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Q3&4 2016 IQ ACTIVE QUANT GOLDEN AGE Smart Data to Big Alpha? HIGH-DEFINITION RETURNS Turning up the Factor Signal MARCH OF THE MACHINES Deep Learning for Better Alpha IQ Q 3 &4 2 016 IQ magazine provides the most relevant thought leadership from State Street Global Advisors. ACTIVE The combination of big data and artificial intelligence is transforming how active managers identify new factor signals to build better alpha models. 3 PUNCTUATED EQUILIBRIUM Like shifts in evolutionary biology, the combination of factor investing and new technology is transforming active management. 7 WHEN LESS IS MORE When it comes to active factor investing, the quality of factors is far more important than the number of factors in alpha models. 15 BACK IN THE SPOTLIGHT Olivia Engel makes history as the first quant and first woman to win Australia’s Blue Ribbon Award. 17 FACTOR TIMING While timing factors is notoriously difficult, we look at promising results when timing is applied to multifactor Smart Beta strategies. 21 RISE OF ARTIFICIAL INTELLIGENCE Dramatic breakthroughs in machine learning offer promising potential for building better alpha models. 25 APPLYING THE FACTOR LENS By readjusting unintended factor bets in equity allocations, investors can target better riskadjusted returns. Q3&4 2016 | The New Active 2 PUNCTUATED EQUILIBRIUM And the Golden Age of Active Quant Management In the last issue of the IQ,1 we discussed how factor investing is disrupting traditional active management and raising the bar on managers to show how much of their return is true, skill-based alpha. RICK LACAILLE Chief Investment Officer State Street Global Advisors (SSGA) SSGA’s First Active Quant Strategy SSGA’s First Smart Beta Strategy Launch of the S&P 500 Index 1976 1984 1993 Punctuated Equilibrium I n this issue we focus on the ways in which the combination of unprecedented amounts of data and advances in artificial intelligence (AI) in our increasingly connected world is helping active managers pursue new sources of uncorrelated alpha in rigorous, systematic ways. We feature the perspectives from our Active Quantitative Equity team on their approach to specifying and testing factors to improve the models they use to generate alpha as well as how our Investment Solutions Group is using the factor lens to build more resilient portfolios. Given the recent performance difficulties of many active managers, including hedge fund managers, it might seem strange to talk about a “golden age” of active management. But we do think we are at an important inflection point in the industry as factor investing contributes to the extinction of certain kinds of active managers whose factor exposures can be captured more cost-efficiently through Smart Beta strategies. At the same time, we expect the advent of new data, tools and technology will give rise to a new species of active managers, increasingly looking at investment opportunities and risks through a factor lens. We compare this transformative change in the industry to a somewhat esoteric phenomenon in evolutionary biology called punctuated equilibrium. For SSGA’s Global Defensive Equity Strategies2 those of you unfamiliar with the concept, it caused quite a stir in 1972 when the late, great American paleontologist and evolutionary biologist Stephen Jay Gould of Harvard University, along with his colleague Niles Eldredge from Columbia University, challenged Darwin’s traditional view of gradual evolutionary development. The two scientists argued that the fossil record showed virtually no evidence of evolutionary gradualism. Instead, it seemed that long periods of stasis or evolutionary equilibrium dominated the history of most fossil species until there were quite dramatic shifts or “punctuations” when step changes occurred that transformed our world. History of Investing We believe it is not too far-fetched to apply a similar theory of development to our own investment management industry today. For many years, investing was based on an eclectic combination of analysis, forecasting and beliefs that was hard to reconcile with evolving academic thinking. Markets experienced periods of irrational exuberance and corrections, but the investment process remained fairly static. One could argue that the first moment of punctuated equilibrium in investment management came with the creation of market-cap weighted indexes in the 1970s. For most of the 20th century, active investors regarded the entirety of their return as having been achieved through the application of skill. The advent of the Efficient Market Hypothesis (EMH) and the Capital Asset Pricing Model (CAPM) suggested that the return should properly be split between that part due to exposure to the overall equity market, and a residual that we would expect to be on average zero. This reframing of active management raised the bar considerably for managers and gave rise to the trillions-large passive investment phenomenon. Active managers had to adapt and provide excess return beyond those benchmarks. Thus ensued the era of traditional active management where managers tried to beat the index primarily by fundamental processes of company-focused security selection. Over time, however, quantitative-based managers joined the industry, forging the path toward more systematic ways of understanding the drivers of risk and return with the first generation of computer-aided analysis and behavioral finance insights. Active Quant AUM $27B3 Smart Beta AUM $88B3 SSGA’s First Smart Beta Fixed Income Strategy 2008 2010 2016 Punctuated Equilibrium Age of Factor Investing More than 40 years on from the creation of market-cap weighted indexes, we think we are now on the cusp of another punctuation that could have far more dramatic implications for investing. The beginnings of this shift have been driven by the insights factor investing has brought to light, even though the initial research into factors goes back nearly as far as the first capweighted indexes. Factor investing provides a powerful lens for understanding the drivers of risk and return beyond traditional asset class categories. As the tools for disaggregating investment returns have become more widespread, we have seen how Smart Beta strategies have challenged traditional active managers and, increasingly, alternatives managers. Smart Data, Big Alpha That factor-based process of natural selection will likely weed out many traditional active managers as well as some high-priced hedge fund managers at the same time that the combined forces of big data and technology will favor data-driven active managers discovering new anomalies and dislocations. In this new age, the lines between fundamental and quantitative active managers will become increasingly blurred as both come to embrace the new tools and technology. We believe the successful active managers of the future will be able to incorporate the best elements of both approaches. While it has become commonplace to marvel at the amount of data our new “Internet of Everything” age is throwing off, it is worth reminding ourselves of the order of magnitude by which the information we can apply to our investment process is growing. The purported 12 exabytes4 of data the US intelligence community is storing in their enormous center in the desert of Utah might send shudders of apprehensiveness down the spine of American citizens until they realize that Google’s data centers reportedly have up to 15 exabytes stored (or the equivalent of around 30 million personal computers).5 And the volumes of new data continue to boggle the mind: every minute, 7.8 million videos are viewed; more than 3.3 million searches are entered; 151 million e-mail messages are sent; and more than 436,000 tweets are posted! Of course, all sorts of data sets have been around for a while now. The difference is the new assortment of tools and artificial intelligence (AI) technology for processing the data. And indeed, this information overload could actually create new scarcity in the form of knowledge differentiation and interpretation. This is the opportunity facing active managers. With real-time data literally pulled from the ether, we may be able to assess companies and markets far more quickly and with more granularity than ever before. All of this could be turned into a new class of investable information, heralding a new golden age of quantdriven active management. Golden Age of Active Quant Management One way to understand how this age of big data and AI will affect our industry is to consider some of the theoretical principles underpinning active management. According to these, active managers’ performance is thought to be a function of three Big Data Per Minute Assessing companies and markets with real time data presents an exciting opportunity for active managers a new class of investable information. 