Investment Portfolios

The Betterment Portfolio Strategy We continually improve the portfolio strategy over time in line with our research-focused investment philosophy. TABLE OF CONTENTS Introduction Global Diversification and  Asset Allocation Portfolio Optimization Tax Management Using Municipal Bonds Conclusion Citations I. Introduction Betterment has a singular objective: to help you make the most of your money, so that you can live better. Our investment philosophy forms the basis for how we pursue that objective: Betterment uses real-world evidence and systematic decision-making to help increase our customers’ wealth. In building our platform and offering individualized advice, Betterment’s philosophy is actualized by our five investing principles. Regardless of one’s assets or specific situation, Betterment believes all investors should: Make a personalized plan. Build in discipline. Maintain diversification. Balance cost and value. Manage taxes. To align with Betterment’s investing principles, a portfolio strategy must enable personalized planning and built-in discipline for investors. The Betterment Portfolio Strategy is comprised of 101 individualized portfolios, in part, because that level of granularity in allocation management provides the flexibility to align to multiple goals with different timelines and circumstances.  In this in-depth guide to the Betterment Portfolio Strategy, our goal is to demonstrate how the Betterment Portfolio Strategy, in both its application and development, contributes to how Betterment carries out its investing principles. When developing a portfolio strategy, any investment manager faces two main tasks: asset class selection and portfolio optimization. How we select funds to implement the Betterment Portfolio Strategy is also guided by our investing principles, and is covered separately in our Investment Selection Methodology paper. II. Global Diversification and Asset Allocation An optimal asset allocation is one that lies on the efficient frontier, which is a set of portfolios that seek to achieve the maximum objective for the lowest amount of risk. The objective of most long-term portfolio strategies is to maximize return, while the associated risk is measured in terms of volatility—the dispersion of those returns. In line with our investment philosophy of making systematic decisions backed by research, Betterment’s asset allocation is based on a theory by economist Harry Markowitz called Modern Portfolio Theory, as well as subsequent advancements based on that theory 1. A major tenet of Modern Portfolio Theory is that any asset included in a portfolio should not be assessed by itself, but rather, its potential risk and return should be analyzed as a contribution to the whole portfolio. Modern Portfolio Theory seeks to optimize maximizing expected returns and minimizing expected risk.  Other forms of portfolio construction may legitimately pursue other objectives, such as optimizing for income, or minimizing loss of principal. However, our portfolio construction goes beyond traditional Modern Portfolio Theory in five important ways: Estimating forward looking returns Estimating covariance Tilting specific factors in the portfolio Accounting for estimation error in the inputs Accounting for taxes in taxable accounts Asset Classes Selected for the Betterment Portfolio Strategy The Betterment Portfolio Strategy’s asset allocation starts with a universe of investable assets. Leaning on the work of Black-Litterman, the universe of investable assets for us is the global market portfolio 2. To capture the exposures of the asset classes for the global market portfolio, Betterment evaluates available exchange-traded funds (ETFs) that represent each class in the theoretical market portfolio. We base our asset class selection on ETFs because this aligns portfolio construction with our investment selection methodology. Betterment’s portfolios are constructed of the following asset classes: Equities U.S. Equities International developed market equities Emerging market equities Bonds U.S. short-term treasury bonds U.S. inflation protected bonds U.S. investment grade bonds U.S. municipal bonds International developed market bonds Emerging market bonds We select U.S. and international developed market equities as a core part of the portfolio. Historically, equities exhibit a high degree of volatility, but provide some degree of inflation protection. Even though significant historical drawdowns, such as the global financial crisis of 2008, demonstrate the possible risk of investing in equities, longer-term historical data and our forward expected returns calculations suggest that developed market equities remain a core part of any asset allocation aimed at achieving positive returns. This is because, over the long term, developed market equities have tended to outperform bonds on a risk-adjusted basis. To achieve a global market portfolio, we also include equities from less developed economies, called emerging markets. Generally, emerging market equities tend to be more volatile than U.S. and