Multifactor rules-based portfolios portfolios
|
|
- Karin Blair
- 6 years ago
- Views:
Transcription
1 JENNIFER BENDER is a managing director at State Street Global Advisors in Boston, MA. jennifer_bender@ssga.com TAIE WANG is a vice president at State Street Global Advisors in Hong Kong. taie_wang@ssga.com Can the Whole Be More Than the Sum of the Parts? Bottom-Up versus Top-Down Multifactor Portfolio Construction JENNIFER BENDER AND TAIE WANG Multifactor rules-based portfolios portfolios constructed to capture multiple factor exposures have become increasingly popular in recent years. The main rationale for combining multiple factors is that it enables investors to achieve potential diversification benefits. Historically, factors such as value, size, quality, low volatility, and momentum have earned a long-run premium over the market, but all factors have experienced periods of underperformance during certain market environments. However, they have not all experienced periods of underperformance at the same time. Some factor pairs such as value and momentum naturally diversify each other based on their definitions. When a stock s price rises, it simultaneously becomes more momentumlike (as long as its price is rising faster than others) and less value-like (because value is typically defined as book-to-price, earningsto-price, or some other fundamental-to-price ratio). Other factors tend to diversify each other historically based on cycles of investment sentiment; quality and low volatility stocks are favored by investors in times of uncertainty. Given the interest in multifactor portfolios, there has been much discussion about the best way to build them. One often-asked question is whether it is better to combine single-factor portfolios or to build a multifactor portfolio from the security level. The latter is a better approach theoretically because each security s portfolio weight will depend on how well it ranks on multiple factors simultaneously. The former approach, combining single-factor portfolios, may miss the effects of cross-sectional interaction between the factors at the security level. But it may be that the differences between the two approaches are small and the combination approach has benefits such as more transparent performance attribution and greater f lexibility. In this article, we explore this issue and discuss the implications of both approaches. Our conclusion is that there are, in fact, beneficial interaction effects among factors that are not captured by the combination approach. Both intuition and empirical evidence favor employing the bottom-up multifactor approach. THE RISE OF RULES-BASED INVESTING The earliest alternative indexing strategies (also known as advanced beta or smart beta) applied an alternative weighting scheme to market-capitalization weighting. Examples include GDP-weighted portfolios in the 980s, equal-weighted portfolios in the 990s, and, more recently, fundamental-weighted portfolios in the 2000s. Proponents of these strategies were typically critical of cap-weighting Special Issue 206 The Journal of Portfolio Management 39
2 and argued that these alternative weighting schemes were superior because they were either more representative of investment value or more diversified. Since 2008, an alternative method of constructing such portfolios has emerged one that focuses on capturing factor premiums more explicitly. This approach focuses on the pure factors that the portfolios are exposed to and derive their returns from. The pure factors, beginning with the multifactor models of Ross [976], are those that have been widely researched in the academic literature, have strong theoretical foundations, and have exhibited persistence over multiple decades. Viewing factor indexation as a means of capturing pure factors is consistent with the way academics have viewed factors; it was most widely popularized by Fama and French s seminal three-factor model and extended over the years by countless others. The most widely discussed factors include the original Fama French Carhart factors value, (low) size, momentum plus a handful of additional factors that have received moderate treatment (low) volatility, quality, liquidity, and yield. Single-Factor Portfolio Construction A number of techniques have been used to build single-factor portfolios. Equal weighting, GDP weighting, fundamental indexation, and its close companion wealth weighting were compelling; they were intuitive and did not employ a black box algorithm such as optimization, which meant security weights could be directly tied to the securities observable characteristics. Subsequent factorbased approaches could also be constructed in a similar fashion; most employ a set of rules that specify security weights as a function of the factor characteristics. Consider the following examples of first- generation rules-based portfolios equal weighting, fundamental indexation, and risk weighting: Equal weights: wi = () N Fi Fundamental indexationweights : w i = N (2) Fi 2 σ i Risk weights: w i = N (3) 2 i σi In Equations 3, w i is the weight of stock i in the portfolio, N is the number of stocks in the universe, F i is the fundamental value of stock i (e.g., book value, i earnings, etc.), and σ i 2 is the variance of stock i. All the methods are relatively straightforward, linking the desired attribute (e.g., volatility or book value) to the securities weights. Our preferred approach has been to adopt a benchmark-relative framework in which multipliers are applied to market-cap weights. For tilted factor portfolio weights, w = w γ i i, mktcap i (4) where γ i is a scalar applied to the market-cap weight of each stock. The scalar γ i can be specified in many ways. It can be the result of a mapping function based on the security s factor characteristics. It can be nonlinear or linear cross-sectionally, and it can be unique for each security or unique for groups of securities. Furthermore, we can screen out certain securities by setting the multiplier equal to zero. We have favored this approach because it allows flexibility and coherency for building portfolios across different factors and at different levels of tracking error and concentration for benchmark-sensitive investors (see Bender and Wang [205]). MULTI-FACTOR PORTFOLIO CONSTRUCTION Combining multiple factors with strong investment merit can produce benefits from the potential diversification among the factors. Historically, factors such as value, size, quality, low volatility, and momentum have exhibited substantial diversification benefits over short horizons such as a week or a month, but also over longer, multiyear periods. Correlations between excess returns are generally below 0.5 and sometimes negative (see Bender, Brandhorst, and Wang [204] for further discussion). Multifactor portfolios can be constructed in two main ways. The simplest way is to combine single-factor portfolios into one portfolio. Within each factor portfolio, security weights are determined according to a specific methodology (ideally, one that is consistent across factors). Single-factor portfolios are blended by assigning weights to individual portfolios. This approach is analogous to building blocks, and its benefits include clear performance attribution and f lexibility in reallocation across factors. For the remainder of this article, we refer to this as the combination approach. 40 Bottom-Up versus Top-Down Multifactor Portfolio Construction special Issue 206