7.8 3.3 151 436K Million Videos Viewed Million Searches Entered Million Emails Sent Tweets Posted Source: Internet Live Stats (cited by World Wide Web Consortium), September 2016. Q3&4 2016 | The New Active 5 Punctuated Equilibrium basic drivers: the accuracy of their forecasts; the number of independent forecasts they can tap into; and the degree to which they can transfer those insights to their portfolios, given the constraints of the markets they operate in and imposed upon them by client mandates. Quant-driven processes will likely come to play a larger role in active management at the same time that society at large will become more comfortable with such processes for everything from choosing a restaurant and a movie to rebalancing investment portfolios. Skilled active managers can add value on each of these dimensions by: embedding proprietary refinements into otherwise well-known return drivers; becoming more effective and consistent in processing publicly available information at the specific company level; transporting data from one context to another to forecast changes of direction in industry or company dynamics; discovering factors that are not in Smart Beta strategies or otherwise in the public domain; identifying better ways of blending these elements; varying exposures to return drivers over time (recognizing that their effectiveness will ebb and flow); and making improvements to risk modelling and portfolio construction. But we still believe the most successful firms will be able to incorporate the best elements of quant and fundamental approaches, as human judgment will still play an important role. Steps like these can improve existing forecasts and diversify them with additional views — helping to ensure that portfolios are driven by forces over which managers have skill, while mitigating the effects of forces beyond their control. As the domain of skill expands, areas that were once considered unpredictable can shrink; dimensions that had to be constrained can become forecastable elements that can be captured as additional sources of alpha. In short, expanding the manager’s skill, the number of forecasts and the transferability of those insights into the portfolio bodes well for active performance. Actionable insights from big data and AI promise to enhance all three drivers of active managers’ performance. The Active Quant Shop of the Future So what does this new golden age of active quant management mean for our industry? We think it has distinct implications for what the preconditions for success will be. Access to data and the tools to harness that data will be more important than ever. Those asset management firms that have already made the necessary investments in data and technology will have an edge. DATA Arms race for new and better structured data sets Of course there will also be new risks in this new world in the form of data highway crashes, which managers will need to mitigate. The new golden age of active quant management will also require a new kind of talent. Graduates in data science are likely to be relatively more attractive to the industry than graduates in economics or traditional finance. Asset management will become much more of a technology industry than it is already, and it will be competing with the Googles, Facebooks and Amazons of the future for the same kind of talent. And what becomes of the golden age when machine learning advances to the stage where human portfolio managers are no longer necessary, when we as an industry reach the same point as the joke about the fully automated factory of the future with two employees: a man and a dog. The man’s job is to feed the dog. The dog’s job is to prevent the man from touching any of the automated equipment. Will we reach that “I, Robot” state where artificial intelligence renders humans in investment management obsolete? Perhaps that day will arrive, but we would argue that the technological singularity will have likely subsumed humans across all industries and endeavors by that point anyway, so the question will be moot — and a truly dramatic example of punctuated equilibrium will have ensued! 1 TECHNOLOGY Rise of deep learning algorithms to harness new and e isting data TALENT Competing with the oogles and ma ons of the future for tech-savvy talent 2 3 4 5 State Street Global Advisors, “The New Investment Reality,” IQ, Q1&2 2016. Strategy names effective as of October 1, 2016. Formerly named Managed Volatility Alpha Strategies. State Street Global Advisors assets under management as of 3/31/2016. An exabyte is a unit of digital information. One exabyte equals one quintillion (or 1000 to the 6th power) bytes of information. Richi Jennings, “NSA’s Huge Utah Datacenter: How Much of Your Data Will It Store?” Forbes, July 26, 2013; Colin Carson, “How Much Data Does Google Store,” Cirrus Insight, November 18, 2014. Q3&4 2016 | The New Active 6 WHEN LESS IS MORE INVESTMENT DISCUSSION The Virtues of Rigorous Factor Selection Q3&4 2016 | The New Active 7 With interest in factor investing as an alpha generator growing, investors are keen to know how quant managers specify and incorporate factor signals into their investment models. This is especially important as the supply of data explodes and managers need to have a rigorous process for separating true signals from noise. SSGA’s Deputy CIO Lori Heinel sat down with Vladimir Zdorovtsov, who heads global research for our Active Quantitative Equity (AQE) team, to discuss the research-based framework the team uses to select factors for their models. When it comes to active factor investing, Vlad says, the quality of the factors is far more important than the number of factors used in a model. LORI HEINEL, CFA Chief Portfolio Strategist, SSGA VLADIMIR ZDOROVTSOV, PhD Managing Director, Active Quantitative Equity, SSGA Q3&4 2016 | The New Active 8 When Less is More LORI W hat’s the investment philosophy behind the alpha models you build? VLAD Our philosophy is three-pronged. First, we believe... In the case of active managers, the factors are proprietary, based on insights specific to a given manager, which are closely guarded. hose insights are meant to deliver enhancements to what might otherwise be well-known phenomena or harness something completely new, and to generate genuine alpha on top of what commoditi ed factors might be capturing. ...markets are not efficient. This inefficiency stems from irrational behaviors and systematic biases, coupled with market frictions or speed bumps that distort the process of price discovery and may also lead to transient mispricings that we might be able to harness. The second prong speaks to how we try to unearth these opportunities. H ow does that fit into the broader category of factor investing, and how does AQE’s approach to factor investing compare with the way our Smart Beta team uses factors? I think of a factor more broadly as a systematic decision criterion... ...a way to compare multiple investment opportunities using the same rule. There are a number of dimensions along which you can differentiate factors. For example, you can think of them as being We believe our alpha ideas need to have a strong economic rationale and that we need to rigorously vet these ideas with methodical and careful empirical testing. Thirdly, we strongly believe that whichever opportunities we find, the best way to tap into them is to be systematic and process-driven and to apply them broadly. transparent versus non-transparent. So in the case of Smart Beta, they are fully transparent. Anyone can replicate those factors because they are all in the public domain. In the case of active managers, the factors are proprietary, based on insights specific to a given manager, which are closely guarded. Those insights are meant to deliver enhancements to what might otherwise be well-known phenomena or harness something completely new, and to generate genuine alpha on top of what commoditized factors might be capturing. Another dimension would be crosssectional versus time series. A decision rule may be systematically deployed to compare multiple opportunities at a specific point in time. So, for example, I can compare stock A to stock B at a given point in time. Alternatively, I can use a systemic rule to make investment decisions over time — for example, to Q3&4 2016 | The New Active 9 When Less is More vary my allocation to value or to make the portfolio more or less defensive. One can think of these systematic decision rules as time series factors. To the extent such rules may be in the public domain and/or are implemented transparently, they are fair game for Smart Beta. We are beginning to see Smart Beta providers make inroads here. I think our approach in AQE is materially different from Smart Beta since we reflect proprietary enhancements that go above and beyond what a public domain version of a factor would capture. Moreover, we may have factors that don’t have any equivalents in the public domain. Another important dimension in factor investing is the difference between explicit and implicit factors. Smart Beta focuses on explicit factors but in many instances, an active manager may be doing something that is implicitly a factor exposure. Say, for example, I am an active value manager selecting underpriced stocks, or a growth manager looking for growth stocks. Underneath these allegedly stock-specific exposures, there is often still a factor bet. I may kick the tires by How do you think about incorporating these kinds of factor signals into your alpha models, and why are there variations across quant managers in the number of factors they use? To answer that, you need