international developed equities. And while our research shows high correlation between this asset class and developed market equities, their inclusion on a risk-adjusted basis is important for global diversification. Note that Betterment’s portfolios exclude frontier markets, which are even smaller than emerging markets, due to their widely varying definition, extreme volatility, small contribution to global market capitalization, and cost to access. The Betterment Portfolio Strategy also includes bond exposure because historically, bonds have a low correlation with equities, and they remain an important way to dial down the overall risk of a portfolio. To promote diversification and leverage various risk and reward tradeoffs, the Betterment Portfolio Strategy includes exposure to several asset classes of bonds. Asset Classes Excluded from the Betterment Portfolio Strategy While Modern Portfolio Theory would have us craft the Betterment Portfolio Strategy to represent the total market, including all available asset classes, we exclude some asset classes whose cost and/or lack of data outweighs the potential benefit gained from their inclusion in the Portfolio Strategy. The Betterment portfolio construction process excludes private equity, commodities, and natural resources asset classes. Specifically, while commodities represent an investable asset class in the global financial market (it is however available as an asset class as part of Flexible Portfolio if investors wish to create their own custom portfolio), we have excluded commodities ETFs from the Betterment Portfolio Strategy because of their low contribution to a global stock/bond portfolio's risk-adjusted return. In addition, real estate investment trusts (REITs), which tend to be well marketed as a separate asset class, are not explicitly included in the Portfolio Strategy (but is also available as part of the Flexible Portfolio to create custom portfolios). The Betterment Portfolio Strategy does however provide exposure to real estate, but as a sector within equities. Adding additional real estate exposure by including a REIT asset class would overweight the Portfolio Strategy’s exposure to real estate relative to the overall market. III. Portfolio Optimization While asset selection sets the stage for a globally diversified portfolio strategy, we further optimize the Betterment Portfolio Strategy by tilting the portfolio strategy to drive higher return potential. While most asset managers offer a limited set of model portfolios at a defined risk scale, the Betterment Portfolio Strategy is designed to give customers more granularity and control over how much risk they want to take on. Instead of offering a conventional set of three portfolio choices—aggressive, moderate, and conservative—our portfolio optimization methods enable the Betterment Portfolio Strategy to contain 101 different portfolios. Optimizing Portfolios Modern Portfolio Theory requires estimating returns and covariances to optimize for portfolios that sit along an efficient frontier. While we could use historical averages to estimate future returns, this is inherently unreliable because historical returns do not necessarily represent future expectations. A better way is to utilize the Capital Asset Pricing Model (CAPM) along with a utility function which allows us to optimize for the portfolio with a higher return for the risk that the investor is willing to accept. Computing Forward-Looking Return Inputs Under CAPM assumptions, the global market portfolio is the optimal portfolio. Since we know the weights of the global market portfolio and can reasonably estimate the covariance of those assets, we can recover the returns implied by the market 3. This relationship gives rise to the equation for reverse optimization: μ = λ Σ ωmarket Where μ is the return vector, λ is the risk aversion parameter, Σ is the covariance matrix, and ωmarket is the weights of the assets in the global market portfolio 4. By using CAPM, the expected return is essentially determined to be proportional to the asset’s contribution to the overall portfolio risk. It’s called a reverse optimization because the weights are taken as a given and this implies the returns that investors are expecting. While CAPM is an elegant theory, it does rely on a number of limiting assumptions: e.g., a one period model, a frictionless and efficient market, and the assumption that all investors are rational mean-variance optimizers 5. In order to complete the equation above and compute the expected returns using reverse optimization, we need the covariance matrix as an input.  