over time. Russell.5 100% 80% Expected Correlation with Beta 1. analyst revisions are strongly tied to past stock return momentum. Figure 17: Portfolio Beta: Momentum (12M) and FY1 EPS Revisions factors. This is evident when looking at the exposure and correlation to Beta of these factors over time (Figure 19 and Figure 20). Figure 19: Portfolio Beta: ROE.5 FY1 EPS Dispersion 60% 40% 20% 0% -20% -40% -60% -80% -2. Russell 1000 Figure 18: Expected Correlation with Beta: Momentum (12M) and FY1 EPS Revisions factors. Indeed. S&P.0 0. the more recent period saw the correlation between ROE and Beta to increase to relatively high levels. Page 13 . Quality factors typically load up on safer assets with lower Beta and overall risk profiles.5 FY1 EPS Revision (3M Avg) -2. Russell 1000 Figure 20: Expected Correlation with Beta: ROE. This helps explain the outperformance of these factors during the slow de-risking in the beginning of 2011 as well as the flat performance during the stronger de-risking in August and September of 2011.5 0.18 April 2012 Portfolios Under Construction Momentum and FY1 EPS Revision factors had picked up on lower Beta stocks during the derisking in the summer of 2010. Russell 1000 1.0 60% 40% 20% 0% -20% -40% -60% FY1 EPS Revision -80% Momentum (12M) Momentum (12M) Source: Axioma. Russell 1000 100% 1. Deutsche Bank Quantitative Strategy Jan-12 Jan-11 Jan-10 Jan-09 Jan-08 Jan-07 Jan-06 Jan-05 Jan-04 Jan-03 Jan-02 Jan-01 Jan-00 Jan-99 Jan-98 Jan-97 Jan-96 Jan-95 Jan-94 Jan-93 Jan-92 Jan-91 Jan-90 Feb-12 Feb-11 Feb-10 Feb-09 Feb-08 Feb-07 Feb-06 Feb-05 Feb-04 Feb-03 Feb-02 Feb-01 Feb-00 Feb-99 Feb-98 Feb-97 Feb-96 Feb-95 Feb-94 Feb-93 Feb-92 Feb-91 Feb-90 -100% Source: Axioma. albeit factor performance for this style was more heterogeneous than that found for Value. Russell. 0 YoY EPS Growth 40% 20% 0% -20% -40% -60% EPS GROWTH (5Yr) -80% YoY EPS Growth Source: Axioma.). the Beta exposure and correlation analysis shown in Figure 21 and Figure 22 show that the two can exhibit significantly different risk profiles as measured by their exposure and correlation to Beta. S&P.0 -0.0 Source: Axioma. Russell.18 April 2012 Portfolios Under Construction Growth Two factors commonly used to describe firm Growth are year-over-year EPS growth (YoY EPS Growth) and five year EPS growth (EPS Growth 5yr. Deutsche Bank Quantitative Strategy Page 14 Jan-12 Jan-11 Jan-10 Jan-09 Jan-08 Jan-07 Jan-06 Jan-05 Jan-04 Jan-03 Jan-02 Jan-01 Jan-00 Jan-99 Jan-98 Jan-97 Jan-96 Jan-95 Jan-94 Jan-93 Jan-92 Jan-91 Jan-12 Jan-11 Jan-10 Jan-09 Jan-08 Jan-07 Jan-06 Jan-05 Jan-04 Jan-03 Jan-02 Jan-01 Jan-00 Jan-99 Jan-98 Jan-97 Jan-96 Jan-95 Jan-94 Jan-93 Jan-92 Jan-91 -100% Jan-90 -1. Russell 1000 100% 1. Fundamentally. Figure 21: Portfolio Beta: YoY EPS Growth and 5yr EPS Growth.5 60% Jan-90 Portfolio Beta 1. Russell 1000 Figure 22: Expected Correlation with Beta: YoY EPS Growth and 5yr EPS Growth. Deutsche Bank Quantitative Strategy Deutsche Bank Securities Inc.5 80% Expected Correlation with Beta 0.5 EPS GROWTH (5Yr) -1. S&P.5 0. However. Russell. the two are similar except that they try to capture different cycles. . Russell 1000 Source: Axioma. we can use the Beta correlation to the trailing earnings yield factor (FY0 Earnings Yield) as an auxiliary comparison metric. S&P. 11 Deutsche Bank Securities Inc. One last scenario is that Beta d Page 15 . the factor’s increase in correlation during the beginning of 2011 may be due to either high Beta stocks becoming cheaper relative to lower Beta stocks or to analyst increasing their estimates to higher Beta stocks. Deutsche Bank Quantitative Strategy One last interesting bit of insight we can get from Figure 23 is to observe the Beta correlations during the periods of strong de-risking