to understand why rigor is so important... ...in building models. To identify a genuine factor, you first have to know what factors are capturing. If you delve deeply into why these factors exist in the first place, it becomes fairly obvious that there are not so many different anomalies, human misbehaviors, risk premia or market frictions. There is a limited number of these opportunities. They may manifest themselves differently, at different points in time or in different contexts and be measured or approximated in different ways. However, the number of drivers underlying the predictability of returns is relatively low. You have to be extremely careful when trying to capture those drivers, because their explanatory power will be relatively weak even under the best of circumstances. You must adhere to a strict economic rationale in terms of the thoroughness of your vetting process. This starts at the ideation stage, which should involve a great deal of scrutiny and debate within the team, making sure that the intuition behind the idea is robust, all the way through a battery of structured in-sample testing and to the out-of-sample corroboration of the findings to ensure you haven’t inadvertently overfitted the data. interviewing a company’s management or do some forensic accounting. But behind all of this there are systematic rules for comparing companies. So if I ask similar questions to multiple companies and management teams, and I determine that this particular company seems to be better run or its accounting seems to be cleaner, underneath my comparison there is still a factor. I’m still using some rule of thumb or decision rule, but it will be an implicit rather than an explicit factor bet. In terms of the variation in how parsimonious different managers’ models are, there are several reasons for this. The first may be merely optical or cosmetic. In some cases, one manager may refer to a single factor, whereas other managers might divide that into several subcomponent factors, but they are still capturing the same idea. Secondly, managers might inadvertently include redundant factors that are already nested in or subsumed by the other model components. When you’re considering a new candidate factor that shows promise on its own, there are several possible outcomes once it is plugged into the existing process. Ideally you find that it is truly orthogonal and merits inclusion in the model. Secondly, you might find that it is already subsumed or nested in one of the model’s existing components and thus redundant. A third possibility is that the candidate factor is a better version of what you already have and it should displace the existing factor. The point is that managers can end up with more factors than they really ought to have if they are not thorough and methodical when looking for Q3&4 2016 | The New Active 10 When Less is More improvements to their process. We believe you need to have a properly high bar for including a factor, as well as a process for revisiting and refreshing what is in the existing model. As you add new signals, you may have to remove others, which some managers may not do. We believe that adversely affects the efficiency of the model. W hat about the possibility of spurious factor signals? That is the most troublesome concern. Again, without sufficient rigor... ... a manager can include a spurious factor signal that is really just a fluke. If you apply brute force to the data without any modicum of economic intuition, you will find many false positives. Such blind torturing of data into submission is probably fairly infrequent among more careful managers. What tends to happen more often is that you may have some underlying economic intuition but it is not prescriptive enough. If you allow too much wiggle room in letting the data speak for itself, you may still find something that looks efficacious, but just by chance. The economic rationale needs to be carefully analyzed in the context of the data to ensure you have not arrived at the results by happenstance. If your economic rationale is pointing to some underlying relationship, you should test to validate that this economic relationship is indeed driving the behavior. For example, if you have some intuition about investor disagreement driving overpricing and subsequently leading to lower returns, you should be able to observe that across a number of different ways of measuring disagreement. If you look at 10 different ways of measuring this and nine of these support the hypothesis and one doesn’t, most likely the last one is a false negative. On the other hand, if you look at 10 ways and only one points in the right direction, then that is likely a false positive. It is important to look at not just whether a variable is predicting returns but to drill into why it is predicting them. If disagreement leads to overpricing because of short-selling constraints, this effect should be more salient among stocks where — and at times when — those constraints are more binding. Similarly, if you conjecture that a given factor predicts returns because it predicts earnings, I should actually look to see if it is predicting returns only because it is predicting earnings. If it is predicting returns without predicting earnings, then it is probably a fluke. So in our rigorous framework it is not enough for a factor signal to appear to be working, it has to be working for the right reasons. Can you give an example of a factor signal you thought was promising but proved redundant or spurious? An interesting example was when we tested how a new sentiment factor might work in our model. We called this new sentiment factor “disposition” after the famous disposition effect rooted in the seminal Prospect Theory work of the Nobel prize-winning behavioralist Daniel Kahneman and his colleague, the late Amos Tversky. Essentially the disposition effect says that investors have a greater propensity to sell winning stocks and an aversion to suffer losses by selling declining stocks, resulting in an underreaction to good and bad news. So this factor had a great deal of elegant theory and strong conceptual appeal behind it, as it seemed to get directly at the core drivers of investor underreaction. As we see in the accompanying table, the disposition factor (DISP) did work well when measured in isolation (the green dots signify a statistically significant positive effect). Q3&4 2016 | The New Active 11 When Less is More Raising a High Bar for Factor Selection (indicated by the green dots in the second column) across a multitude of different stock universes, though not as many as the model’s existing momentum factor (the CONS or “consistency” signal). However, when the disposition signal was tested together with all the other existing alpha model components The table below shows why it is important to test factors together with existing elements in an alpha model, across a broad universe of markets. When a new momentum factor (the DISP or “disposition” signal) was tested on its own (“univariate”), it showed statistically significant positive effects (“multivariate”) across the same stock universes, the effect was no longer statistically significant at conventional levels (indicated by red and orange dots in the last column), and therefore not included in the model. UNIVARIATE CONS Stock Universe IC* (%) ACWI IMI 4.1 WLD IMI 3.7 WLD STD 1.8 WLD SMALL 4.2 EM IMI 4.2 NA IMI 3.3 EU WLD IMI 3.2 APEXJP WLD IMI 6.3 JP IMI 3.1 NA STD 1.8 EU WLD STD 0.7 APEXJP WLD STD 2.5 JP STD 1.7 RUSSELL2000 3.2 EU WLD SMALL 4.3 APEXJP WLD SMALL 8.1 JP WLD SMALL 3.3 US 3.3 AU 4.4 CA 5.8 UK 2.3 EAFE 1.8 EAFE SMALL 5.1 MULTIVARIATE DISP p-value • • • • • • • • • • • • • • • • • • • • • • • IC* (%) 0.00 3.4 0.00 2.8 0.05 0.8 0.00 3.5 0.00 3.7 0.00 1.5 0.00 2.5 0.00 5.6 0.01 2.2 0.09 0.4 0.29 -0.1 0.04 2.2 0.11 1.4 0.00 1.3 0.00 3.8 0.00 7.1 0.00 1.9 0.00 1.3 0.00 5.9 0.00 5.0 0.03 1.5 0.06 1.0 0.00 4.8 CONS p-value • • • • • • • • • • • • • • • • • • • • • • • Source: SSGA Active Quantitative Equity Research Team. Data sample reflects the time period 1/1997-12/2013. For illustrative purposes only. * Information Coefficient. IC* (%) 0.00 2.0 0.01 2.1 0.25 1.3 0.00 2.2 0.00 1.3 0.12 1.8 0.03 1.4 0.00 3.7 0.09 1.8 0.39 1.3 0.46 0.3 0.08 1.5 0.23 0.4 0.11 1.9 0.00 1.7 0.00 5.1 0.11 2.2 0.13 1.9 0.00 -0.2 0.00 2.0 0.16 1.3 0.23 1.2 0.00 2.7 DISP p-value • • • • • • • • • • • • • • • • • • • • • • • IC* (%) 0.00 0.7 0.00 0.4 0.02 -0.8 0.00 0.9 0.01 0.8 0.00 -0.7 0.00 -0.6 0.00 2.3 0.00 -0.2 0.04 -1.5 0.30 -1.2 0.07 0.5 0.36 -1.2 0.00 -0.9 0.00 0.0 0.00 3.0 0.00 -0.4 0.00 -0.8 0.43 1.9 0.01 0.6 0.04 -0.9 0.04 -0.6 0.00 1.5 Strongest p-value • • • • • • • • • • • • • • • • • • • • • • • 0.16 0.31 0.21 0.15 0.15 0.25 0.24 0.01 0.43 0.12 0.16 0.35 0.20 0.12 0.49 0.00 0.36 0.23 0.07 0.31 0.24 0.28 0.04 Weakest Q3&4 2016 | The New Active 12 When Less is More However, when we combined it with our existing momentum factor (“consistency” or CONS), and controlled for other components within our process, we found that our existing consistency factor remained strong, while the disposition factor was no longer significant. Those results were broadly consistent across a variety of testable stock universes, which is a critical part of the vetting