The covariance matrix mathematically describes the relationships of every asset with each other as well as the volatility risk of the assets themselves. Our process for estimating the covariance matrix aims to avoid skewed analysis of the conventional historical sample covariance matrix and instead employs Ledoit and Wolf’s shrinkage methodology, which uses a linear combination of a target matrix with the sample covariance to pull the most extreme coefficients toward the center, which helps reduce estimation error 6. Tilting the Betterment Portfolios based on the Fama-French Model Academic research also points to persistent drivers of returns that the market portfolio doesn’t fully capture. A framework known as the Fama-French Model demonstrates how equity returns are driven by three factors: market, value, and size 7. The underlying asset allocation of the Betterment Portfolio Strategy ensures the market factor is incorporated, but to gain higher returns from value and size, Betterment tilts the portfolios. For the actual mechanism of tilting, we turn to the Black-Litterman model. Black-Litterman starts with our global market portfolio as the asset allocation that an investor should take in the absence of views on the underlying assets. Then, using the Idzorek implementation of Black-Litterman, the Betterment Portfolio Strategy is tilted based on the level of confidence we have for our views on size and value 8. These views are computed from historical data analysis, and our confidence level is a free parameter of the implementation. Tilts are expressed, taking into account the constraints imposed by the liquidity of the underlying funds. Monte Carlo Simulations Betterment uses Monte Carlo simulations to predict alternative market scenarios. By doing an optimization of the Portfolio Strategy under these simulated market scenarios, Betterment averages the weights of asset classes in each scenario, which provides a more robust estimate of the optimal weights. Betterment believes this secondary optimization analysis alleviates the portfolio construction’s sensitivity to returns estimates and leads to more diversification and expected performance over a broader range of potential market outcomes. Thus, through our method of portfolio optimization, the Betterment Portfolio Strategy is weighted based on the tilted market portfolio, based on Fama-French, averaged by the weights produced by our Monte Carlo simulations. This portfolio construction process gives us a portfolio strategy designed to be optimal at any risk level for not just diversification and expected future value, but also ideal for good financial planning and for managing investor behavior. IV. Tax Management Using Municipal Bonds For investors with taxable accounts, portfolio returns may be further improved on an after-tax basis by utilizing municipal bonds. This is because the interest from municipal bonds is exempt from federal income tax. To take advantage of this, the Betterment Portfolio Strategy in taxable accounts is also tilted toward municipal bonds because interest from municipal bonds is exempt from federal income tax, which can further optimize portfolio returns. Other types of bonds remain for diversification reasons, but the overall bond tax profile is improved by tilting towards municipal bonds. For investors in states with the highest tax rates—New York and California—Betterment can optionally replace the municipal bond allocation with a more narrow set of bonds for that specific state, further saving the investor on state taxes. Betterment customers who live in NY or CA can contact customer support to take advantage of state specific municipal bonds. Conclusion After setting the strategic weight of assets in the Betterment Portfolio Strategy, the next step in implementing the strategy is Betterment’s investment selection process, which selects the appropriate ETFs for the respective asset exposure in a low-cost, tax-efficient way. In keeping with our philosophy, that process, like the portfolio construction process, is executed in a systematic, rules-based way, taking into account the cost of the fund and the liquidity of the fund. Beyond ticker selection is our established process for allocation management—how we advise downgrading risk over time—and our methodology for automatic asset location, which we call Tax Coordination. Finally, our overlay features of automated rebalancing and tax-loss harvesting are designed to be used to help further maximize individualized, after-tax returns. Together these processes put our principles into action, to help each and every Betterment customer maximize value while invested at Betterment and when they take their money home. Citations 1 Markowitz, H., "Portfolio Selection".The Journal of Finance, Vol. 7, No. 1. (Mar., 1952), pp. 77-91. 2 Black F. and Litterman R., Asset Allocation Combining Investor Views with Market Equilibrium, Journal of Fixed Income, Vol. 1, No. 2. (Sep., 1991), pp. 7-18. Black F. and Litterman R., Global Portfolio Optimization, Financial Analysts Journal, Vol. 48, No. 5 (Sep. - Oct., 1992), pp. 28-43. 3 Litterman, B. (2004) Modern Investment Management: An Equilibrium Approach. 4 Note that that the risk aversion parameter is a essentially a free parameter. 5 Ilmnen, A., Expected Returns. 6 Ledoit, O. and Wolf, M., Honey, I Shrunk the Sample Covariance Matrix, Olivier Ledoit & Michael Wolf. 7 Fama, E. and French, K., (1992). "The Cross-Section of Expected Stock Returns". The Journal of Finance.47 (2): 427. 8 Idzorek, T., A step-by-step guide to the Black-Litterman Model.  