and re-risking. Yet another likely scenario is that higher Beta stocks did indeed increase their EPS relative to lower Beta stocks. Figure 23: Expected correlation between FY1 Earnings Yield. For example. Therefore. the FY1 Earnings Yield factor analyzed in Figure 11 is a function of price and analyst FY1 EPS estimates. In addition. FY0 Earnings Yield and FY1 EPS Revisions and performance of the Beta portfolio. Note that during August derisking episode. analyst began their downward revisions of higher Beta stocks so much that it offset an increase the correlation that would happen naturally by strong de-risking episodes in which higher Beta stocks experience stronger decreases in price and consequently making them “cheaper” relative to their past (see Figure 13). Note that the moderately negative returns to Beta may have also had a slight impact on the rise in the FY1 correlation is suggested by the slight increase in the Beta correlation to the FY0 Earnings Yield factor11. Russell.18 April 2012 Portfolios Under Construction Factor Dynamics and Regimes We can also use the expected correlation to Beta measure to analyze factor dynamics by comparing the Beta alignment across factors related by similar fundamental measures. We can infer from Figure 23 that the increase Beta alignment of the FY1 Earnings Yield factor is mainly due to an increase in EPS revisions for higher Beta stocks as suggested by the increasing correlation of the FY1 EPS Revision factor. Russell. In addition. S&P. “Quantitative Tactical Asset Allocation” and Luo et al. 2012. Did VRP forecast changes in risk-appetite during 2011 and early 2012? The strong and persistent changes in risk appetite experienced in the summer and fall of 2011 and early 2012 provide a good test environment in which to analyze the link between VRP and risk appetite as well as its efficacy for market and Beta timing. 2012 “New Insights in Country Rotation”. Deutsche Bank Securities Inc. In this section. Simply. As the name implies. albeit recent predictive power is much stronger than the historical norm. Deutsche Bank Quantitative Strategy A rolling correlation analysis over time (Figure 25) shows that the predictive power observed in Figure 24 is consistent over time. the measure is considered to be a premium for the risk embedded in asset markets and it has been shown to have predicted power of equity market returns. there was only one month in 2011 when VRP got the forecast completely wrong (August 2011). we explore its efficacy for capturing strong and persistent changes in risk-appetite and show how to implement it for market or Beta timing. 12 Page 16 See Luo et al. Figure 24 overlays the VRP estimate from the prior month over the monthly returns to the market and Beta portfolios. country rotation and for asset allocation. In fact. Figure 24: VRP(t-1) versus Market and Beta (D10-D1) monthly return (DBQS universe) Source: Axioma. the measure can be thought as the difference between market implied variance and expected realized variance. we will investigate its efficacy for timing style factors and propose two simple strategies that use VRP to predict and select a set of style factors that outperform an equal weighted factor benchmark. The graph shows that VRP was able to forecast the Beta and Market portfolio quite effectively over this period. we have found that it has predictive power for equity market timing. Indeed in our prior research12 on VRP. .18 April 2012 Portfolios Under Construction VRP and style rotation Variance risk premium and risk appetite changes A topical and recurring theme in our research is what academics have dubbed the variance risk premium (VRP). Correlation with one-month lagged VRP (60-month rolling window) 50% 40% 30% 20% 10% 