Target Date Evolution: How Risk Capacity Analysis Differentiates Fidelity s Glide Path

Transcription

1 leadership series JANUARY 2016 Target Date Evolution: How Risk Capacity Analysis Differentiates Fidelity s Glide Path Andrew Dierdorf, CFA l Portfolio Manager Brett Sumsion, CFA l Portfolio Manager Mathew R. Jensen, CFA l Director, Target Date Strategies Emil Iantchev, PhD l Research Analyst KEY TAKEAWAYS The strategic asset allocation (i.e. glide path) for Fidelity s ClearPath Portfolios is informed by a risk boundary that, at each point in the life cycle, strikes what Fidelity believes is an appropriate balance between the total return needed for wealth accumulation and the risk associated with capital losses due to market events. To determine an appropriate level of risk for a target date investor throughout the age spectrum, we assessed the capacity for investors to withstand losses and the length of time it could take to recover from such losses using the 20 significant global equity market declines in the last 90 years. Our risk capacity framework also considers investors inherent bias against losses via a utility function that assumes the pain of loss is felt twice as acutely as the pleasure from an equivalently sized gain at any point in the life cycle. The framework employs a robust control approach that attempts to limit the cumulative pain over a set of worst-case scenarios, thus addressing the shortcomings of traditional risk models, which underestimate the likelihood of significant market events. Evaluating both behavioral and robust control elements, Fidelity s quantitative risk capacity framework establishes a risk boundary that seeks to provide a constraint on the level of long-term portfolio volatility at each age in the life cycle. In the context of a target date strategy, the notion of risk to an investor is typically viewed as having insufficient income through retirement. Because using an overly conservative asset mix could increase the risk of falling short of one s retirement income-replacement needs, an investor may be inclined to simply own a portfolio consisting only of asset classes with the highest return potential, in an attempt to accumulate a high level of wealth. Theoretically, this approach would provide a higher probability of success than a more conservative allocation. However, this approach could also subject the investor to significant portfolio declines, as investments with higher return potential often experience higher levels of volatility. If a major decline occurs ten years prior to retirement in the investor s investment horizon, it could leave little time to rebuild assets in a subsequent market recovery, or it could cause distress, pressuring the investor to abandon the strategy at possibly an unfavourable time. Therefore, the strategic asset allocation

2 leadership series JANUARY 2016 (i.e., glide path) of a target date strategy should balance the benefits of a portfolio with high expected returns and the costs of potential capital losses by maintaining an appropriate level of portfolio volatility across the age spectrum. But how much risk is appropriate? Because a target date strategy is designed to be a long-term holding, it is important to consider how economic and behavioral factors affect the way investors react in times of market stress and adverse short-term outcomes. While our analysis on actual behavior during periods of market stress provides insight into the short-term risk tolerance of investors (covered in our related paper, Target Date Evolution: Enhancements to Fidelity s ClearPath Portfolios, ) a risk capacity framework should also consider the impact on portfolio outcomes and behavior over time. Therefore, to evaluate an investor s risk capacity over longer time periods, we have developed a robust assessment of risk capacity that defines a risk boundary across the age spectrum. We believe this assessment of risk is unique in the industry because it evaluates investor behavior, the frequency of severe market events, and a varying investment time horizon to inform a risk constraint applied to the glide path for Fidelity s ClearPath Portfolios. Risk considerations in constructing a glide path In developing an investment strategy that focuses on providing an appropriate level of risk along the glide path, there are three primary factors that must be considered: (see Exhibit 1). 1 Kahneman and Tversky initially tested their loss aversion theory with hypothetical choices in a laboratory setting, 2 but subsequent studies have verified the validity of their findings using real monetary choices in both laboratory and field settings. 3 Wealth outcomes are experienced as losses or gains depending on how they differ from a wealth reference 1 Kahneman, Daniel, and Amos Tversky Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2): Tversky, A., and D. Kahneman (1992). Advances in Prospect Theory: Cumulative Representation of Uncertainty. Journal of Risk and Uncertainty 5: For a laboratory experiment using real monetary choices see: Tom, S. et al. (2007). The Neural Basis of Loss Aversion in Decision-Making under Risk. Science 315(5811): For a field experiment on loss aversion, see: Post, T., M. van den Assem, G. Baltussen, and R. Thaler. (2007). Deal or No Deal? Decision Making under Risk in a Large-Payoff Game Show. American Economic Review 98: Exhibit 1 At each age, losses relative to the wealth reference plan (i.e., accumulated wealth) bring twice as much pain as the pleasure associated with a gain. The steepness of the line illustrates that the pain people experience from a loss is twice as powerful as the pleasure they experience when they achieve an equivalent size of gain. Loss Aversion in the Context of Expected Retirement Wealth Investors have an emotional aversion to losses that is twice as great as the pleasure from similar gains. Utility Gains Significant market declines occur more frequently than traditional models predict. An investor s time horizon is a critical and ever-changing element that affects the trade-offs between risk and return differently across the age spectrum Reference Wealth Deviation Losses are more emotionally impactful than gains The behavioral elements of our risk framework are based on the groundbreaking work on loss aversion done by behavioral economics pioneers Daniel Kahneman and Amos Tversky. Their work suggests that individuals feel the pain of a loss twice as acutely as the pleasure they enjoy from an equivalent gain Losses 2 Wealth reference: The level or balance of expected assets at any point in the glide path based on the adherence to given assumptions. Utility: The satisfaction experienced by an investor at a given age in comparison to the wealth reference plan. Chart is illustrative and based on concept of Prospect Theory developed by Kahneman & Tversky (1979, 1992). 2