ETF Selection For Portfolio Construction: A Methodology TABLE OF CONTENTS Why ETFs Total Annual Cost of Ownership Mitigating Market Impact Conclusion 1. Why ETFs? When constructing a portfolio, Betterment focuses on exchange traded funds (“ETFs”) securities with generally low-costs and high liquidity. An ETF is a security that generally tracks a broad-market stock or bond index or a basket of assets just like an index mutual fund, but trades just like a stock on a listed exchange. By design, index ETFs closely track their benchmarks—such as the S&P 500 or the Dow Jones Industrial Average—and are bought and sold like stocks throughout the day. ETFs have certain structural advantages when compared to mutual funds. These include: A. Clear Goals and Mandates Betterment generally selects ETFs that have mandates to passively track broad-market benchmark indexes. A passive mandate explicitly restricts the fund administrator to the singular goal of replicating a benchmark rather than making active investment decisions constituting market timing, building concentration in either a single name, group of names, or themes in an effort to beat the fund’s underlying benchmark. Adherence to this mandate ensures the same level of investment diversification as the benchmark indexes, makes performance more predictable, and reduces idiosyncratic risk associated with active manager decisions. B. Intraday Availability ETFs are transactable during all open market hours just like any other stock. As such, they are heavily traded by the full spectrum of equity market participants including market makers, short-term traders, buy-and-hold investors, and fund administrators themselves creating and redeeming units as needed (or increasing or decreasing the supply of ETFs based on market demand). This diverse trading activity leads to most ETFs carrying low liquidity premiums (or lower costs to transact due to competition from readily available market participants pushing prices downward) and equity-like transaction times irrespective of the underlying holdings of each fund. This generally makes ETFs fairly liquid, which makes them cheaper and easier to trade on-demand for activities like creating a new portfolio or rebalancing an existing one. C. Low Fee Structures Because most benchmarks update constituents (i.e., the specific stocks and related weights that make up a broad-market index) fairly infrequently, passive index-tracking ETFs also register lower annual turnover (or the rate a fund tends to transact its holdings) and thus fewer associated costs are passed through to investors. In addition, ETFs are generally managed by their administrators as a single share class that holds all assets as a single entity. This structure naturally lends itself as a defense against administrators practicing fee discrimination across the spectrum of available investors. With only one share class, ETFs are investor-type agnostic. The result is that ETF administrators provide the same exposures and low fees to the entire spectrum of potential buyers. D. Tax Efficiency In the case when a fund (irrespective of its specific structure) sells holdings that have experienced capital appreciation, the capital gains generated from those sales must, by law, be accrued and distributed to shareholders by year-end in the form of distributions. These distributions increase tax liabilities for all of the fund’s shareholders. With respect to these distributions, ETFs offer a significant tax advantage for shareholders over mutual funds. Because mutual funds are not exchange traded, the only available counterparty available for a buyer or seller is the fund administrator. When a shareholder in a mutual fund wishes to liquidate their holdings in the fund, the fund’s administrator must sell securities in order to generate the cash required to satisfy the redemption request. These redemption-driven sales generate capital gains that lead to distributions for not just the redeeming investor, but all shareholders in the fund. Mutual funds thus effectively socialize the fund’s tax liability to all shareholders, leading to passive, long-term investors having to help pay a tax bill for all intermediate (and potentially short-term) shareholder transactions. Because ETFs are exchange traded, the entire market serves as potential counterparties to a buyer or seller. When a shareholder in an ETF wishes to liquidate their holdings in the fund, they simply sell their shares to another investor just like that of a single company’s equity shares. The resulting transaction would only generate a capital gain or loss for the seller and not all investors in the fund. In addition, ETFs enjoy a slight advantage when it comes to taxation on dividends paid out to investors. After the passing of the Jobs and Growth Tax Relief Reconciliation Act of 2003, certain qualified dividend payments from corporations to investors are only subject to the lower long-term capital gains tax rather than standard income tax (which is still in force for ordinary, non-qualified dividends). Qualified dividends have to be paid by a domestic corporation (or foreign corporation listed on a domestic stock exchange) and must be held by both the investor and the fund for 61 of the 120 days surrounding the dividend payout date. As a result of active mutual funds’ higher turnover, a higher percentage of dividends paid out to their investors violate the holding period requirement and increase investor tax profiles. E. Investment Flexibility The maturation and growth of the global ETF market over the past few decades has led to the development of an immense spectrum of products covering different asset classes, markets, styles, and geographies. The result is a robust market of potential portfolio components which are versatile, extremely liquid, and easily substitutable. Despite all the advantages of ETFs, it is still important to note that not all ETFs are exactly alike or equally beneficial to an investor. Betterment’s investment selection process seeks to select ETFs that provide exposure to the desired asset classes with the least amount of difference between underlying asset class behavior and portfolio performance. In other words, we attempt to minimize the “frictions” (the collection of systematic and idiosyncratic factors that lead to performance deviations) between ETFs and their benchmarks. Betterment’s measure of these frictions is summarized as the “total annual cost of ownership”, or TACO: a composition of all relevant frictions used to rank and select ETF candidates for the Betterment portfolio. 