0% -10% Market -20% Beta (D10-D1) -30% Jan-12 Jan-11 Jan-10 Jan-09 Jan-08 Jan-07 Jan-06 Jan-05 Jan-04 Jan-03 Jan-02 Jan-01 Jan-00 Jan-99 Jan-98 Jan-97 Jan-96 Jan-95 Jan-94 Jan-93 Jan-92 Jan-91 Jan-90 -40% Source: Axioma. Deutsche Bank Quantitative Strategy See Alvarez et al. Deutsche Bank Quantitative Strategy 13 Deutsche Bank Securities Inc. Deutsche Bank Quantitative Strategy VRP as a proxy for macroeconomic risk One strand of the literature on VRP suggests that it is a risk premium for the macroeconomic uncertainty embedded in asset markets (see Londono. S&P. US DBQS universe. The predictive power for Market and Beta timing shown in Figure 25 lends credit to this hypothesis in that VRP shows to be a good predictor of changes in risk appetite as measured by the Beta and Market portfolios. Median Pairwise Stock Correlation within Sectors (24-month window) Source: Axioma. S&P. In a prior paper 13 . Another way we can verify the link between VRP and macroeconomic uncertainty is to link it to stock return correlation and the systematic component of the cross sectional dispersion of stock returns (aka the opportunity set). we showed how stock return correlation was linked to systematic component of cross-sectional return dispersion as well as macroeconomic uncertainty. “Correlation and Consequences”. Figure 26 and Figure 27 show both of these measures against the volatility of VRP and suggest that VRP volatility is strongly related to other measures we have shown are affected by macroeconomic uncertainty. Russell 1000.18 April 2012 Portfolios Under Construction Figure 25: Rolling 60-month correlation with one-month lagged VRP: Market and Beta Portfolios (Decile 10 minus Decile 1). 2012. Page 17 . [2011] or Bansal and Yaron [2004]). Note that high VRP volatility indicates high levels of uncertainty about market direction. S&P. 15% 0 25% 0 Volatility of VRP (24M Standard Deviation) Volatility of VRP (24M Standard Deviation) % Macro-related Opportunity Set Source: Axioma. Figure 26: Volatility of VRP versus Relative Macro- Figure 27: Volatility of VRP versus pairwise stock return correlation within sectors (Russell 1000) 30 25 40% 20 35% 15 10 30% 5 Volatility of VRP (24-month rolling std) 45% Relative Macro Oppotunity Set Volatility of VRP (24-month rolling std) 30 60% 55% 25 50% 20 45% 40% 15 35% 30% 10 25% 5 20% Median Pairwise Stock Correlation related Opportunity Set. Russell. Russell. Russell. 5% annualized and run the timing strategy on its own and then add it to the market portfolio. we set a volatility target of 2. captures the mean effect of both variables.VRPVRPt −1 + ε t In this equation RM . A simple way to get this estimate is to use linear regression14. Once these parameters are estimated.18 April 2012 Portfolios Under Construction Implementing the VRP strategy The results in the last sections suggest that VRP can be used for Beta and market timing purposes. t is the return to the market over t-1 to t. Then to rescale the forecasts we estimate the volatility of the strategy for k = 1 . where k is a constant that we will use to target a specific volatility or tracking error. but we leave a more sophisticated model for a future study. Specifically. t +1 = αˆ + βˆ RM . Now we choose a volatility target (or tracking error target) for the timing strategy σ Target .VRP and αˆ every month so a realistic backtest will follow the same practice. We can do this analytically through the model or empirically using a history of returns for the strategy for k = 1 . the trick is to convert the VRP today to a forecast of return for the next period. we can simply use OLS15.VRPVRPt In practice. the goal is to obtain the best forecast of forward return conditional on the current value of VRP. However. the regression estimate