3 The second way to build the portfolios is from the security level up, bottom up, by incorporating all the factor characteristics simultaneously. Security weights are assigned based on the security s combined characteristics. Asness [997], for example, highlighted the effects of interaction between value and momentum. The top-down combination approach may miss these interaction effects. In our example, we construct portfolios by multiplying the starting weights (e.g., cap weights) with a multiplier, as in Equation 4. Note that if we were employing optimization, these two approaches would not yield the same results. (The typical objective function is a quadratic function; because it is nonlinear, a linear combination of factor portfolios cannot be equivalent.) We focus on non-optimized portfolios, and therefore it is less clear if there are differences and how large those differences are in actuality. A STYLIZED EXAMPLE Let us begin with a stylized example using 0 stocks to construct a three-factor portfolio. We consider three representative factors: dividend yield, book-to-price ratio, and return on assets. To build the portfolio, we rank the securities based on each security s factor characteristics and assign multipliers based on their ranks. The multipliers can be assigned to any set of starting weights; market-cap weights and equal weights are our candidates. The new weights (starting weight x multiplier) are then rescaled to sum to 00%. What are the conditions under which the bottom-up and combination approaches will yield the same results? The starting weight is equal weight. The sums of the multipliers for each single-factor portfolio and for the bottom-up factor portfolio are identical. We show this as follows: If there are two-factor portfolios, the resulting weights for a linear combination of two-factor portfolios can be calculated as W M S L = λ + ( ) M S L Factor Factor 2 S S (5) where m m2 Multipliers for factor : M = ; Multipliers. l m n for factor 2: l2 L =. l n s Starting weights: s2 S = ; Final weights:. w s n w2 W =. w n Weight in factor : λ Weight in factor 2: λ The symbol denotes element-wise product. The resulting weight for the bottom-up, twofactor portfolio is λ + ( )L = S W λ + ( )L S (6) For Equations 5 and 6 to be identical, the necessary conditions are as follows: s = s 2 =... s n, (7) l li. (8) The derivation appears in Appendix A. To illustrate this equivalence, we built the following two portfolios (as shown in Exhibit ). Combination Portfolio: In the two left-most panels, we sorted the securities based on raw metrics one factor at a time and ranked them. We assigned multipliers identical to the stock s rank (e.g., a stock ranked fifth has a multiplier of 5). The multipliers were applied to equal weights (e.g., Special Issue 206 The Journal of Portfolio Management 4
4 E x h i b i t Scenario in Which the Combination Portfolio Is Equivalent to the Bottom-Up Portfolio each security s weight is 0%). We then rescaled the weights to sum to 00%. Next, we blended the three resulting portfolios into one using equal weights that is, we averaged the security weights across the three portfolios. Bottom-Up Portfolio: In the right-most panel, we ranked the securities for each factor, as we did for the combination portfolio. This time, we computed an average (equally weighted) rank across the three factors and multiplied this combined rank by security equal weights (0%). We then rescaled the weights to sum to 00%. The bottom-up multifactor portfolio is identical to the combination portfolio shown in Exhibit. Note that the multiplier does not capture information on the cross-sectional distribution (e.g., each security receives one unique multiplier, no two multipliers are the same; the sums of the multipliers are identical), and the starting weights are equal weights. We next replaced the starting weights with market-cap weights instead of equal weights. As summarized in Exhibit 2, the correlation between the weights of the combination and bottom-up portfolios is exactly, but the actual weights of the two portfolios are no longer identical. Applying the multipliers to cap weights creates differences due to the way the excess weight (i.e., the difference between the sum of the weights and the 00% target weight) is allocated. If equal weights are used as starting points, this excess weight is uniformly distributed across the 0 securities. But when cap weights are used, the excess weight is prorated based on the market-cap weights; larger-cap, higher-ranked securities receive more of that excess weight. Because the two approaches distribute the excess weight differently, the final combination and bottom-up portfolios are not the same. That said, we interpret the correlation of to mean that the essence of the portfolios is comparable. The ordering of the security weights is identical in the two portfolios. A SECOND STYLIZED EXAMPLE What would happen if we used scores and not ranks? To calculate scores for each factor, we standardize the raw metrics for each security by subtracting the mean and dividing by the standard deviation across securities. Thus, 42 Bottom-Up versus Top-Down Multifactor Portfolio Construction special Issue 206
5 E XHIBIT 2 Summary of Rank-Based Approach xi x (9) σ where x i is the security s raw characteristic, x is the average across all securities, and σ is the standard deviation of the raw characteristics across all securities. Scores preserve the distributional characteristics of each factor in a way that ranks do not. If a security has an extremely high price-to-book relative to the other securities, that extremeness will be captured by the score. Once we assign scores to the securities, how do we determine the multiplier? Because scores can be negative (e.g., 5 to +5), we cannot just set multipliers equal to scores, as we did for the ranks. 2 Keeping it simple, we assigned ranks based on scores and set the multipliers equal to the ranks. Note that ranking based on scores is equivalent to ranking based on raw metrics in the combination approach. However, in the bottom-up approach, average rank is not the same as a rank based on average score. This is a critical point. In this example, the multiplier applied to each security for the bottom-up portfolio is no longer just the average of the multipliers for the single-factor portfolios. That is, we changed the way we specify multipliers; Equation 5 still holds, but Equation 6 does not. Exhibit 3 summarizes the difference between the rank-based approach and the score-based approach. When we use scores instead of ranks, the two portfolios weights are meaningfully different, and the correlations are not whether we apply the multipliers to equal weights or cap weights. Why does the score-based approach create a larger difference between combination and bottom-up portfolios? In this example, the score-based approach preserves the distributional differences in a way that the rank-based approach does not. Does employing a rank-based method rule out the ability to capture these interaction effects between factors? In this stylized example, it did, because of the way we had specified the multipliers (i.e., one unique multiplier for each unique rank). However, there are portfolio construction methods that use ranks and do capture interaction effects (see Brandhorst [203]). GLOBAL PORTFOLIO SIMULATIONS To test the impact of combination versus bottom-up portfolio construction with an actual investment universe, we consider a four-factor portfolio capturing value, low volatility, quality, and momentum (as commonly defined in existing literature). 3 The two multifactor portfolios were constructed as follows: Combination Portfolio: For the combination portfolio, we created single-factor portfolios for the four factors: value, low volatility, quality, and momentum. First, we calculated the security scores for each factor and sorted the securities based on those scores. Second, we grouped the securities into 20 subportfolios, in which each subportfolio held 5% of market-cap weight. 4 We applied a fixed set of multipliers (linearly interpolated between 0.05 and.95 in increments of 0.0) to the subportfolios; the multiplier was applied to the market-cap weight of the security, depending on which subportfolio it fell in. The multipliers are shown in Exhibit 4. Finally, we rescaled the weights so that they summed to 00%. The combination portfolio is an equally weighted average of the four single-factor portfolios. Portfolios are rebalanced monthly. Bottom-Up Portfolio: First, we assigned scores to securities for each factor. Second, we averaged the scores (equally weighting the factors). Third, we grouped the securities into 20 subportfolios, Special Issue 206 The Journal of Portfolio Management 43