2. Total Annual Cost of Ownership (TACO) The total annual cost of ownership (TACO) is Betterment’s fund scoring method, used to rate funds for inclusion in the Betterment portfolio. TACO takes into account an ETF’s transactional and liquidity costs as well as costs associated with holding funds. In addition to TACO, Betterment also considers certain other qualitative factors of ETFs, including but not limited to, whether the ETF fulfills a desired portfolio mandate and/or exposure. TACO is determined by two components, a fund’s cost-to-trade and cost-to-hold. The first, cost-to-trade, represents the cost associated with trading in and out of funds during the course of regular investing activities, such as rebalancing, cash inflows or withdrawals, and tax loss harvesting. Cost-to-trade is generally influenced by two factors: Volume: A measure of how many shares change hands each day. Bid-ask spread: The difference between the price at which you can buy a security and the price at which you can sell the same security at any given time. The second component, cost-to-hold, represents the annual costs associated with owning the fund and is generally influenced by these two factors: Expense ratios: Fund expenses imposed by an ETF administrator. Tracking difference: The deviation in performance from the fund’s benchmark index. Let’s review the specific inputs to each component in more detail: Cost-to-Trade: Volume and Bid-Ask Spread Volume: Volume is a historical measure of how many shares may change hands each day. This helps assess how easy it might be to find a buyer or seller in the future. This is important because it tends to indicate the availability of counterparties to buy (e.g., when Betterment is selling ETFs) and sell (e.g., when Betterment is buying ETFs). The more shares of an ETF Betterment needs to buy on behalf of our client, the more volume is needed to complete the trades without impacting market prices. As such, we measure average market volume for each ETF as a percentage of Betterment’s normal trading activity. Funds with low average daily trading volume compared to Betterment’s trading volume will have a higher cost, because Betterment’s higher trading volume is more likely to influence market prices. Bid-Ask Spread: Generally market transactions are associated with two prices: the price at which people are willing to sell a security, and the price others are willing to pay to buy it. The difference between these two numbers is known as the bid-ask spread, and can be expressed in currency or percentage terms. For example, a trader may be happy to sell a share at $100.02, but only wishes to buy it at $99.98. The bid-ask currency spread here is $.04, which coincidentally also represents a bid-ask percentage of 0.04%. In this example, if you were to buy a share, and immediately sell it, you’d end up with 0.04% less due to the spread. This is how traders and market makers make money—by providing liquid access to markets for small margins. Generally, heavily traded securities with more competitive counterparties willing to transact will carry lower bid-ask spreads. Unlike the expense ratio, the degree to which you care about bid-ask spread likely depends on how actively you trade. Buy-and-hold investors typically care about it less compared to active traders, because they will accrue significantly fewer transactions over their intended investment horizons. Minimizing these costs is beneficial to building an efficient portfolio which is why Betterment attempts to select ETFs with narrower bid-ask spreads. Cost-to-Hold: Expense Ratio and Tracking Difference Expense Ratio: An expense ratio is the set percentage of the price of a single share paid by shareholders to the fund administrators every year. ETFs often collect these fees from the dividends passed through from the underlying assets to holders of the security, which result in lower total returns to shareholders. Tracking Difference: Tracking difference is the underperformance or outperformance of a fund relative to the benchmark index it seeks to track. Funds may deviate from their benchmark indexes for a number of reasons, including any trades with respect to the fund’s holdings, deviations in weights between fund holdings and the benchmark index, and rebates from securities lending. It’s important to note that, over any given period, tracking difference isn’t necessarily negative; in some periods, it could lead to outperformance. However, tracking difference can introduce systematic deviation in the long-term returns of the overall portfolio when compared purely with a comparable basket of benchmark indexes other than ETFs. Finding TACO We calculate TACO as the sum of the above components: TACO = "Cost-to-Trade" + "Cost-to-Hold" As mentioned above, cost-to-trade estimates the costs associated with buying and selling funds in the open market. This amount is weighted to appropriately represent the aggregate investing activities of the average Betterment client in terms of cash flows, rebalances, and tax loss harvests. The cost-to-hold represents our expectations of the annual costs an investor will incur from owning a fund. Expense ratio makes up the majority of this cost, as it is the most explicit and often the largest cost associated with holding a fund. We also account for tracking difference between the fund and its benchmark index. In many cases, cost-to-hold, which includes an ETF’s expense ratio, will be the dominant factor in the total cost calculations. Of course, one can’t hold a security without first purchasing it, so we must also account for transaction costs, which we accomplish with our cost-to-trade component. 