is optimal in the sense of being the linear unbiased estimator with the minimum amount of estimation error. we will update our estimates βˆ RM . The idea is to see how implement the VRP timing strategy in practice. An intuitive strategy is to allocate to the market portfolio in proportion to the forecasts so that our market weight Wt takes on the following form: Wt = k Rˆ M . we use the latter and call it σˆ k =1 . Now we have a realistic forecast of market return for each month that can be used for market timing and asset allocation among other uses.VRP is the beta of the forward market return to the current value of VRP.t = α + β RM . β RM . t + 1 In the next example. exactly how to use it in a strategy is not so clear. it is desired that higher forecasts imply higher market allocation and vice-versa. 15 The error does not satisfy all the properties for OLS optimal estimates. and ε t is a random error term. VRP is given in variance units so it is not directly a return forecast (alpha). To keep things simple. Therefore. One question that comes up is how much market or Beta exposure should one take given the level of VRP? In the following we propose a scheme to use it in a market or Beta-timing context. . t + 1 . the intercept. To estimate the β RM . α . Page 18 Deutsche Bank Securities Inc. and set k to: k = σ Target σˆ k = 1 So the weight set to the market portfolio for the timing strategy at time t is: Wt = σ Target σˆ k = 1 Rˆ M . then the forecast of the next period market return is: (2) Rˆ M . Scaling the forecast For market timing purposes. The dependent variable will be the market return over t-1 to t and the independent variable is the value of VRP at time t-1 as follows: (1) RM .VRP and α parameters in equation (1). 14 Under certain assumptions. 12 per.5% Volatility of VRP Timing Strategy Cumulative Return Monthly Return 300% 2% 0% 250% 200% 150% 100% -2% 50% -4% 0% Source: Russell.P. S&P. while Figure 29 shows the cumulative return of mixing the market portfolio with the same strategy. Deutsche Bank Quantitative Strategy Last we note that adding the VRP timing strategy to the market in a more optimal manner requires the estimation of the covariance matrix between the timing strategy and the market portfolio. In this section we analyze VRP’s potential for style-timing and develop a simple scheme using our correlation analysis from the prior section. Then we simply use the VRP forecast to turn-on or turn-off the factors..e. Beta-Correlation and VRP for timing style factors The results in the prior sections of this report suggest a simple scheme to time style factors using their exposure and correlation to Beta. the following question is whether it can only predict the Beta component implicit in style factors or whether it can predict more.P. while factors with negative correlation to Beta will have a tilt towards decreasing risk appetite. which we argued can be proxied by the return to a portfolio that is long high Beta and short low Beta stocks (i. An upcoming report will develop a more rigorous style rotation algorithm using VRP as well as other macroeconomic and capital market variables18. raw style factors may possess significant exposure to changes in risk-appetite. Page 19 . S&P. Decile 10 minus Decile 1).18 April 2012 Portfolios Under Construction Figure 28 shows the monthly returns to our VRP market timing strategy with a 2. Deutsche Bank Securities Inc.L. The latter two points suggest that VRP can be used for style-timing or style-rotation. (VRP Strategy with 2.5% annulized vol.5% target annual volatility) Figure 29: Market + VRP Market-timing strategy cumulative return (VRP strategy run at 2. Avg. We also saw that the variance risk premium (VRP) is quite adept at forecasting significant changes and reversals in investor risk appetite.L.) 350% 4% Market Market + 2.16 Figure 28: VRP Market-timing strategy monthly return (2. We leave the second question for a future report. 