process. So while it looked attractive in isolation, it did not pass our bar for including in the model. I should mention though that while the disposition factor was not additive in this case, we are actually looking at an improved version of the signal which addresses a possibly questionable assumption that all shares of a stock float are equally likely to change hands on a given day. So we may well add a revised version of this factor to our process in the future. H ow does the onslaught of ever more data and tools in this age of big data and artificial intelligence affect the search for genuine alpha signals? As active managers, what we are trying to predict is... ...ultimately a function of information, so the more relevant information we have, the better. We already see multiple new data sets capturing the underlying behaviors of market participants and economic agents in ever more granular ways. These should enable us to better capture future earnings and other drivers of returns. Doesn’t it increase the risk of incorporating noise or spurious signals into your model? It does indeed, which is why we believe ... ...a strict economic rationale will become even more important in this age of big data. That rationale will guide the important decisions around how the data needs to be structured; otherwise you’ll find there are multiple ways of coalescing the data into some predictive signal. So rigorous parsimony will become even more important in selecting factors. On a positive note, as more of this data becomes available, it will allow us to reflect the true fundamentals of a company in a more comprehensive and direct way. To that extent, prices will become increasingly reflective of intrinsic value and markets will become progressively more efficient. Q3&4 2016 | The New Active 13 When Less is More W hat will that mean for the search for alpha, and how do you see the future of factor investing evolving from here? We already see an arms race for access to data, technology and talent, as active managers become better at gleaning insights from the data. The unknowable part of what drives variations in returns is likely to shrink. But even in a world where a combination of big data and deep learning tools will lead to a better understanding of a company’s fundamentals, the risk embedded in those fundamentals, and the market’s pricing of both of these, there will always be room for active management. That is because markets can never be efficient without someone making them so. This goes to the heart of the Grossman-Stiglitz paradox whereby for markets to be efficient there needs to be some minimal level of inefficiency. And the role of human judgment and skill will continue to be key, even as the data and tools evolve. The tools might get “smarter,” but you’ll still need human input to know which tools to use where and how to apply them. Whatever the future holds, I believe the quality of people, tools and data will continue to be the differentiating element in active management. It is these three areas we think clients should focus on when evaluating managers, rather than the narrow and possibly misleading metrics of supposed model complexity. ... the role of human judgment and skill will continue to be key, even as the data and tools evolve. he tools might get smarter, but you ll still need human input to know which tools to use where and how to apply them. Q3&4 2016 | The New Active 14 Back in the SPOTLIGHT ACTIVE QUANT Olivia Engel makes history as the first quant and first woman to win Australia’s Blue Ribbon Award. Olivia Engel Makes History as First Quant and First Woman to Win Australian Blue Ribbon Award Q3&4 2016 | The New Active 15 MANAGER PROFILE Olivia Engel Head of Active Quantitative Equity, Asia-Pacific, SSGA Olivia Engel did not begin her career in asset management as a quant, but over time she came to favor taking a precise and systematic approach to constructing stock portfolios. “I saw the benefits of taking emotion out of stock-picking and instead focusing on building a rigorous model that would buy, hold or sell stocks according to objective criteria.” That discipline has worked well for her. Engel was recently awarded the Blue Ribbon Award1 for the best Australian large cap strategy, recognition for quant managers at a time when the broad category has struggled. It was the first time that a woman and a quant manager have won the award. Engel, who heads quantitative equities for SSGA in the Asia-Pacific region, believes that active managers need to show clients that they are adding value for their fees. “We know that Smart Beta strategies are raising the bar on active managers to demonstrate that they are providing more than what an investor could achieve by tilting to some well-known factor premia. The bar should be raised, so I welcome it.” Based in Sydney, Engel joined SSGA in 2011 and is responsible for investment management and research for Australian and Pacific ex-Japan active equity portfolios. The Australian large cap strategy managed by her team (including three fellow portfolio managers and one research analyst) is a low volatility defensive strategy focusing on the dual objectives of delivering strong returns and reducing drawdown risk, without using the performance benchmark as an anchor to portfolio construction. “In this lower-for-longer return environment, investors need far more time to recover from portfolio drawdowns, which is why we are so focused on managing the volatility of the strategy.” The Blue Ribbon Award research partner Morningstar described the strategy as a credible option for investors seeking core equity exposure. “While we manage the Australian version of this strategy,” Engel says, “the same philosophy, process and stock selection models underpin all of our active quant equity strategies around the world. For all the benchmarkunaware strategies, the goal is the same: target the best possible return and a meaningful reduction in the volatility of the investment.” I saw the benefits of taking emotion out of stock-picking and instead focusing on building a rigorous model that would buy, hold or sell stocks according to objective criteria. Engel agrees that quant managers seem poised on the cusp of a promising period as better data sets and tools should help them harness new sources of alpha. She says it is important for managers to be intentional about the factor bets they have in their portfolios. “This year in particular,” she says, “we’ve seen cases where making the wrong or unintended factor bets have undermined an active manager’s stock-picking talents.” Reflecting on her distinction, Engel is quick to credit her colleagues for their contribution to all aspects of the investment process. As the first woman to receive the prestigious award, she wonders why it took so long. She believes successful investment teams need to have a high degree of diversity to avoid groupthink and ensure they are drawing on a wide universe of ideas. She assigns team diversity the same importance she gives to portfolio diversification, and sees no reason why the number of female portfolio managers should not continue to rise. Prior to joining SSGA, the Australian native spent eight years at GMO as a Senior Portfolio Manager in the Australian Equities group, responsible for portfolio management across all Australian strategies. She also worked for Colonial First State Global Asset Management and Commonwealth Investment Management as a portfolio manager and has been working in the investment industry since 1997. She received a Bachelor of Economics and a Bachelor of Commerce from the Australian National University in Canberra and earned the Chartered Financial Analyst designation, serving as a past president of the CFA Society of Sydney. In her spare time she likes to sing with her chamber choir, make music with her daughters and hear live music as much as possible, which she says provides a welcome counterpoint to days spent examining data sets to find that next alpha-generating gem. 1 The Smart Investor Blue Ribbon Award, assessed by Morningstar, was awarded to the State Street Australian Equity Fund on August 19, 2016. The award recognised the fund as ‘the best strategy’ for end investors in the prevailing market environment over 1- and 3-year return and risk outcomes. Q3&4 2016 | The New Active 16 The Quest to Harness Cyclicality for Better Risk and Return FACTOR JENYA EMETS, CFA Managing Director, Active Quantitative Equity, SSGA KISHORE KARUNAKARAN Portfolio Strategist, Active Quantitative Equity, SSGA Q3&4 2016 | The New Active 17 Factor Timing O timing model developed by our Active Quantitative Equity team to universally defined factors, the same as those used in our Smart Beta strategies, to improve riskadjusted returns. ne of the hallmarks of SSGA’s active quantitative investment process is maintaining consistent exposures to factors with durable, long-term value. At the same time, empirical evidence pointing to the inherent cyclicality of factors has fueled efforts to try to time them to improve both return potential as well as the ability to manage drawdown risk. The difficulty of timing factors has been well-documented, given the uncertainty of exogenous elements affecting their behavior and the complexity