3 TARGET DATE EVOLUTION: HOW RISK CAPACITY ANALYSIS DIFFERENTIATES FIDELITY S GLIDE PATH plan, 4 which recent research has linked to an investor s expected wealth over time. In the context of target date investing, this result has both intuitive and quantitative appeal. When an investor s portfolio falls short of the level of assets needed to provide adequate income in retirement, the consequences can be significant, particularly during periods of market stress. Because this experience is painful both economically and behaviorally, such an outcome should ideally be avoided more than favorable outcomes in which the portfolio exceeds the target level of assets. Traditional risk analysis underestimates significant market events Historically, severe market environments have occurred more frequently than traditional quantitative models predict. While quantitative mean-variance risk models typically assume 4 Wealth reference plan or wealth reference level: The level or balance of expected assets at any point in the glide path based on the adherence to given assumptions. Defining the three phases of a retirement investment horizon Our risk framework defines three time frames during a life cycle the accumulation period, the transition period, and the retirement period when an allocation is determined based on an investor s remaining time horizon and capacity for withstanding major equity market declines. In the accumulation period, an investor is typically in his or her early working years and is looking to build wealth, so our accumulation portfolio allocation is focused on capital appreciation. At the other end of one s life cycle, we assume that an investor is in the latter years of retirement, so our retirement portfolio seeks a balance among total return, high current income (yield), and capital preservation. These two portfolios (accumulation portfolio and retirement portfolio) have fixed allocations, and serve as anchors for the asset allocation in the most aggressive target date portfolio (for younger investors) and the most conservative target date portfolio (for older investors). During the transition period, which covers the period between the accumulation period and the retirement period, our risk capacity analysis determines the slope of the glide path, or the rate of risk reduction that links the two static end-point portfolios. The asset allocation in the transition period adjusts in a nonlinear manner over time based on our quantitative risk capacity analysis. Exhibit 2 Quantitative modelling techniques often underestimate the frequency of major equity market declines. Rate of major equity market declines implied by normal distribution Great Depression ,000 2,000 Years required for event to occur is calculated as 1/(probability of a larger decline than the given event) where the probability is calculated based on normally distributed real equity returns (random walk with drift) with annualized mean of 7.81% and annualized standard deviation of 14.03%. Source: Fidelity Investments. 3,000 4,000 5,000 6,000 Years required for decline to occur under normal distribution 16,000 that investment returns follow a normal, or bell shaped, distribution, the actual frequency with which markets have produced significant declines has been much higher (see Exhibit 2). In a statistical context, if equity returns were normally distributed, annualized declines greater than 25% would occur once every 103 years. But in reality, global equity markets have experienced eight such declines since In fact, if stock returns were truly normally distributed, the global equity market downturn in 2008 would have been expected to occur once every 823 years. Traditional quantitative analysis underestimates the frequency of significant market declines, which suggests that a robust risk framework is necessary to account for the actual probability of extreme market events. Investment horizon matters The timing of a negative market event the point along an investor s life cycle at which a major decline occurs is critical in determining how risky the event is in the context of the long term goals and a finite investment horizon (see Defining the three phases of a retirement investment horizon, ). Indeed, the risk that exists in the subsequent period after a market 3