3. Minimizing Market Impact Market impact, or the change in price caused by an investor buying or selling a fund, is incorporated into Betterment’s total cost number through the cost-to-trade component. This is specifically through the interaction of bid-ask spreads and volume. However, we take additional considerations to control for market impact when evaluating our universe of investable funds. A key factor in Betterment’s decision-making is whether the ETF has relatively high levels of existing assets under management and average daily traded volumes. This helps to ensure that Betterment’s trading activity and holdings will not dominate the security’s natural market efficiency, which could either drive the price of the ETF up or down when trading. We define market impact for any given investment vehicle as the Betterment platform’s relative size (RSRS) in two key areas. Our share of the fund’s assets under managements is calculated quite simply as RS of AUM = ('AUM of Betterment' / 'AUM of ETF') while our share of the fund’s daily traded volume is calculated as RS Vol = ('Vol of Betterment' / 'Vol of ETF') ETFs without an appropriate level of assets or daily trade volume might lead to a situation where Betterment’s activity on behalf of clients moves the existing market for the security. In an attempt to avoid potentially negative effects upon our investors, we generally do not consider ETFs with smaller asset bases and limited trading activity unless some other extenuating factor is present. Conclusion As with any investment, ETFs are subject to market risk, including the possible loss of principal. The value of any portfolio will fluctuate with the value of the underlying securities. ETFs may trade for less than their net asset value (NAV). There is always a risk that an ETF will not meet its stated objective on any given trading day. Betterment reviews its asset selection analysis on a periodic basis to assess: the validity of existing selections, potential changes by fund administrators (raising or lowering expense ratios), and changes in specific ETF market factors (including tighter bid-ask spreads, lower tracking differences, growing asset bases, or reduced selection-driven market impact). Betterment also considers the tax implications of portfolio selection changes and estimates the net benefit of transitioning between investment vehicles for our clients. We use the ETFs that result from this process in our allocation advice that is based on your investment horizon, balance, and goal. For the details on our allocation advice, please see Betterment’s Goal Allocation Recommendation Methodology.

Goldman Sachs Smart Beta Portfolio Methodology The Goldman Sachs Smart Beta portfolio is meant for investors who seek to outperform a market-cap portfolio strategy in the long term, despite periods of underperformance. Our Smart Beta portfolio sourced from Goldman Sachs Asset Management helps meet the preference of our customers who are willing to take on additional risks to potentially outperform a market capitalization strategy. The Goldman Sachs Smart Beta portfolio strategy reflects the same underlying principles that have always guided the core Betterment portfolio strategy—investing in a globally diversified portfolio of stocks and bonds. The difference is that the Goldman Sachs Smart Beta portfolio strategy seeks higher returns by moving away from market capitalization weightings in and across equity asset classes. What is a smart beta portfolio strategy? Portfolio strategies are often described as either passive or active. Most index funds and exchange-traded funds (ETFs) are categorized as “passive” because they track the returns of the underlying market based on asset class. By contrast, many mutual funds or hedge fund strategies are considered “active” because an advisor or fund manager is actively buying and selling specific securities to attempt to beat their benchmark index. The result is a dichotomy in which a portfolio gets labeled as passive or active, and investors infer possible performance and risk based on that label. In reality, portfolio strategies reside within a plane where passive and active are just two cardinal directions. Smart beta funds, like the ones we’ve selected for this portfolio, seek to achieve their performance by falling somewhere in between extreme passive and active, using a set of characteristics, called “factors,” with an objective of outperformance while managing risk. The portfolio strategy also incorporates other passive funds to achieve appropriate diversification. This alternative approach is also the reason for the name “smart beta.” An analyst comparing conventional portfolio strategies usually operates by assessing beta, which measures the sensitivity of the security to the overall market. In developing a smart beta approach, the performance of the overall market is seen as just one of many factors that affects returns. By identifying a range of factors that may drive return potential, we seek the potential to outperform the market in the long term while managing reasonable risk. When we develop and select new portfolio strategies at Betterment, we operate using five core principles of investing: Personalized planning A balance of cost and value Diversification Tax optimization Behavioral discipline The Goldman Sachs Smart Beta portfolio strategy aligns with all five of these principles, but the strategy configures cost, value, and diversification in a different way than Betterment’s Core portfolio. In order to