of the underlying relationships. However, we believe at the margin it is possible to time certain elements that can add value and improve outcomes. Aside from adding breadth to the investment process by including another dimension to an active manager’s views, we believe trying to forecast factor pay-offs is a critical element in helping to reflect changes in the macro environment and account for the time-varying performance of factors. The following discussion looks at the kinds of systematic elements we believe are needed to time factors effectively. We describe how we applied the behavior leading to systematic mispricings will vary over time as will the degree to which market frictions slow down or distort price discovery. Understanding the interactions of these dynamics is key to forming expectations of a factor’s future pay-off. Building a Factor Timing Framework We believe an effective timing framework should try to specify some common drivers of factor returns, notably: Factor premia are composed of three main components: • Compensation for exposure to risk • Return potential from irrational market participant behavior • Factor persistence • Effects of systematic and structural market frictions, such as market circuit breakers or restrictions on short selling Each of these may be affected by different drivers at different times. For example, as market risk appetite waxes and wanes, the compensation for bearing an exposure to a risk embedded within a factor will move accordingly. Similarly, the extent to which markets overreact, underreact, or display other irrational Figure 1 Timing Value With Valuation Spreads Valuation Spread (%) Value IC Ratio 1.6 Information Coefficient of Value (rhs) 1.2 0.2 Valuation Spread (lhs) 0.1 0.8 0.0 0.4 -0.1 0.0 May 1991 Jan 1998 Sep 2009 May 2011 Nov 2015 Source: Universe: MSCI World Standard Index. Past performance is not an indication of future results. Returns do not represent those of an index but were achieved by mathematically combining the actual performance data of index-member stocks arranged and re-weighted according to their value ranking. The performance assumes no transaction and rebalancing costs, so actual results will differ. Index returns reflect capital gains and losses, income, and the reinvestment of dividends • Factor valuation -0.2 • Macroeconomic cycle phase • Risk sentiment Just as an investor would expect a cheaper security to outperform an expensive one, or a recent winning stock to continue to outperform a recent loser, the same applies to a factor, to the extent that its return is driven by securities based on that factor. To illustrate this kind of cyclicality, we plotted the information coefficient (IC) of the value factor against valuation spreads in Figure 1 over a 27-year period. The IC of value demonstrates the average power of the factor on a 12-month horizon. Valuation spreads measure the difference in book-to-price between the cheapest and the most expensive value basket and can indicate when a factor becomes cheap compared to its history. For example, as cheaper stocks get cheaper and more expensive stocks continue rising, valuation spreads get wider and the value factor underperforms. At the same time, when this theme starts to look cheaper, the opportunity set increases. When spreads widen and cheap stocks fall well below their fair value, market participants start looking for value opportunities and the factor begins to outperform again. Q3&4 2016 | The New Active 18 Factor Timing An important caveat to this relationship is the risk of rotating into value too early and withstanding some underperformance ahead of the factor’s recovery. While it can be argued that it is better to be in too early than too late, we also recognize the importance of adding other dimensions to factor timing to strengthen predictive power. Understanding where we are in the macroeconomic cycle and the degree of risk sentiment are two other important parts of a robust timing framework. These will create important head/or tailwinds for various factors, as market regimes shift across recession, recovery, boom periods and slowdowns. For example, while value stocks might be expected to fare better during economic recoveries, quality defensive stocks would be rewarded during recessionary and “risk-off” conditions. Sentimentlinked trending factors might do better during lower volatility regimes when market-leading stocks are not changing as much. While these general factor timing principles may seem reasonable, the reality of factor performance is far more nuanced because the underlying causal links are time-varying. For example, compare the performance of brick-andmortar–oriented value stocks struggling during the boom of the 1990s with value’s excellent performance during the recession that followed. Similarly, value stocks with high credit exposures suffered during the global financial crisis but showed strong performance during the economic recovery, as shown in Figure 2. Risk exposures embedded within sentiment factors may also vary over time in a more predictable manner, with risk-on sentiment coinciding with a prolonged market rally and becoming defensive after a bear market. As risk appetite changes, the implications for Figure 2 e o nce o e toc co i e ent co e i e % 50 1.0 Recession 40 0.8 30 0.6 Value 20 0.4 10 0.2 0 Feb 1995 Apr 2000 Jun 2005 Aug 2010 Oct 2015 0.0 Past performance is not an indication of future results. Returns do not represent those of an index but were achieved by mathematically combining the actual performance data of index-member stocks arranged and re-weighted according to their value ranking. The performance assumes no transaction and rebalancing costs, so actual results will differ. Index returns reflect capital gains and losses, income, and the reinvestment of dividends. Figure 3 Rolling 1 Year Correlation Between Axioma Momentum and Volatility 0.8 Momentum/Volatility 0.4 0.0 -0.4 -0.8 -1.2 Dec 1997 Jul 2002 Mar 2007 Oct 2011 Jun 2016 Source: SSGA Active Quantitative Equity Research team. a given factor will depend on how it is exposed to a particular risk at a certain point in time. Furthermore, these risk effects do not occur in isolation. The changes in risk on/risk-off sentiment can both reflect and affect the shifting expectations of the macroeconomic environment. Similarly, whether a factor is expensive or cheap, or has performed well or poorly recently — these dynamics will interact with, and affect, other potential predictors. In other words, an effective timing model needs to reflect the dynamism of the drivers of factor premia as well as a range of other possible interaction effects. Last but not least, care should be taken to reflect the regional specificity of these factor drivers while mitigating the risk of over-tuning model signals to potentially spurious sampling noise. Q3&4 2016 | The New Active 19 Factor Timing Applying Factor Timing to ti cto t et t te ie Those are the central issues we considered when building our factor timing model. To test its effectiveness, we applied our proprietary dynamic weights to our static multifactor Smart Beta strategy over an 18-year time period. Figure 4 illustrates the theoretical value added by SSGA’s dynamic factor timing model to a static, equally weighted allocation to value, quality, momentum and low volatility. In a backtest, the dynamic portfolio outperformed the static portfolio by 1.08% on an annualized gross basis over the 18-year period, while the tracking error versus the MSCI World index decreased by 0.05%, resulting in an improvement in the information ratio from 0.88 to 1.17. Of course, given the dynamic reweighting of the portfolio, monthly one-way turnover increased from 8.24% to 11.29%.1 Understanding the interdependencies of macroeconomic and market behavioral influences on factor premia is indeed at the heart of the active quantitative process. Moreover, we believe that advances in big data and the tools to leverage that data may improve our ability to more accurately comprehend and harness the cyclicality of factors for better outcomes. In the meantime, we see distinct advantages in using top-down drivers of factor timing to add value to the active investment process by: • Increasing long-term alpha potential • Enhancing portfolio diversification with a dynamic approach to factor weightings; and • Improving overall portfolio risk management by reducing the tail of drawdown risk. managers continue to develop their skill in identifying and harvesting alpha sources. While the results of the analysis we describe here are promising, it is important to acknowledge the notorious difficulties of timing factors with precision, especially in the short term. It is also important to emphasize that this is only one of many drivers of value in the active process as 1 As for transaction costs, a proprietary, tiered transaction cost model was applied during the performance analysis. The levels of transaction costs varied across stocks but on average were about 9 basis points in our simulations. Figure 4 Applying SSGA’s Factor Timing Model to Smart Beta Factors % 12 2.0 9 1.5 6 1.0 3 0.5 0 Static Portfolio n Value Added n Tracking Error 0 Dynamic Portfolio n Turnover Information Ratio Backtest performance is not indicative