6 E x h i b i t 3 Bottom-Up vs. Combination Method: Rank vs. Score-Based Approach, December 3, 204 (0-stock example) E x h i b i t 4 Multipliers Assigned to Subportfolios in Simulations Notes: The starting weight is 5% for each subportfolio. Because each subportfolio holds 5% of market cap, this is equivalent to holding market-cap weights. each holding 5% of market cap, based on their average scores. We then applied the same fixed set of multipliers, depending on the subportfolio the security fell in. Finally, we rescaled the weights so that they summed to 00%. The factor definitions, universe, and rebalancing frequency are the same as previously. Exhibit 5 shows the results of the simulations. The bottom-up portfolio returns are higher than any of the underlying component factor returns and higher than the combinations. The difference is not insignificant a spread of 86 basis points. Moreover, the volatility of the bottom-up portfolio is significantly lower, and risk-adjusted return increases from 0.73 in the combination portfolio to 0.84 in the bottom-up approach. The differences between the two portfolios should not come as a surprise to us. Consider the case in which we group stocks into quartiles for value, momentum, 44 Bottom-Up versus Top-Down Multifactor Portfolio Construction special Issue 206
7 E XHIBIT 5 Combination vs. Bottom-Up Approach, Four-Factor Portfolios, January 993 March 205 (gross USD returns) Notes: Average historical turnover for the portfolios ranges from 30% to 70% (one-way annual) the lowest for low volatility and the highest for momentum. The bottom-up portfolio has exhibited 46% average annual one-way turnover for the period shown. Turnover in smart beta strategies is largely driven by the rebalancing frequency. Here, our choice to deploy quarterly rebalancing (to incorporate momentum, which requires the higher rebalancing frequency to be effective) causes turnover to be higher than the 5% 25% range typically seen in smart beta strategies. Semi-annual and annual rebalancing frequencies for non-momentum factors is more the norm. and quality for a global developed market universe. We assigned scores based on the quartiles, 5 and then we summed the scores for two pairs: value quality and value momentum. 6 Stocks with a score of zero rank the worst on both factors simultaneously; stocks with a score of 6 rank the best on both factors simultaneously. It is apparent in Exhibit 6 that the distribution for value quality is quite different from value momentum; for example, a greater percentage of stocks rank the highest on both value and momentum than on value and quality. These are the interaction effects captured by the bottom-up approach. In our backtest, has the bottom-up approach consistently produced better performance over the combination approach in all periods? Exhibit 7 shows the 36-month excess rolling returns for the two portfolios in Exhibit 5, as well as the rolling excess difference in returns. The bottom-up approach underperformend only during a short period in the early 200s. THE IMPACT OF STOCK SCREENING OR SELECTION How would the results change if we were to remove, or screen out, a subset of securities from the starting universe? Stock screening (also sometime referred to as stock selection) is equivalent to assigning a multiplier of zero in our framework. In other words, the bottom-ranked securities are not held but are merely E XHIBIT 6 Interaction Effects Captured in Distribution of Aggregate Scores, May 205 underweighted. Intuitively, we would not expect the impact of stock screening to change our hypothesis that the interaction effects captured by bottom-up techniques may have a meaningful impact on the final multifactor portfolio. To test this, we repeat the simulations in the previous section, but this time we employ stock screening. The only change is to remove the bottom 5 subportfolios; the same multipliers shown in Exhibit 4 are applied to the top 5 subportfolios. The results, shown in Exhibit 8, confirm our expectation that the bottom-up approach captures interaction effects in a way that the combination does not, even when stock screening is employed. Special Issue 206 The Journal of Portfolio Management 45
8 E x h i b i t 7 Time Variation in the Performance of the Bottom-Up vs. Combination Approaches There are in fact several ways to introduce stock screening in bottom-up multifactor portfolio construction. To understand what occurs under different forms of screening, imagine stocks arrayed along two factor dimensions, as shown in Exhibit 9. In our bottom-up simulations, we ranked stocks along each dimension and then combined the two dimensions while simultaneously removing the bottom-ranked stocks. This is shown in Exhibit 9, Diagram A (for illustrative purposes, we removed the bottom half, rather than the bottom quarter, in the simulations). In the combination approach, we ranked stocks along each dimension, creating one factor portfolio at a time by deleting the lowest-ranked stocks and then combining the two-factor portfolios. This is shown in Diagram B of Exhibit 9. The third approach, shown in Diagram C, is to take the intersection of the two factors. This is another valid approach to creating a bottom-up portfolio that accounts for the interaction effects between factors. INTERACTION EFFECTS FOR DIFFERENT FACTOR COMBINATIONS In this last section, we drill down into the interaction effects by pair. We evaluate the following six pairs: momentum quality momentum low volatility quality low volatility value momentum value quality value low volatility 46 Bottom-Up versus Top-Down Multifactor Portfolio Construction special Issue 206
9 E XHIBIT 8 Combination vs. Bottom-Up Approach: Four-Factor Portfolios, January 993 March 205 (gross USD returns) E XHIBIT 9 The Impact of Screening Exhibit 0 first plots joint distributions, similar to Exhibit 6. We grouped stocks into quartiles for value, momentum, and quality. We assigned scores based on the quartiles and summed the scores for each pair. As before, stocks with a score of zero rank the worst on both factors simultaneously; stocks with a score of 6 rank the best on both factors simultaneously. There are clear distributional differences between the pairs the most distinct distributions being value quality and value momentum. Note that these two factors also have the lowest correlations among all the factors. We repeated the simulations shown in the previous section, but this time for each pair at a time. Based on the distributions shown in Exhibit 0, we expect value quality and value momentum to show the largest performance differences between combination and bottom-up approaches. Exhibit shows the backtested performance of the pairs, and it corroborates what we expect based on the joint distributions. Moreover, there is surprising consistency across the pairs; the bottom-up approach universally outperforms the combination approach. Special Issue 206 The Journal of Portfolio Management 47
10 E x h i b i t 0 Joint Distributions for Factor Characteristics, January 993 March 205 (gross USD returns) 48 Bottom-Up versus Top-Down Multifactor Portfolio Construction special Issue 206