2011 “Robust Factor Models” or Alvarez et al.5% annulized vol. 16 The estimates for the parameters in the model were computed using an expanding window. The basic idea is that style factors exhibiting a positive expected correlation to Beta will have a tilt towards increasing risk appetite.5% annualized volatility target. If so. Then a rigorous signal weighting technique could be used to obtain an optimal mean-variance portfolio17 VRP for style rotation As we saw in the last section. 2011. Bloomberg L. “Driving in the fast lane”. This scheme will be used to turn-on and turnoff factors. 2010 “Style Rotation”). 17 See Luo et al. Deutsche Bank Quantitative Strategy Source: Russell. Mov.5% annual vol) 6% 400% VRP Strategy with 2.. 18 This will be an extension of our previous style rotation research and model (see Luo et al. Bloomberg L. especially during the past three years of unprecedented economic uncertainty. which can be done analytically or empirically. the performance subsequent to 2010 is significantly better for the correlation weighted model. Last. conversely for negative VRP we select the bearish set. To do this we center VRP by subtracting its mean over the last 12-months19. This is because the VRP-CW model tends to load up heavily on factors with higher/lower correlation to Beta given that we are targeting the Beat component of each of the style factors. 2010. Step 5: Build two models for the weights to the selected group.18 April 2012 Portfolios Under Construction The methodology for the style-timing scheme is outlined in the following 5 steps: Step 1: Set each factor to the same risk level using the factor portfolio historical volatility or a forecast from a risk model. The correlation weighted model will overweigh factors with higher correlation to Beta. We chose a 12-month window to capture faster dynamics at the cost of higher turnover and possibly more error. which as we have documented in past research is not an easy benchmark to beat20. we tried an expanding window. which is an implicit reference to those factors having greater implied alpha as referred to in our last section. The Sharpe ratios show that over the full period the VRP-EW performs the best in risk-adjusted space. Step 2: Identify the expected correlation of each style factor to Beta. but significantly outperforms in subsequent periods. 60. Specifically. 20 Page 20 See Luo et al. The results of the three models over different periods are shown in Figure 30. 48. We can also consider this to be a naïve version of risk adjustment in the absence of an optimizer. 19 The results are quite robust for different window lengths. In fact. which consists of those having positive expected correlation „ A “bearish” group consisting of those having negative expected correlation Step 4: Use the VRP forecast to select the bullish or bearish group of factors. Then for positive values of this centered VRP we select the bullish group of factors. . Our benchmark will be the equally weighted factor combination of the all the factors. 