pursue higher overall return potential, the smart beta strategy adds additional systematic risk factors that are summarized in the next section. Additionally, the strategy seeks to achieve global diversification across stocks and bonds while overweighting specific exposures to securities which may not be included in Betterment’s Core portfolio, such as real estate investment trusts (REITs). Meanwhile, with the smart beta portfolio, we’re able to continue delivering all of Betterment’s tax-efficiency features, such as tax loss harvesting and Tax Coordination. Investing in smart beta strategies has traditionally been more expensive than a pure market cap-weighted portfolio. While the Goldman Sachs Smart Beta portfolio strategy has a far lower cost than the industry average, it is slightly more expensive than the core Betterment portfolio strategy. Because a smart beta portfolio incorporates the use of additional systematic risk factors, we typically only recommend this portfolio for investors who have a high risk tolerance and plan to save for the long term. Which “factors” drive the Goldman Sachs Smart Beta portfolio strategy? Factors are the variables that drive performance and risk in a smart beta portfolio strategy. If you think of risk as the currency you spend to achieve potential returns, factors are what determine the underlying value of that currency. We can dissect a portfolio’s return into a linear combination of factors. In academic literature and practitioner research (Research Affiliates, AQR), factors have been shown to drive historical returns. These analyses form the backbone of our advice for using the smart beta portfolio strategy. Factors reflect economically intuitive reasons and behavioral biases of investors in aggregate, all of which have been well studied in academic literature. Most of the equity ETFs used in this portfolio are Goldman Sachs ActiveBetaTM, which are Goldman Sach’s factor-based smart beta equity funds. The factors used in these funds are equal weighted and include the following: Good Value When a company has solid earnings (after-tax net income), but has a relatively low price (i.e., there’s a relatively low demand by the universe of investors), its stock is considered to have good value. Allocating to stocks based on this factor gives investors exposure to companies that have high growth potential but have been overlooked by other investors. High Quality High-quality companies demonstrate sustainable profitability over time. By investing based on this factor, the portfolio includes exposure to companies with strong fundamentals (e.g., strong and stable revenue and earnings) and potential for consistent returns. Low Volatility Stocks with low volatility tend to avoid extreme swings up or down in price. What may seem counterintuitive is that these stocks also tend to have higher returns than high volatility stocks. This is recognized as a persistent anomaly among academic researchers because the higher the volatility of the asset, the higher its return should be (according to standard financial theory). Low-volatility stocks are often overlooked by investors, as they usually don’t increase in value substantially when the overall market is trending higher. In contrast, investors seem to have a systematic preference for high-volatility stocks based on the data and, as a result, the demand increases these stocks’ prices and therefore reduces their future returns. Strong Momentum Stocks with strong momentum have recently been trending strongly upward in price. It is well documented that stocks tend to trend for some time, and investing in these types of stocks allows you to take advantage of these trends. It’s important to define the momentum factor with precision since securities can also exhibit reversion to the mean—meaning that “what goes up must come down.” How can these factors lead to future outperformance? In specific terms, the factors that drive the smart beta portfolio strategy—while having varying performance year-to-year relative to their market cap benchmark—have potential to outperform their respective benchmarks when combined. You can see an example of this in the chart of yearly factor returns for US large cap stocks below. You’ll see that the ranking of the four factor indexes varies over time, rotating outperformance over the S&P 500 Index in nearly all of the years. Performance Ranking of Smart Beta Indices vs. S&P 500 Why invest in a smart beta portfolio? As we’ve explained above, we generally only advise using Betterment’s choice smart beta strategy if you’re looking for a more tactical strategy that seeks to outperform a market-cap portfolio strategy in the long term despite potential periods of underperformance. For investors who fall into such a scenario, our analysis, supported by academic and practitioner literature, shows that the four factors above may provide higher return potential than a portfolio that uses market weighting as its only factor. While each factor weighted in the smart beta portfolio strategy has specific associated risks, some of these risks have low or negative correlation, which allow for the portfolio design to offset constituent risks and control the overall portfolio risk. Of course, these risks and correlations are based on historical analysis, and no advisor could guarantee their outlook for the future. An investor who elects the Goldman Sachs Smart Beta portfolio strategy should understand that the potential losses of this strategy can be greater than those of market benchmarks. In the year of the dot-com collapse of 2000, for example, when the S&P 500 dropped by 10%, the S&P 500 Momentum Index lost 21%. Given the systematic risks involved, we believe the evidence that shows that smart beta factors may lead to higher expected return potential relative to market cap benchmarks, and thus, we are proud to offer the portfolio for customers with long investing horizons.