11 E XHIBIT Combination vs. Bottom-Up Differences by Factor Pair CONCLUSION Indexed-factor portfolios have emerged in recent years as an alternative for investors dissatisfied with market-cap weighting or as an explicit way to achieve exposure to well-known factors that have been shown to drive stock returns. These portfolios are passively implemented and thus retain the same benefits as traditional passive investing transparency, implementation efficiency, and low costs. These innovations are changing the investment landscape, which until recently was characterized by traditional passive investing and active management. Portfolios that utilize multiple factors have emerged as a way of accessing the diversification opportunities inherent across factors. Multifactor portfolios can be constructed either by combining individual singlefactor portfolios or by creating bottom-up portfolios in which security weights are a function of multiple factors simultaneously. We evaluate the two approaches in this article and show that a bottom-up approach will produce different, and sometimes superior, results than will a combination of individual single-factor portfolios. This is because bottom-up approaches capture nonlinear cross-sectional interaction effects between factors that combination approaches do not. We evaluate wellknown factors such as value, quality, low volatility, and momentum and find that the bottom-up approach results in higher excess returns over the long run. We further find that there are differences in the joint distributional characteristics of various pairs. The most distinct differences arise for the value quality and value momentum pairings where the difference between bottom-up and combination portfolios is the starkest. A PPENDIX A EQUIVALENCE OF BOTTOM-UP AND COMBINATION PORTFOLIOS The combination of a two-factor portfolio is M S L S W =λ + ( ) M S L S Factor Factor 2 Note that denotes element-wise product. The bottom-up two-factor portfolio is λ + ( )L = S W λ + ( )L S (A-) (A-2) The following two conditions that must be satisfied for these to be the same: s = s2 =... s n (A-3) l li (A-4) Here is the proof: If s = s 2 = s n and it is also true that i ll, then M S = L S, ( ) λ ( ) λ + ) S = λ S+ L S = M S = L S (A-5) We substitute Equation A-5 in Equation A- as follows: Special Issue 206 The Journal of Portfolio Management 49
12 M S λ ( ) + λ L S =λ M S M S L S λ M + λ L S Factor Factor 2 ( ) + λ ( ) ( ) ( ) = λ M S + λ L S λ M + λ L S ( ) ( ) λ + λ = M L S λ M + λ L S ( ) LS ( ) M S [ ] λ + λ M S = λ M ( ) + ( λ ) L S M S M S M S Factor Factor 2 λ M + λ L S λ M + ( λ) L S ( ) = λ M + λ L S M S M S (A-6) (A-7) (A-8) (A-9) It should be noted that the condition M S = L S is all that is required for the two equations to be equivalent. Thus, if Equation A-3 were not to hold, there could still be a set of multipliers such that M S = L S; likewise, if Equation A-4 were not to hold, there could still be a set of starting weights such that M S = L S. ENDNOTES The authors would like to thank Xiaole Sun for her many contributions to this research and Scott Conlon and Ana Harris for their many insights. We are also grateful to Marc Reinganum for providing the idea behind the article s title. We note that this reweighting effect is an artifact of the multiplier-based framework. By determining weights in this way in Equation 4, we introduce this effect, which is not present in the equal-weighting or fundamental-weighting approaches. 2 Note that the issue of transforming scores to weights is a widespread one in heuristic rules-based portfolios. Index vendors have proposed various mapping functions to deal with this transformation, including linear and nonlinear functions. Probability-based functions have also been suggested. 3 Value measured as exponentially weighted five-year averages of earnings, cash flow, sales, dividend, and book value in the denominator and price in the numerator. The five price-fundamental ratios are equally weighted. Low volatility is measured as 60-month variance of returns. Quality is measured as return on assets, debt to equity, and five-year variability in earnings per share. These three measures are equally weighted. Momentum is measured as trailing 2-month return minus the last month s return. Size is measured as free-f loat market capitalization in U.S. dollars. 4 In the previous section, we applied a unique multiplier to each security. When we implement the multiplier-based approach to real portfolios, we apply multipliers to groupings, or subportfolios, of securities, rather than at the stock level (e.g., a unique multiplier for each security). Intuitively, this reduces the impact of security-specific noise, because our objective is to tilt in aggregate toward securities with the desired factor characteristics. (Factor effects are not generally monotonically increasing, i.e., for each incremental increase in book-to-price, there is not an incremental increase in return.) The number of subportfolios we decide to employ is relatively arbitrary because there is no theoretical relationship between the level of granularity and the level of noise. Our aim is to balance the objective of reducing noise with the desire to sufficiently differentiate the weighting scheme across securities, because the latter occurs as we reduce granularity. 5 The least desirable Quartile receives a score of zero; Quartile 2 receives a score of, etc. 6 The data are shown as of May 20, 205. Momentum is defined as previously discussed. Value here is approximated by annual price-to-book ratio, and quality is approximated by return on equity. REFERENCES Asness, C. The Interaction of Value and Momentum Strategies. Financial Analysts Journal, Vol. 53, No. 2 (997). Bender, J., E. Brandhorst, and T. Wang. The Latest Wave of Advanced Beta. The Journal of Index Investing, Vol. 5, No. (204), pp Bender, J., and T. Wang. Tilted and Anti-Tilted Portfolios: A Coherent Framework for Advanced Beta Portfolio Construction. The Journal of Index Investing, Vol. 6, No. (205), pp Brandhorst, E. A Methodology for Capturing Advanced Beta Factors. Capital Insights, State Street Global Advisors, 203. Ross, S.A. The Arbitrage Theory of Capital Asset Pricing. Journal of Economic Theory, 3 (976), pp To order reprints of this article, please contact Dewey Palmieri at dpalmieri@iijournals.com or Bottom-Up versus Top-Down Multifactor Portfolio Construction special Issue 206
It is well known that equity returns are
DING LIU is an SVP and senior quantitative analyst at AllianceBernstein in New York, NY. ding.liu@bernstein.com Pure Quintile Portfolios DING LIU It is well known that equity returns are driven to a large
More informationVOLUME 40 NUMBER 2 WINTER The Voices of Influence iijournals.com
VOLUME 40 NUMBER 2 www.iijpm.com WINTER 2014 The Voices of Influence iijournals.com Can Alpha Be Captured by Risk Premia? JENNIFER BENDER, P. BRETT HAMMOND, AND WILLIAM MOK JENNIFER BENDER is managing
More informationTop-down or bottom-up? Balancing exposure and diversification in multi-factor index construction
Insights Top-down or bottom-up? Balancing exposure and diversification in multi-factor index construction Executive summary For designers of factor indexes there is an inherent trade-off between factor
More informationFactor investing: building balanced factor portfolios
Investment Insights Factor investing: building balanced factor portfolios Edward Leung, Ph.D. Quantitative Research Analyst, Invesco Quantitative Strategies Andrew Waisburd, Ph.D. Managing Director, Invesco
More informationMinimizing Timing Luck with Portfolio Tranching The Difference Between Hired and Fired
Minimizing Timing Luck with Portfolio Tranching The Difference Between Hired and Fired February 2015 Newfound Research LLC 425 Boylston Street 3 rd Floor Boston, MA 02116 www.thinknewfound.com info@thinknewfound.com
More informationin-depth Invesco Actively Managed Low Volatility Strategies The Case for
Invesco in-depth The Case for Actively Managed Low Volatility Strategies We believe that active LVPs offer the best opportunity to achieve a higher risk-adjusted return over the long term. Donna C. Wilson
More informationIntroducing the Russell Multi-Factor Equity Portfolios
Introducing the Russell Multi-Factor Equity Portfolios A robust and flexible framework to combine equity factors within your strategic asset allocation FOR PROFESSIONAL CLIENTS ONLY Executive Summary Smart
More informationResearch Factor Indexes and Factor Exposure Matching: Like-for-Like Comparisons
Research Factor Indexes and Factor Exposure Matching: Like-for-Like Comparisons October 218 ftserussell.com Contents 1 Introduction... 3 2 The Mathematics of Exposure Matching... 4 3 Selection and Equal
More informationTHEORY & PRACTICE FOR FUND MANAGERS. SPRING 2011 Volume 20 Number 1 RISK. special section PARITY. The Voices of Influence iijournals.