of the past or future performance of any SSGA offering. The portion of results through 09/30/2015 represents a backtest of the Dynamic Factor-Timing model, which means that those results were achieved by means of the retroactive application of the model which was developed with the benefit of hindsight. All data shown above does not represent the results of actual trading, and in fact, actual results could differ substantially, and there is the potential for loss as well as profit. The performance does not reflect management fees, transaction costs, and other fees and expenses a client would have to pay, which reduce returns. Please reference the Backtested Methodology Disclosure for a description of the methodology used as well as an important discussion of the inherent limitations of backtested results. Figure 5 Cumulative Net Active Return % 400 Dynamic Portfolio 300 200 Static Portfolio 100 0 Jan 1997 Aug 2000 May 2004 Nov 2011 Sep 2015 Past performance does not guarantee future results. Q3&4 2016 | The New Active 20 THE RISE OF ARTIFICIAL INTELLIGENCE In the Search for Alpha JEAN-SEBASTIEN PARENT-CHARTIER Senior Quantitative Research Analyst, SSGA e i eo ti ci nte i ence T he enormous amounts of data generated by “The Internet of Everything” might be expanding the potential for active managers to detect new sources of alpha. But it is the dramatic advances in artificial intelligence (AI) technologies that seem to offer some of the most promising methods of actually harnessing the true value in big data. Recent breakthroughs in artificial intelligence, particularly in the area of deep learning, suggest that the new AI technologies could be poised to revolutionize research across any number of fields, including investment management. Relentless Research Assistant Likened to a “relentless research assistant,” deep learning can accomplish a wide variety of tasks without human supervision and learn to recognize patterns through the act of processing vast quantities of data. At its core, deep learning originates from the work on neural networks dating back to the 1950s, the dawn of artificial intelligence. Although these early neural networks showed theoretical promise, the meager computing power available at the time severely handicapped their ability to mimic certain features of intelligence. Chief among them was layered learning; that is, absorbing simple concepts first and then using them to understand more complex ones. Deep learning incorporates this critical feature by building deep neural networks that drastically reduce the amount of human intervention and curation required to develop iterative learning.1 One of the most dramatic examples of the degree of complexity that deep learning can now master was the ability of Google’s AlphaGo program earlier this year to defeat one of the world’s best players of Go, an ancient Chinese board game. The number of possible board positions in Go is said to exceed the number of atoms in the universe, and most experts expected it would take at least another decade for a computer to beat an expert human player.2 Accelerated Progress Given this accelerated progress, it is not surprising that AI is now driving millions of dollars of investment in startups and research into a range of possible applications from strengthening internet search engines to building self-driving cars, while at the same time inspiring visions of both wonder and worry. Worry to the extent that the new technologies could displace millions of low-skill workers around the world more quickly than they can be retrained for higherskill employment. This is a particularly concerning scenario for many emerging economies that have relied on global labor cost advantages for goods and services and could face what economists call a “premature deindustrialization” if automation spreads too quickly.3 But many historians liken fears about the potential implications of AI to similar anxieties 200 years ago about the march of machines during the Industrial Revolution, and there are still significant technological hurdles to be surmounted. In the meantime, many researchers are focused on more immediate and practical applications of deep learning that will help them better sift through growing volumes of data to find actionable insights. We believe that machine learning can help improve the ability to forecast returns and manage risk while also increasing the productivity of research processes. But as with all systematic processes, the quality of the output depends on the quality of the input; in other words, “garbage in, garbage out.” Improving Data Quality That is why SSGA’s Active Quantitative Equity team has made machine learning an important part of our big data innovation strategy aimed at improving how we assess the quality of data used in our models. We believe deep learning can measurably improve our ability to detect data anomalies and strengthen our ability to assess data integrity quickly and at a scale that was previously unthinkable with manual processes. We define a data anomaly as an instance where an erroneous data item prevents our investment models from functioning as expected. These anomalies can arise from errors such as data obtained in the wrong currency or unit, stale or missing data, or extreme data values. ecent breakthroughs in artificial intelligence, particularly in the area of deep learning, suggest that the new I technologies could be poised to revolutioni e research across any number of fields, including investment management. Q3&4 2016 | The New Active 22 e i eo ti ci nte i ence Previously, these anomalies were spotted using manual, hard-coded rules, derived from common sense or as a result of lessons learned from previous errors. This was a resource-intensive exercise unequal to the huge volumes associated with big data. Manually investigating each data item did not scale with such geometric increases in data volume. This challenge provided a perfect testing ground for developing deep learning algorithms for assessing data properties and flagging irregularities without human intervention. This improved data assessment has direct positive benefits for the integrity of the alpha models we use in our investment process. We focused our work on training a deep neural network to recognize anomalies for financial data that are either directly related or approximately related. The former refers to financial data such as prices and returns; return on equity, earnings and book value; sales in different currencies; and book to price. Financial data that are approximately related include items such as returns measured over different time horizons; realized versus forecast metrics such as earnings, sales and gross domestic product (GDP). Training the Algorithm First we “trained” the algorithm by feeding it a dataset with 10 million entries related to financial data with exact relationships. By processing this data, the algorithm began to recognize the mathematical relationships among the different categories of financial data. Then we began to add anomalies to test the algorithm’s ability to detect those instances where the mathematical relationships broke down. Figure 1 shows the high degree of accuracy the algorithm had in detecting anomalies both large and small. An error as small as a 0.002 deviation from the correct amount was able to be detected. While smaller anomalies might be missed, arguably their downstream effect on the integrity of the model outputs will be commensurately less impactful and likely immaterial. Obviously training a deep learning approach to recognize anomalies in data that are only approximately related is more difficult. The murkier and less certain the mapping between different data items, the foggier our lens is and the harder it is to see smaller problems. For example, a deep neural network will learn that forecasted and realized GDP growth is similar and may consider a 10% difference between them as an anomaly, but it would not consider suspicious a 0.1% difference, even if one of the two quantities were genuinely wrong. Our second exercise again used a dataset of 10 million entries to train the algorithm, but found that anomalies now needed to be about twice the size of the original items for the deep neural network to identify all of them (Figure 2). But this is still a significant time savings over combing the data manually for such defects. These are just two straightforward examples of how artificial intelligence can transform active quantitative investing. In the future, we believe that the combination of growing data sources and improved machine learning technology may revolutionize an active managers’ ability to identify and harvest new sources of alpha and open up a whole new chapter in our industry’s evolution. 