36 and 24 month windows and all showed similar results. “Portfolios Under Construction: Robust Factor Models”. The VRP-CW model shows similar performance to the benchmark (EQWGT) over the full history. especially post 2009. suggesting that level (not just the sign) of the correlation between factors is a strong predictor of factor outperformance. we note that the volatility of the VRP-CW strategy is significantly larger than both other strategies. EQWGT. VRP-EW: equal weighted: equal weight all the factors in the group chosen in Step 3 VRP-CW: correlation weighted: weight factors selected in Step 3 in proportion to the absolute value of their correlations Note that the risk scaling in Step 1 ensures that a factor with very high volatility does not dominate the model. Deutsche Bank Securities Inc. Step 3: Divide style factors into two groups: „ A “bullish” group. Russell.50 Jan 2000 – Mar 2012 13% 16% 30% 24% 21% 47% 0. Page 21 . this is consistent with both high correlations and higher systematic cross-sectional dispersion (Figure 26 and Figure 27). Price-toBook and Price-to-Sales were turned on throughout most of 2009. Also note that the VRP-EW strategy has the same active factors shown in the figure. Deutsche Bank Quantitative Strategy Source: Axioma. It has switched its allocation towards the more defensively positioned factors and styles such as Momentum and Quality. Mov. S&P. These factors were ripe for the re-risking that took place throughout that year since they had rotated towards higher Beta stocks that were made cheap during the 2008 de-risking. If we compare with the last few months in Figure 33. S&P. Avg. Deutsche Bank Securities Inc.33 1. S&P.61 0. Russell.58 0.53 0. Avg. Indeed.28 1.78 0. It is also worthwhile to note that Momentum has turned off during the more recent period. The factors that are turned on.70 Source: Axioma. the more recent period shows that VRP-CW strategy significantly outperformed the VRP-EW strategy. Note that the cells in blue indicate that the factor is turned off. The difference is that the VRP-EW strategy equally weights the active factors in the model.29 Jan 2010 – Mar 2012 5% 13% 45% 14% 12% 26% 0. Similarly the models had turned off Momentum during the risk-rally in the spring of 2009.63 Jan 2007 – Mar 2012 5% 16% 49% 18% 13% 44% 0. More importantly. In contrast.18 April 2012 Portfolios Under Construction Figure 30: Style rotation results for three style rotation models Period Mean (Annual) EQWGT Std. (VRP_EW) 40% 20% 20% 10% 0% 0% -20% -10% -40% -20% -60% -30% Source: Axioma. was turned off during the re-risking that hurt the factor in January 2012.21 1. However. This implies that the level of Beta mattered more during the more recent period. and interestingly enough has only appeared episodically since January 2009. Figure 31: VRP-CW style rotation model 60% Figure 32: VRP-EW style rotation model 30% VRP_CORR VRP_EW 12 per.14 1. the VRP-EW factor timing strategy outperformed the VRP-CW strategy. Mov. Dev. range from yellow to red. Deutsche Bank Quantitative Strategy The weights to the factors given by the VRP-CW strategy since January 2009 are shown in Figure 33. Note that in risk adjusted terms.12 Jan 2009 – Mar 2012 -6% 13% 56% 18% 12% 44% -0. we find that VRP has switched more bearish sentiment as is illustrated above in Figure 24. depending on the intensity of the absolute value of the correlation to Beta.04 1. (VRP_CORR) 12 per.40 1. Russell. (Annual) VRP-EW VRP-CW EQWGT Sharpe Ratios VRP-EW VRP-CW EQWGT VRP-EW VRP-CW Jan 1992 – Mar 2012 11% 11% 21% 21% 18% 42% 0. Finally Figure 34 shows the VRP-CW factor weights along with factor expected correlations to Beta. Deutsche Bank Quantitative Strategy Figure 31 and Figure 32 show the return series to both VRP factor timing strategies. 