T H E J O U R N A L O F THEORY & PRACTICE FOR FUND MANAGERS SPRING 0 Volume 0 Number RISK special section PARITY The Voices of Influence iijournals.com Risk Parity and Diversification EDWARD QIAN EDWARD
More informationDiversified or Concentrated Factors What are the Investment Beliefs Behind these two Smart Beta Approaches?
Diversified or Concentrated Factors What are the Investment Beliefs Behind these two Smart Beta Approaches? Noël Amenc, PhD Professor of Finance, EDHEC Risk Institute CEO, ERI Scientific Beta Eric Shirbini,
More informationThe Merits and Methods of Multi-Factor Investing
The Merits and Methods of Multi-Factor Investing Andrew Innes S&P Dow Jones Indices The Risk of Choosing Between Single Factors Given the unique cycles across the returns of single-factor strategies, how
More informationFE670 Algorithmic Trading Strategies. Stevens Institute of Technology
FE670 Algorithmic Trading Strategies Lecture 4. Cross-Sectional Models and Trading Strategies Steve Yang Stevens Institute of Technology 09/26/2013 Outline 1 Cross-Sectional Methods for Evaluation of Factor
More informationSight. combining RISK. line of. The Equity Imperative
line of Sight The Equity Imperative combining RISK FACTORS for SUPERIOR returns Over the years, academic research has well-documented the notion of compensated risk factors. In Northern Trust s 2013 paper,
More information3 questions you need to answer when choosing factor-based products
3 questions you need to answer when choosing factor-based products March 5, 2018 by Vanguard Advisors are interested in using factors. But it takes a lot of due diligence to choose among the many products
More informationA Framework for Understanding Defensive Equity Investing
A Framework for Understanding Defensive Equity Investing Nick Alonso, CFA and Mark Barnes, Ph.D. December 2017 At a basketball game, you always hear the home crowd chanting 'DEFENSE! DEFENSE!' when the
More informationFactor Performance in Emerging Markets
Investment Research Factor Performance in Emerging Markets Taras Ivanenko, CFA, Director, Portfolio Manager/Analyst Alex Lai, CFA, Senior Vice President, Portfolio Manager/Analyst Factors can be defined
More informationSmart Beta: Index Investing, Evolved
Franklin LibertyShares TM Topic Paper November 2017 Smart Beta: Index Investing, Evolved Global investing literally and figuratively is foreign to many US investors. That s why some have taken a passive
More informationLazard Insights. Distilling the Risks of Smart Beta. Summary. What Is Smart Beta? Paul Moghtader, CFA, Managing Director, Portfolio Manager/Analyst
Lazard Insights Distilling the Risks of Smart Beta Paul Moghtader, CFA, Managing Director, Portfolio Manager/Analyst Summary Smart beta strategies have become increasingly popular over the past several
More informationSciBeta CoreShares South-Africa Multi-Beta Multi-Strategy Six-Factor EW
SciBeta CoreShares South-Africa Multi-Beta Multi-Strategy Six-Factor EW Table of Contents Introduction Methodological Terms Geographic Universe Definition: Emerging EMEA Construction: Multi-Beta Multi-Strategy
More informationMSCI LOW SIZE INDEXES
MSCI LOW SIZE INDEXES msci.com Size-based investing has been an integral part of the investment process for decades. More recently, transparent and rules-based factor indexes have become widely used tools
More informationDoes Relaxing the Long-Only Constraint Increase the Downside Risk of Portfolio Alphas? PETER XU
Does Relaxing the Long-Only Constraint Increase the Downside Risk of Portfolio Alphas? PETER XU Does Relaxing the Long-Only Constraint Increase the Downside Risk of Portfolio Alphas? PETER XU PETER XU
More informationTopic Four: Fundamentals of a Tactical Asset Allocation (TAA) Strategy
Topic Four: Fundamentals of a Tactical Asset Allocation (TAA) Strategy Fundamentals of a Tactical Asset Allocation (TAA) Strategy Tactical Asset Allocation has been defined in various ways, including:
More informationFactor Investing: Smart Beta Pursuing Alpha TM
In the spectrum of investing from passive (index based) to active management there are no shortage of considerations. Passive tends to be cheaper and should deliver returns very close to the index it tracks,
More informationTHEORY & PRACTICE FOR FUND MANAGERS. SPRING 2016 Volume 25 Number 1 SMART BETA SPECIAL SECTION. The Voices of Influence iijournals.
T H E J O U R N A L O F THEORY & PRACTICE FOR FUND MANAGERS SPRING 2016 Volume 25 Number 1 SMART BETA SPECIAL SECTION The Voices of Influence iijournals.com Efficient Smart Beta Nicholas alonso and Mark
More informationFactor exposures of smart beta indexes
Research Factor exposures of smart beta indexes FTSE Russell Factor exposures of smart beta indexes 1 Introduction Capitalisation weighted indexes are considered to be representative of the broad market
More informationLeverage Aversion, Efficient Frontiers, and the Efficient Region*
Posted SSRN 08/31/01 Last Revised 10/15/01 Leverage Aversion, Efficient Frontiers, and the Efficient Region* Bruce I. Jacobs and Kenneth N. Levy * Previously entitled Leverage Aversion and Portfolio Optimality:
More informationFTSE ActiveBeta Index Series: A New Approach to Equity Investing
FTSE ActiveBeta Index Series: A New Approach to Equity Investing 2010: No 1 March 2010 Khalid Ghayur, CEO, Westpeak Global Advisors Patent Pending Abstract The ActiveBeta Framework asserts that a significant
More informationFurther Test on Stock Liquidity Risk With a Relative Measure
International Journal of Education and Research Vol. 1 No. 3 March 2013 Further Test on Stock Liquidity Risk With a Relative Measure David Oima* David Sande** Benjamin Ombok*** Abstract Negative relationship
More informationRisk Factors Citi Volatility Balanced Beta (VIBE) Equity US Gross Total Return Index
Risk Factors Citi Volatility Balanced Beta (VIBE) Equity US Gross Total Return Index The Methodology Does Not Mean That the Index Is Less Risky Than Any Other Equity Index, and the Index May Decline The
More informationBEYOND SMART BETA: WHAT IS GLOBAL MULTI-FACTOR INVESTING AND HOW DOES IT WORK?