1 2 3 The advance of deep learning is also challenging a fundamental tenet of data science wisdom which stipulates that pre-treating the data to satisfy machine learning requirements represents almost 80% of the task. Christopher Moyer, “How Google’s AlphaGo Beat a Go World Champion,” The Atlantic, March 28, 2016. “March of the Machines,” a special report on Artificial Intelligence, The Economist, June 25th – July 1st, 2016. e believe that the combination of growing data sources and improved machine learning technology will revolutioni e an active managers ability to identify and harvest new sources of alpha and open up a whole new chapter in our industry s evolution. Figure 1 Anomaly Detection in Exact Items Relationships Figure 2 Anomaly Detection in Approximate Items Relationships 280 Source: SSGA Active Quantitative Equity Research. As of July 1, 2016. For illustrative purposes only. Source: SSGA Active Quantitative Equity Research. As of July 1, 2016. Q3&4 2016 | The New Active 24 Looking through the factor lens at equity allocations may reveal unintended risks in portfolio exposures. In the current market environment, for example, many investors who seem to be taking a diversified approach have ended up with portfolios that are negatively exposed to value and positively exposed to high volatility. Dane Smith of our Investment Solutions Group highlights the benefits of monitoring and understanding such implicit factor bets, which may not be rewarded in the markets. By allocating to value and low volatility in combination, investors may align factor exposures toward those premia that have historically been compensated over the long run — and target better risk-adjusted returns. Applying the FACTOR DANE SMITH Investment Strategist Investment Solutions Group, SSGA MAKING IT WORK Case Study Truth and Consequences of Concentrated Value The argument for a concentrated value allocation is that value managers tend to take a disciplined approach to identifying higher quality companies with lower ratios of price to fundamental metrics such as book value, cash flow, dividends, earnings or sales. The historical evidence for a value strategy has been compelling, with empirical studies conducted by many academics finding that value is among the few equity factors that have earned better risk-adjusted returns over the long term compared to a benchmark index weighted by market capitalization. Factor performance can vary under different market and economic conditions, however. When the economy is healthy or recovering, for example, small cap stocks tend to do well. But when the business cycle is waning, small caps are likely to underperform. Quality and low volatility generally fare better in tougher market environments but not as well during market upturns. Similar to other factors in this regard, value has been sensitive to market distress and macro shocks, so its historical return pattern has shown short-term cyclicality. Prior to the global financial crisis starting in late 2008, securities with attractive valuations tended to exhibit lower betas and less volatility than the market overall. Yet in the search for relative safety after the crisis, lower beta stocks have moved out of reach for value approaches (see Figure 1). Passively owning cheaper companies could have the unintended consequence of exposure to higher volatility. In order to remain true to the style, value investors would have to shift their focus to higher beta companies that were distressed — with poor balance sheets, cash flows and income statements — or otherwise out of favor with the market. As a result, a concentrated portfolio of value securities could have higher risk relative to a cap-weighted benchmark. Value Factor Risk Decomposition. To demonstrate this, we can evaluate risk exposures across a representative grouping of concentrated value managers in the United States. This group comprises the top five strategies with the highest active exposures to the value factor as defined by the Axioma risk model. Considering the holdings in this representative value portfolio, we observe that growth and value are the two largest style factor deviations from the US Large Cap universe in Morningstar (see Figure 2). The portfolio is also underweight momentum, return on equity and large size (that is, overweight small size), and overweight dividend yield, market sensitivity and high volatility. These results are consistent with our intuition for fundamental value managers. When looking through the lens of an asset allocator, owning a concentrated value portfolio has merits. Concentrated managers have higher active risk and require lower capital allocations to Figure 1 Valuation of Low Beta Versus High Beta Has Reversed Since 2008 Median P/B 6 4 2 0 Dec 1991 — Q1 (Lowest) Beta Oct 1996 Aug 2001 Jun 2006 Apr 2011 Mar 2016 — Q5 (Highest) Beta Source: MSCI, FactSet, SSGA. Data as of June 30, 2016. Universe: MSCI World Index. The results shown do not represent those of a singular index, but were achieved by mathematically combining the actual data of index member stocks arranged and reweighted according to their beta ranking. Based on the consistency of its track record over the long run, the value premium may be perceived as evergreen. owever, value performance tends to be cyclical, so deciduous may be a more accurate description. Source: Richard Hannam and Taie Wang, CFA, “Understanding the Value Premium,” State Street Global Advisors IQ Insights, May 2016. Q3&4 2016 | The New Active 26 Applying the Factor Lens impact the factor risk exposures within their portfolios. As mentioned above, this representative concentrated value portfolio does have higher total risk and higher beta relative to the Russell 1000 benchmark. Asset allocators may find benefits in achieving a balance between the contributions of factor risk and asset specific risk to overall active risk. Stock selection could be a meaningful source of active risk for a skilled fundamental manager. Taking an active approach and having a differentiating fundamental view on each security might help to generate the risk-reduction benefit of owning value — and increase the potential for greater return. But in this example, the grouping of concentrated value managers has diversified away the specific risk. Low Volatility to the Rescue Investors seek to capture the low volatility factor because of the potential for reduced variability and marketlike returns in their portfolios — much like the traditional rationale for value investing. By allocating capital to a low volatility exposure, investors are generally attempting to obtain downside protection in falling markets, while also participating in up markets. Although the arguments for investing in these factors are similar, their Investors in the low vol factor anticipate lower volatility of returns and improved harpe ratios over time when compared to a cap-weighted inde . Figure 2 Factor Exposures and Risk Characteristics of a Representative Grouping of Concentrated Value Managers Growth Medium-Term Momentum Return-on-Equity (Large) Size Leverage Industries Market Exchange Rate Sensitivity Total Portfolio Risk (%) 17.40 Benchmark Risk (%) 16.31 Predicted Beta 1.04 Active Risk 4.16 Asset (%) Liquidity (High) Volatility eci c i Factor Risk (%) Market Sensitivity 16.49 83.51 Dividend Yield Value -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 Source: Axioma, FactSet, SSGA. Benchmark is Russell 1000 Index. Data as of December 31, 2015. Figure 3 Rolling 36 Month Correlations of Q1 Minus Q5 Value to Low Volatility Show Reversal 1.2 0.8 0.4 0.0 -0.4 -0.8 Nov 1989 May 1996 Nov 2002 May 2009 Nov 2015 Source: MSCI, FactSet, SSGA. Data as of June 30, 2016. Universe: US Large Cap Managers in Morningstar. Rolling 36 Month Information Coefficients (IC). IC is Spearman rank correlation of universe model alpha with 1 month ranked returns. The information coefficient measure the predictive power of asset return forecasts, and is a correlation ranging between -1 (weak) and +1 (strong). correlations show a stark difference in how value and low volatility factors have been trading (see Figure 3). Aside from the technology bubble in the late 1990s, value and low volatility stocks tended to be highly correlated leading up to the global financial crisis. But since then they have decoupled and now show a significant negative correlation. Investors who combine exposures to the value factor with exposures to low volatility may be able to benefit from the diversifying effects of this negative correlation — with the potential to avoid the unintended consequences of concentrated value while smoothing out performance over the long term. Q3&4 2016 | The New Active 27 Applying the Factor Lens Figure 4 Factor Exposures and Risk Characteristics of a 50% Concentrated Value and 50% Low Volatility Portfolio Further Reading Growth Medium-Term Momentum Market Sensitivity (Large) Size Return-on-Equity Leverage Total Portfolio Risk (%) 15.20 Benchmark Risk (%) 16.31 Predicted Beta 0.92 Exchange Rate Sensitivity Active Risk 2.27 Market Asset (%) Liquidity Industries eci c i Factor Risk (%) (High) Volatility 18.97 81.03 Dividend Yield Value -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 Source: Axioma, FactSet, SSGA. Benchmark is Russell 1000 Index. Data as of December 31, 2015. Combined Factor Risk Decomposition. To investigate this premise, we return to the factor risk decomposition analysis and consider these same attributes when allocating 50% to concentrated value and 50% to low volatility strategies. Following the same methodology as described above, the grouping of low volatility managers comprises the top five strategies with the highest active risk exposures to the low volatility factor. The combined