156504 0 0 0 0 0.162924 0.135089 0 0 0 Source: Axioma.220262 0. Deutsche Bank Quantitative Strategy Page 22 Deutsche Bank Securities Inc.12725 0 0 0 0.095594 0.22833 0 0 Mar-11 0.178586 0 0 0 0 0.202833 0.222494 0 0. Figure 34: VRP-CW factor weights and expected Beta correlations for April 2012 25% 20% Weights for April 2012 80% 20% 20% 71% Correlations 59% 60% 45% 19% 40% 35% 17% 15% Weight 13% 10% 10% Correlation 20% 0% -6% -20% -40% -45% -60% 5% 1% 0% 0% 0% 0% -58% -80% -75% -84% 0% -100% -90% -91% Source: Axioma.135135 0 0 0 0.264078 0.267429 0 0 Nov-11 0 0.165365 0 0.125418 0.311962 0.111048 0.170959 0.14783 0 0 0 0 0. Russell.088089 0 0 0 0. S&P.294093 0 0.239542 0.073938 Feb-11 0 0 0.229961 0 0 0 0.163803 0 0 0 0. Russell.400082 0 0.276759 0.069836 0 Jul-11 0.258983 Sep-11 0.155782 0.236637 0.274596 0 0 0 0 0.032063 0 0 0.271174 0 0. S&P.286247 0 0 0 0 0. Bloomberg LLP.077767 0 Oct-11 0 0.200319 0 0.285303 0 0 0 0 0.279379 0.00668 0.184169 0.357995 0.314953 0 0 0 Aug-11 0 0. Deutsche Bank Quantitative Strategy Factors and correlations for April 2012 The VRP-CW implied factors weights and the expected factor correlations with Beta are shown in Figure 34 for April 2012. The weights suggest that the 12-month centered VRP factor has taken on a slight “bearish” sentiment (see Figure 24).118958 0 0 0 0.273484 0.166944 0.1555 0.013361 0.134873 0.169812 0 May-11 0.27508 0.242979 0.36996 0 0.191029 0 Apr-11 0.212653 0.294686 0 0 0.192028 0.130059 0 Jun-11 0.163218 0.246433 0 0.177604 0.243417 0.340666 0 0.18 April 2012 Portfolios Under Construction Figure 33: Factor loadings for the VRP-CW model January 2009 – March 2012 FY1 Dividend Yield (RHS) FY1 Earnings Yield Price-to-Book Price-to-Sales Momentum (12M) Momentum (6M) ROE FY1 EPS Dispersion YoY EPS Growth EPS GROWTH (5Yr) FY1 EPS Revision Jan-09 17% 15% 35% 28% 0% 0% 0% 0% 0% 6% 0% Feb-09 20% 21% 31% 27% 0% 0% 0% 0% 0% 0% 0% Mar-09 23% 13% 33% 31% 0% 0% 0% 0% 0% 0% 0% Apr-09 22% 0% 41% 36% 0% 0% 0% 0% 0% 0% 0% May-09 4% 0% 52% 44% 0% 0% 0% 0% 0% 0% 0% Jun-09 0% 0% 53% 47% 0% 0% 0% 0% 0% 0% 0% Jul-09 0% 0% 41% 38% 0% 22% 0% 0% 0% 0% 0% Aug-09 0% 0% 37% 35% 0% 27% 0% 0% 0% 0% 0% Sep-09 0% 0% 31% 29% 0% 40% 0% 0% 0% 0% 0% Oct-09 0% 0% 28% 28% 0% 44% 0% 0% 0% 0% 0% Nov-09 0% 0% 36% 31% 0% 33% 0% 0% 0% 0% 0% Dec-09 11% 20% 0% 0% 0% 0% 21% 20% 18% 8% 2% FY1 Dividend Yield (RHS) FY1 Earnings Yield Price-to-Book Price-to-Sales Momentum (12M) Momentum (6M) ROE FY1 EPS Dispersion YoY EPS Growth EPS GROWTH (5Yr) FY1 EPS Revision Jan-10 10% 20% 0% 0% 0% 0% 21% 20% 17% 8% 4% Feb-10 12% 22% 0% 0% 0% 0% 23% 22% 17% 5% 0% Mar-10 13% 19% 0% 0% 0% 0% 22% 22% 16% 9% 0% Apr-10 15% 16% 0% 0% 0% 6% 20% 21% 16% 6% 0% May-10 17% 18% 0% 0% 0% 0% 22% 23% 12% 8% 0% Jun-10 16% 20% 0% 0% 0% 0% 22% 23% 6% 13% 0% Jul-10 0% 0% 25% 27% 27% 0% 0% 0% 1% 0% 20% Aug-10 17% 19% 0% 0% 0% 7% 20% 22% 0% 15% 0% Sep-10 0% 0% 23% 23% 1% 0% 0% 0% 29% 0% 23% Oct-10 0% 0% 17% 26% 0% 0% 0% 0% 35% 0% 23% Nov-10 20% 12% 0% 0% 0% 17% 18% 20% 0% 13% 0% Dec-10 0% 0% 28% 38% 4% 0% 0% 0% 29% 0% 0% FY1 Dividend Yield (RHS) FY1 Earnings Yield Price-to-Book Price-to-Sales Momentum (12M) Momentum (6M) ROE FY1 EPS Dispersion YoY EPS Growth EPS GROWTH (5Yr) FY1 EPS Revision Jan-11 0.312195 0 0.202718 0. .084403 0.331006 0 0.03068 0 0 0 0.019851 0.315973 0 0.356969 0 0 Dec-11 Jan-12 Feb-12 Mar-12 0.229495 0.150566 0 0 0 0.217863 0.148297 0 0 0 0 0. R.. J. June 16. Jussa. “Portfolios Under Construction: Volatility=1/N”. available at SSRN: http://ssrn. Bansal... Luo. M. Alvarez. 2005. Luo et al. [2011]. R.. J.. [2011]. Jussa. R. January 21... “Interpretable Asset Markets?”. Cahan. J. Deutsche Bank Quantitative Strategy.. Deutsche Bank Quantitative Strategy. Deutsche Bank Quantitative Strategy. “Signal Processing: Quant Tactical Asset Allocation (QTAA)”. V... Deutsche Bank Quantitative Strategy. Khatchatrian. (2011).. R. Cahan.. M.. “Portfolios Under Construction: Robust Factor Modeling”. R. September 7.. 2011. [2010]. Deutsche Bank Quantitative Strategy. [2011]. R.. 2012. Alvarez.. [2010]. M. Deutsche Bank Quantitative Strategy.. Jussa. J. J.. [2010]. Jussa. J. M. Sheng. Londono. Chen.. September 19. Cahan. Y. Page 23 . 2011. “QCD Model: DB Quant Handbook”.. Cahan. Chen. Alvarez. 2011. Jussa.. 