INVESTING INSIGHTS BEYOND SMART BETA: WHAT IS GLOBAL MULTI-FACTOR INVESTING AND HOW DOES IT WORK? Multi-Factor investing works by identifying characteristics, or factors, of stocks or other securities
More informationEnhancing equity portfolio diversification with fundamentally weighted strategies.
Enhancing equity portfolio diversification with fundamentally weighted strategies. This is the second update to a paper originally published in October, 2014. In this second revision, we have included
More informationReturn and risk are to finance
JAVIER ESTRADA is a professor of finance at IESE Business School in Barcelona, Spain and partner and financial advisor at Sport Global Consulting Investments in Spain. jestrada@iese.edu Rethinking Risk
More informationHow to evaluate factor-based investment strategies
A feature article from our U.S. partners INSIGHTS SEPTEMBER 2018 How to evaluate factor-based investment strategies Due diligence on smart beta strategies should be anything but passive Original publication
More informationResearch. Multifactor Indexes. The Power of Tilting
Research Multifactor Indexes The Power of Tilting ftserussell.com January 2016 Introduction It wasn t too long ago that the concept of factors in investing was the exclusive province of professors of finance
More informationThe Equity Imperative
The Equity Imperative Factor-based Investment Strategies 2015 Northern Trust Corporation Can You Define, or Better Yet, Decipher? 1 Spectrum of Equity Investing Techniques Alpha Beta Traditional Active
More informationSize. Volatility. Quality
How The to red use herrings factor-based investing in of your tax portfolio efficiency Factors are the underlying exposures that explain and influence an investment s risk. 1 Equity factor-based investing
More informationQuantitative Investment: From indexing to factor investing. For institutional use only. Not for distribution to retail investors.
Quantitative Investment: From indexing to factor investing For institutional use only. Not for distribution to retail investors. 1 What s the prudent portfolio mix? It depends Objective Investment approach
More informationPortfolio strategies based on stock
ERIK HJALMARSSON is a professor at Queen Mary, University of London, School of Economics and Finance in London, UK. e.hjalmarsson@qmul.ac.uk Portfolio Diversification Across Characteristics ERIK HJALMARSSON
More informationJournal Of Financial And Strategic Decisions Volume 10 Number 2 Summer 1997 AN ANALYSIS OF VALUE LINE S ABILITY TO FORECAST LONG-RUN RETURNS
Journal Of Financial And Strategic Decisions Volume 10 Number 2 Summer 1997 AN ANALYSIS OF VALUE LINE S ABILITY TO FORECAST LONG-RUN RETURNS Gary A. Benesh * and Steven B. Perfect * Abstract Value Line
More informationAn All-Cap Core Investment Approach
An All-Cap Core Investment Approach A White Paper by Manning & Napier www.manning-napier.com Unless otherwise noted, all figures are based in USD. 1 What is an All-Cap Core Approach An All-Cap Core investment
More informationNIFTY Multi-Factor Indices. Multi-factor index strategies provide diversified factor-exposure with varied risk-return profile
Multi-Factor Indices Multi-factor index strategies provide diversified factor-exposure with varied risk-return profile July 2017 Introduction Factor-based investing has gathered popularity amongst the
More informationFACTOR ALLOCATION MODELS
FACTOR ALLOCATION MODELS Improving Factor Portfolio Efficiency January 2018 Summary: Factor timing and factor risk management are related concepts, but have different objectives Factors have unique characteristics
More information+ = Smart Beta 2.0 Bringing clarity to equity smart beta. Drawbacks of Market Cap Indices. A Lesson from History
Benoit Autier Head of Product Management benoit.autier@etfsecurities.com Mike McGlone Head of Research (US) mike.mcglone@etfsecurities.com Alexander Channing Director of Quantitative Investment Strategies
More informationStructured Portfolios: Solving the Problems with Indexing
Structured Portfolios: Solving the Problems with Indexing May 27, 2014 by Larry Swedroe An overwhelming body of evidence demonstrates that the majority of investors would be better off by adopting indexed
More informationTed Stover, Managing Director, Research and Analytics December FactOR Fiction?
Ted Stover, Managing Director, Research and Analytics December 2014 FactOR Fiction? Important Legal Information FTSE is not an investment firm and this presentation is not advice about any investment activity.
More informationPremium Timing with Valuation Ratios
RESEARCH Premium Timing with Valuation Ratios March 2016 Wei Dai, PhD Research The predictability of expected stock returns is an old topic and an important one. While investors may increase expected returns
More informationComprehensive Factor Indexes
Methodology overview Comprehensive Factor Indexes Part of the FTSE Global Factor Index Series Overview The Comprehensive Factor Indexes are designed to capture a broad set of five recognized factors contributing
More informationCapital Markets (FINC 950) Introduction. Prepared by: Phillip A. Braun Version:
Capital Markets (FINC 950) Introduction Prepared by: Phillip A. Braun Version: 6.26.17 Syllabus 2 Introduction to the Capital Markets Class The capital markets class provides a structure for thinking about
More informationFOCUS: SIZE. Factor Investing. msci.com
FOCUS: SIZE Factor Investing msci.com FACTOR INVESTING FACTOR FOCUS: SIZE IN THE REALM OF INVESTING, A FACTOR IS ANY CHARACTERISTIC THAT HELPS EXPLAIN THE LONG-TERM RISK AND RETURN PERFORMANCE OF AN ASSET.
More informationINSIGHTS. The Factor Landscape. August rocaton.com. 2017, Rocaton Investment Advisors, LLC
INSIGHTS The Factor Landscape August 2017 203.621.1700 2017, Rocaton Investment Advisors, LLC EXECUTIVE SUMMARY Institutional investors have shown an increased interest in factor investing. Much of the
More informationLiquidity and IPO performance in the last decade
Liquidity and IPO performance in the last decade Saurav Roychoudhury Associate Professor School of Management and Leadership Capital University Abstract It is well documented by that if long run IPO underperformance
More informationPortfolio performance and environmental risk
Portfolio performance and environmental risk Rickard Olsson 1 Umeå School of Business Umeå University SE-90187, Sweden Email: rickard.olsson@usbe.umu.se Sustainable Investment Research Platform Working
More informationCOMPLETE GUIDE TO SMART BETA. Beyond Active and Passive
COMPLETE GUIDE TO SMART BETA Beyond Active and Passive Complete Guide to Smart Beta 04 THE IDEA 04 Smart Beta Strategies: A Roadmap 06 Value 08 Size 10 Low Volatility 12 Quality 14 Momentum 16 Multi-Factor
More informationSTRATEGY OVERVIEW. Long/Short Equity. Related Funds: 361 Domestic Long/Short Equity Fund (ADMZX) 361 Global Long/Short Equity Fund (AGAZX)
STRATEGY OVERVIEW Long/Short Equity Related Funds: 361 Domestic Long/Short Equity Fund (ADMZX) 361 Global Long/Short Equity Fund (AGAZX) Strategy Thesis The thesis driving 361 s Long/Short Equity strategies
More informationNasdaq Chaikin Power US Small Cap Index
Nasdaq Chaikin Power US Small Cap Index A Multi-Factor Approach to Small Cap Introduction Multi-factor investing has become very popular in recent years. The term smart beta has been coined to categorize
More informationThe Rise of Factor Investing
Aon Hewitt Retirement and Investment A paper from Aon s UK Investment Committee The Rise of Factor Investing How clients should invest Table of contents Key conclusions.... 3 Factor investing a reminder...