portfolio maintains overweight exposures to value, dividend yield and small size — all risk premia that we expect to be compensated in the long term (see Figure 4). But now the portfolio has zero exposure to the volatility factor. This neutral positioning is beneficial because we believe that over the long run, high volatility is an uncompensated risk factor exposure. We find evidence of additional risk reduction benefits from the combination of value and low volatility. Total risk and predicted beta are lower, and the Mike Sebastian and Sudhakar Attaluri, “Conviction in Equity Investing,” The Journal of Portfolio Management, Summer 2014, pp 77–83. active risk of the portfolio has been reduced significantly — the result of diversification between value and low volatility — but still remains high, as desired. Using global factor indexes, we obtained similar results in the broad market, although the risk benefits were of lesser magnitude than those we measured with the available data across US active managers pursuing value and low volatility strategies. What this suggests to us is that concentrated active managers could be used to help enhance active risk and achieve greater active exposure to compensated risk factors — as long as we are mindful not to diversify away the specific risk that active management may provide. By combining value with low volatility, we have built a portfolio with positive exposure to value, lower beta and less total risk to the market, which has been a difficult feat to accomplish in today’s markets. Barry Glavin, CFA, and Brian Routledge, CFA, “Cheap for a Reason — Finding Value in Uncomfortable Places,” State Street Global Advisors IQ Insights, June 2015. Aligning Factor Exposures it Co en te i e i Once again, we see the usefulness of looking through the factor lens to understand the risk in an equity portfolio — whether or not it was intended. Constructing a diversified equity allocation focused on valuation has been shown to reduce asset specific risk and introduce unintended factor exposures. Combining a concentrated fundamental value approach with an active low volatility strategy in a thoughtful way may help to better align the portfolio’s factor exposures with compensated risk premia, as well as lowering predicted beta and total risk. In our view, this analysis demonstrates the benefit of combining and balancing factors to provide stability and increase return potential across all market conditions. Q3&4 2016 | The New Active 28 For investment professional use only. Not for public use. Smart Beta Multi-factor Strategy While diversification does not ensure a profit or guarantee against loss, investors in Smart Beta may diversify across a mix of factors to address cyclical changes in factor performance. However, factors may have high or increasing correlation to each other. Smart Beta Strategies A Smart Beta strategy does not seek to replicate the performance of a specified cap-weighted index and as such may underperform such an index. The factors to which a Smart Beta strategy seeks to deliver exposure may themselves undergo cyclical performance. As such, a Smart Beta strategy may underperform the market or other Smart Beta strategies exposed to similar or other targeted factors. In fact, we believe that factor premia accrue over the long term (5-10 years), and investors must keep that long time horizon in mind when investing. The back-tested performance shown in figure 4 was created by the SSGA Active Quantitative Equity Team. The historical back-test was performed using data as available at the historical point in time to eliminate any survivorship bias. The SSGA’s Dynamic Factor Timing Model was back tested in the Third quarter of 2015 using data from Jan 1997 – September 2015. The results shown do not represent the results of actual trading using client assets but were achieved by means of the retroactive application of an investment process that was designed with the benefit of hindsight, otherwise known as back-testing. Thus, the performance results noted above should not be considered indicative of the skill of the advisor or its investment professionals. The back-tested performance was compiled after the end of the period depicted and does not represent the actual investment decisions of the advisor. These results do not reflect the effect of material economic and market factors on decision making. In addition, back-tested performance results do not involve financial risk, and no hypothetical trading record can completely account for the impact of financial risks associated with actual investing. No representation is being made that any client will or is likely to achieve profits or losses similar to those shown. In fact, there are frequently significant differences between back-tested performance results subsequently achieved by following a particular strategy. The back-tested performance data is reported on a gross of fees basis, but net of administrative costs. Additional fees, such as the management fee, would reduce the return. For example, if an annualized gross return of 10% was achieved over a 5-year period and a management fee of 1% per year was charged and deducted annually, then the resulting return would be reduced from 61% to 54%. The performance includes the reinvestment of dividends and other corporate earnings and is calculated in US dollars. The alpha scores were created using SSGA’s Active Quantitative Equity Team’s Proprietary active emerging markets stock selection model. The risk model data was based on Axioma’s worldwide medium term fundamental risk model estimated with data as available at the historical point in time. Portfolio construction methodology is similar to that used in our Emerging Markets Defensive Equity Strategy. Monthly portfolios were created, and returns generated based on the results of a buy and hold strategy over the next month. Transaction costs were also included in the analysis and assumed to be 100 bps each way. Each component in the stock selection process — growth, value, sentiment, and quality — is being implemented in the same manner in which it was back tested. The whole or any part of this work may not be reproduced, copied or transmitted or any of its contents disclosed to third parties without SSGA’s express written consent. The views expressed in this material are the views of the SSGA Active Quantitative Equity Research team through the period ended 9/1/16 and are subject to change based on market and other conditions. This document contains certain statements that may be deemed forward-looking statements. Please note that any such statements are not guarantees of any future performance and actual results or developments may differ materially from those projected. Q3&4 2016 | The New Active 29 CONTACTS AUSTRALIA HONG KONG SINGAPORE State Street Global Advisors, Australia Ltd. 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Toranomon Hills Mori Tower 25F1–23–1 Toranomon, Minato-ku Tokyo 105–6325 Japan T +813 4530 7380 F +813 4530 7364 T +65 6826 7500 F +65 6826 7501 SWITZERLAND State Street Global Advisors AG Beethovenstrasse 19 Postfach, CH–8027 Zurich T +41 (0)44 245 70 00 F +41 (0)44 245 70 16 UNITED ARAB EMIRATES State Street Bank and Trust Company (Representative Office) Boulevard Plaza 1, 17th Floor Office 1703, PO Box 26838 Dubai, United Arab Emirates T +971 (0)4 437 2800 F +971 (0)4 437 2818 UNITED KINGDOM State Street Global Advisors Ltd. 20 Churchill Place Canary Wharf, London, E14 5HJ T +020 3395 6000 F +020 3395 6350 Authorized and regulated by the Financial Conduct Authority. Registered in England, Number 2509928; VAT No. 5776591 81. NETHERLANDS State Street Global Advisors Netherlands Ltd. Apollo Building, 7th floor Herikerbergweg 29 1101 CN Amsterdam T +31 (0) 20 7181701 F +31 (0) 20 7087329 A branch office of State Street Global Advisors Limited; authorized and regulated by the Financial Conduct Authority in the United Kingdom. UNITED STATES State Street Global Advisors State Street Financial Center One Lincoln Street Boston, MA 02111–2900 T +1 617 664 7727 F +1 617 664 4024 GERMANY State Street Global Advisors GmbH Brienner Strasse 59 D-80333 Munich T +49 (0)89 55878 100 F +49 (0)89 55878 440 Q3&4 2016 | The New Active 30 About Us For nearly four decades, State Street Global Advisors has been committed to helping our clients, and those who rely on them, achieve financial security. We partner with many of the world’s largest, most sophisticated investors and financial intermediaries to help them reach their goals through a rigorous, research-driven investment process spanning both indexing and active disciplines. With trillions* in assets, our scale and global reach offer clients access to markets, geographies and asset classes, and allow us to deliver thoughtful insights and innovative solutions. State Street Global Advisors is the investment management arm of State Street Corporation. * Assets under management were $2.30 trillion as of June 30, 2016. AUM reflects approx. $40.9 billion (as of June 30, 2016) with respect to which State Street Global Markets, LLC (SSGM) serves as marketing agent; SSGM and State Street Global Advisors are affiliated. ssga.com © 2016 State Street Corporation. All Rights Reserved. INST-6900 0816 Exp. Date: 09/30/17