2011.. R.. [2012]. Luo. J.. Deutsche Bank Quantitative Strategy. Chen. July 6. [2010]. Alvarez. Y. R. April 26. Luo. Alvarez. J... Y. Y. J.. M.. “Signal Processing: New Insights in Country Rotation”. J. A. J. 1035. January 24. A. Y. Alvarez. 22 July 2010. September 2012. Luo. M. R. J. 2010...M. Cahan. Luo. FRB International Finance Discussion Paper No. 2010. Mesomeris. Chen. “Factor Neutralization and Beyond”. and Yaron. M. European Economic Review. Luo. Alvarez. W.. 2010. “The Variance Risk Premium Around the World”. Jussa. J.. S. Y. Jussa. Chen. Cahan.. M. “Portfolios Under Construction: Driving in the fast lane”. 49. “Signal Processing: Style Rotation”. Chen. Jussa.. Y. 2010. J. Luo. Kassam. Vol.. “Signal Processing: Reviving Momentum: Mission Impossible?”. J.. [2011].. Deutsche Bank Quantitative Strategy. M.. Cahan..18 April 2012 Portfolios Under Construction References Alvarez. “Portfolios Under Construction: Correlation and Consequences”..com/abstract=2009065 Deutsche Bank Securities Inc. September 21. Y.. Alvarez. Cahan. Luo. Taking into account historical events the backtesting of performance also differs from actual account performance because an actual investment strategy may be adjusted any time. for any reason. economic or market factors.db. No representation is made that any trading strategy or account will or is likely to achieve profits or losses similar to those shown.com/ger/disclosure/DisclosureDirectory. from the analysis. brokerage or other commissions. Actual results will vary. Page 24 Deutsche Bank Securities Inc. please see the most recently published company report or visit our global disclosure look-up page on our website at http://gm. The backtested performance includes hypothetical results that do not reflect the reinvestment of dividends and other earnings or the deduction of advisory fees. .eqsr. hypothetical or simulated performance results have inherent limitations. and any other expenses that a client would have paid or actually paid. Miguel-A Alvarez/Yin Luo/Rochester Cahan/Javed Jussa/Zongye Chen/Sheng Wang Hypothetical Disclaimer Backtested. Past hypothetical backtest results are neither an indicator nor guarantee of future returns. perhaps materially. simulated results are achieved by means of the retroactive application of a backtested model itself designed with the benefit of hindsight. Alternative modeling techniques or assumptions might produce significantly different results and prove to be more appropriate.18 April 2012 Portfolios Under Construction Appendix 1 Important Disclosures Additional information available upon request For disclosures pertaining to recommendations or estimates made on a security mentioned in this report. 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Prior to buying or selling an option investors must review the “Characteristics and Risks of Standardized Options. other financial products or issuers discussed in this report is available upon request.com/components/docs/riskstoc. the NASD. As a result of the high degree of leverage obtainable in futures trading.Disclaimer The information and opinions in this report were prepared by Deutsche Bank AG or one of its affiliates (collectively “Deutsche Bank”). may take a view that is inconsistent with that taken in this research report. We believe that such policy will allow us to make best use of our resources. a member of the London Stock Exchange and regulated by the Financial Services Authority for the conduct of investment business in the UK and authorized by the BaFin.db. for which it receives compensation. foreign or domestic. sell to or buy from customers on a principal basis. this report. manager. in a manner inconsistent with the view taken in this research report. 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