More informationFactor Investing. Fundamentals for Investors. Not FDIC Insured May Lose Value No Bank Guarantee
Factor Investing Fundamentals for Investors Not FDIC Insured May Lose Value No Bank Guarantee As an investor, you have likely heard a lot about factors in recent years. But factor investing is not new.
More informationActive vs. Passive Money Management
Active vs. Passive Money Management Exploring the costs and benefits of two alternative investment approaches By Baird s Advisory Services Research Synopsis Proponents of active and passive investment
More informationGet active with Vanguard factor ETFs
Get active with Vanguard factor ETFs Factor investing has gained attention in recent years, in part because of the rise of alternatively weighted indexes and smart-beta products. Yet factor investing has
More informationHighest possible excess return at lowest possible risk May 2004
Highest possible excess return at lowest possible risk May 2004 Norges Bank s main objective in its management of the Petroleum Fund is to achieve an excess return compared with the benchmark portfolio
More informationAmajority of institutional
JANUARY FEATURE IS IT TIME TO TILT? Exploring a Fundamental Question in Factor Investing By Andrew Ang, PhD, Ked Hogan, PhD, and Justin Peterson Amajority of institutional investors are now investing in
More informationOptimal Portfolio Inputs: Various Methods
Optimal Portfolio Inputs: Various Methods Prepared by Kevin Pei for The Fund @ Sprott Abstract: In this document, I will model and back test our portfolio with various proposed models. It goes without
More informationThe large drawdowns and extreme
KHALID (KAL) GHAYUR is a managing partner and CIO at Westpeak Global Advisors, LLC, in Lafayette, CO. kg@westpeak.com RONAN HEANEY is a partner and director of research at Westpeak Global Advisors, LLC,
More informationGetting Smart About Beta
Getting Smart About Beta December 1, 2015 by Sponsored Content from Invesco Due to its simplicity, market-cap weighting has long been a popular means of calculating the value of market indexes. But as
More informationJACOBS LEVY CONCEPTS FOR PROFITABLE EQUITY INVESTING
JACOBS LEVY CONCEPTS FOR PROFITABLE EQUITY INVESTING Our investment philosophy is built upon over 30 years of groundbreaking equity research. Many of the concepts derived from that research have now become
More informationImproving Risk Adjusted Returns in Factor Investing
ASSET MANAGEMENT Improving Risk Adjusted Returns in Factor Investing Matt Peron Executive Vice President Head of Global Equity 1 THE IMPETUS FOR FACTOR BASED INVESTING Stock selection has historically
More informationPortfolio Construction Research by
Portfolio Construction Research by Real World Case Studies in Portfolio Construction Using Robust Optimization By Anthony Renshaw, PhD Director, Applied Research July 2008 Copyright, Axioma, Inc. 2008
More informationApplied Macro Finance
Master in Money and Finance Goethe University Frankfurt Week 2: Factor models and the cross-section of stock returns Fall 2012/2013 Please note the disclaimer on the last page Announcements Next week (30
More informationActive vs. Passive Money Management
Active vs. Passive Money Management Exploring the costs and benefits of two alternative investment approaches By Baird s Advisory Services Research Synopsis Proponents of active and passive investment
More informationSyllabus for Capital Markets (FINC 950) Prepared by: Phillip A. Braun Version:
Syllabus for Capital Markets (FINC 950) Prepared by: Phillip A. Braun Version: 1.15.19 Class Overview Syllabus 3 Main Questions the Capital Markets Class Will Answer This class will focus on answering
More informationDiscover the power. of ETFs. Not FDIC Insured May May Lose Lose Value Value No No Bank Bank Guarantee
Discover the power of ETFs Not FDIC Insured May May Lose Lose Value Value No No Bank Bank Guarantee Discover exchange-traded funds (ETFs) Financial television programs and publications continue to give
More informationFOCUS: YIELD. Factor Investing. msci.com
FOCUS: YIELD Factor Investing msci.com FACTOR FOCUS: YIELD FACTOR FOCUS: YIELD IN THE REALM OF INVESTING, A FACTOR IS ANY CHARACTERISTIC THAT HELPS EXPLAIN THE LONG-TERM RISK AND RETURN PERFORMANCE OF
More informationLazard Insights. Growth: An Underappreciated Factor. What Is an Investment Factor? Summary. Does the Growth Factor Matter?
Lazard Insights : An Underappreciated Factor Jason Williams, CFA, Portfolio Manager/Analyst Summary Quantitative investment managers commonly employ value, sentiment, quality, and low risk factors to capture
More informationTAKE CONTROL OF YOUR INVESTMENT DESTINY Increasing control over your investments.
TAKE CONTROL OF YOUR INVESTMENT DESTINY Increasing control over your investments. Challenge for Investors Case for Factor-based Investing What Next? The Real World Economic and Market Outlooks are Constrained
More informationDividend Growth as a Defensive Equity Strategy August 24, 2012
Dividend Growth as a Defensive Equity Strategy August 24, 2012 Introduction: The Case for Defensive Equity Strategies Most institutional investment committees meet three to four times per year to review
More informationAN INTRODUCTION TO FACTOR INVESTING
WHITE PAPER AN INTRODUCTION TO FACTOR INVESTING THIS DOCUMENT IS INTENDED FOR INSTITUTIONAL INVESTORS ONLY. IT SHOULD NOT BE DISTRIBUTED TO, OR USED BY, INDIVIDUAL INVESTORS. OUR RESEARCH COMMITMENT As
More informationThe Case for Growth. Investment Research
Investment Research The Case for Growth Lazard Quantitative Equity Team Companies that generate meaningful earnings growth through their product mix and focus, business strategies, market opportunity,
More informationAlternative Index Strategies Compared: Fact and Fiction
Alternative Index Strategies Compared: Fact and Fiction IndexUniverse Webinar September 8, 2011 Jason Hsu Chief Investment Officer Discussion Road Map Status Quo of Indexing Community Popular Alternative
More informationDynamic Asset Allocation for Practitioners Part 1: Universe Selection
Dynamic Asset Allocation for Practitioners Part 1: Universe Selection July 26, 2017 by Adam Butler of ReSolve Asset Management In 2012 we published a whitepaper entitled Adaptive Asset Allocation: A Primer
More informationMS&E 348 Winter 2011 BOND PORTFOLIO MANAGEMENT: INCORPORATING CORPORATE BOND DEFAULT
MS&E 348 Winter 2011 BOND PORTFOLIO MANAGEMENT: INCORPORATING CORPORATE BOND DEFAULT March 19, 2011 Assignment Overview In this project, we sought to design a system for optimal bond management. Within
More informationComparison of U.S. Stock Indices
Magnus Erik Hvass Pedersen Hvass Laboratories Report HL-1503 First Edition September 30, 2015 Latest Revision www.hvass-labs.org/books Summary This paper compares stock indices for USA: Large-Cap stocks
More informationMorningstar Style Box TM Methodology
Morningstar Style Box TM Methodology Morningstar Methodology Paper 28 February 208 2008 Morningstar, Inc. All rights reserved. The information in this document is the property of Morningstar, Inc. Reproduction
More informationPrinciples of Finance
Principles of Finance Grzegorz Trojanowski Lecture 7: Arbitrage Pricing Theory Principles of Finance - Lecture 7 1 Lecture 7 material Required reading: Elton et al., Chapter 16 Supplementary reading: Luenberger,
More informationSmart Beta and the Evolution of Factor-Based Investing
Smart Beta and the Evolution of Factor-Based Investing September 2016 Donald J. Hohman Managing Director, Product Management Hitesh C. Patel, Ph.D Managing Director Structured Equity Douglas J. Roman,
More informationStock Returns and Holding Periods. Author. Published. Journal Title. Copyright Statement. Downloaded from. Link to published version
Stock Returns and Holding Periods Author Li, Bin, Liu, Benjamin, Bianchi, Robert, Su, Jen-Je Published 212 Journal Title JASSA Copyright Statement 212 JASSA and the Authors. The attached file is reproduced
More informationSmart Beta and the Evolution of Factor-Based Investing
Smart Beta and the Evolution of Factor-Based Investing September 2017 Donald J. Hohman Managing Director, Product Management Hitesh C. Patel, Ph.D Managing Director Structured Equity Douglas J. Roman,
More informationSmart Beta Dashboard. Thoughts at a Glance. June By the SPDR Americas Research Team
By the SPDR Americas Research Team Thoughts at a Glance Factor performance diverged across regions in Q2. In the US, all factors with the exception of underperformed broad US equities. As volatility in
More informationThe benefits of core-satellite investing
The benefits of core-satellite investing Contents 1 Core-satellite: A powerful investment approach 3 The key benefits of indexing the portfolio s core 6 Core-satellite methodology Core-satellite: A powerful
More informationFactor Mixology: Blending Factor Strategies to Improve Consistency
May 2016 Factor Mixology: Blending Factor Strategies to Improve Consistency Vassilii Nemtchinov, Ph.D. Director of Research Equity Strategies Mahesh Pritamani, Ph.D., CFA Senior Researcher Factor strategies
More informationModule 3: Factor Models
Module 3: Factor Models (BUSFIN 4221 - Investments) Andrei S. Gonçalves 1 1 Finance Department The Ohio State University Fall 2016 1 Module 1 - The Demand for Capital 2 Module 1 - The Supply of Capital
More informationIDIOSYNCRATIC RISK AND AUSTRALIAN EQUITY RETURNS
IDIOSYNCRATIC RISK AND AUSTRALIAN EQUITY RETURNS Mike Dempsey a, Michael E. Drew b and Madhu Veeraraghavan c a, c School of Accounting and Finance, Griffith University, PMB 50 Gold Coast Mail Centre, Gold
More informationDynamic Smart Beta Investing Relative Risk Control and Tactical Bets, Making the Most of Smart Betas
Dynamic Smart Beta Investing Relative Risk Control and Tactical Bets, Making the Most of Smart Betas Koris International June 2014 Emilien Audeguil Research & Development ORIAS n 13000579 (www.orias.fr).
More informationPublication for private investors
MindScope Use of the right factors can contribute to the best stock selection for a portfolio. But which factors are the right ones to use? And how can we most efficiently reap their rewards in factor
More informationDirexion/Wilshire Dynamic Asset Allocation Models Asset Management Tools Designed to Enhance Investment Flexibility
Daniel D. O Neill, President and Chief Investment Officer Direxion/Wilshire Dynamic Asset Allocation Models Asset Management Tools Designed to Enhance Investment Flexibility Executive Summary At Direxion
More informationDimensions of Equity Returns in Europe
RESEARCH Dimensions of Equity Returns in Europe November 2015 Stanley Black, PhD Vice President Research Philipp Meyer-Brauns, PhD Research Size, value, and profitability premiums are well documented in
More informationInvestabilityof Smart Beta Indices
Investabilityof Smart Beta Indices Felix Goltz, PhD Research Director, ERI Scientific Beta Eric Shirbini, PhD Global Product Specialist, ERI Scientific Beta EDHEC-Risk Days Europe 2015 24-25 March 2015
More informationCORESHARES SCIENTIFIC BETA MULTI-FACTOR STRATEGY HARVESTING PROVEN SOURCES OF RETURN AT LOW COST: AN ACTIVE REPLACEMENT STRATEGY
CORESHARES SCIENTIFIC BETA MULTI-FACTOR STRATEGY HARVESTING PROVEN SOURCES OF RETURN AT LOW COST: AN ACTIVE REPLACEMENT STRATEGY EXECUTIVE SUMMARY Smart beta investing has seen increased traction in the
More informationHigh conviction: Creating multi-asset portfolios designed to achieve investors objectives
The Invesco White Paper Series High conviction: Creating multi-asset portfolios designed to achieve investors objectives Contributors: Duy Nguyen, CFA, CAIA Senior Portfolio Manager Chief Investment Officer
More informationHarbour Asset Management New Zealand Equity Advanced Beta Fund FAQ S
Harbour Asset Management New Zealand Equity Advanced Beta Fund FAQ S January 2015 ContactUs@harbourasset.co.nz +64 4 460 8309 What is Advanced Beta? The name Advanced Beta is often interchanged with terms
More informationVolatility reduction: How minimum variance indexes work
Insights Volatility reduction: How minimum variance indexes work Minimum variance indexes, which apply rules-based methodologies with the aim of minimizing an index s volatility, are popular among market
More information