4 leadership series JANUARY 2016 decline depends in large part on whether the investor s time horizon is long enough for the market and his or her portfolio value to recover. In short, the investment time horizon is a critical component at each stage of the life cycle, and the probability of significant market events must be placed within this context. Our analysis of historical equity market declines shows there can be a long time period for the market to recover. More specifically, one can consider the 20 largest global equity market declines since Five years after each of the declines, the stock market had not returned to the previous peak, in real terms, in 8 of the 20 occasions. By 14 years after the initial decline, the market had recovered back to its pre-decline level in every circumstance, and in many cases substantially exceeded the pre-decline level (see Exhibit 3). The implication of these outcomes for risk management is that target date strategies must reflect the possibility that an extended recovery period may be needed after significant market events, and that over long time horizons equities have historically resumed their role as a potentially effective source of wealth. These market patterns must be integrated with the evolving investment time horizon of the investor in a target date fund to develop the appropriate risk capacity at each stage of the investor s life cycle. How Fidelity s quantitative risk framework addresses and combines the three factors To establish an appropriate risk level for the glide path, we must combine and balance the three risk considerations loss aversion, the frequency of significant market events, and the importance of time horizon with the need for capital appreciation and income, to help achieve the Fidelity ClearPath Portfolios goal of replacing approximately 45% of one s final Exhibit 3 It can take several years for an investor to recoup losses sustained after a major equity market decline under par after 5 years 3 under par after 7 years 1 under par after 10 years 0 under par after 14 years Real Wealth ($) Years Colored lines above represent the 20 worst global equity market declines* and the length of time it took for the index composite to recover back to its predecline level on an inflation-adjusted (i.e., real) total return basis in each of the 20 scenarios. Inflation: Consumer Price Index (CPI-U). Under par: value of index remains below pre-decline level. Source: Blend of 25% TSE 300 Index, 37.5% Fama-French U.S. Market Value -Weighted Index and 37.5% MSCI ACWI Ex-Canada-U.S. (MSCI EAFE prior to 1987). Additional information can be provided upon request. See endnotes for index definitions** and specific dates of market declines.* Past performance is no guarantee of future results. 4

5 TARGET DATE EVOLUTION: HOW RISK CAPACITY ANALYSIS DIFFERENTIATES FIDELITY S GLIDE PATH preretirement salary as income in retirement (given a set of assumptions, which are not guaranteed). 5 To do so, our risk capacity analysis addresses the three risk considerations through the following steps: 1. Loss aversion: defines an investor utility function Loss aversion is based on gains and losses relative to a wealth reference amount. We use wealth reference to represent this amount as an estimated value for target date investors as a population. We build the wealth reference plan around the retirement goal 6 of replacing approximately 45% of one s final salary through age 93, inflation adjusted. In constructing the wealth reference plan, we also consider 5 The glide path goal of Fidelity s ClearPath Portfolios is based on a set of assumptions regarding an investor s total savings rate, retirement savings start date, planning horizon, and annual salary increase, among others. 6 See Footnote #5. Exhibit 4 A quantitative value is assigned to the pain a target date fund investor experiences when an actual portfolio value falls below the wealth reference plan (expected portfolio value based on given assumptions) due to market declines. The value of this shortfall is twice as significant as the value of the pleasure that an investor experiences with an equivalent gain. Loss aversion utility applied in context of a target date portfolio investor Multiple of Final Salary (real) Accumulation Illustrative Wealth Reference Plan Gain = Pleasure Loss = Pain (2x) Transition Retirement For illustrative purposes only. Based on Prospect Theory research of D. Kahneman and A. Tversky. Accumulation : early working life; Retirement : late retirement years; Transition : years between Accumulation period and Retirement. Source: Fidelity Investments. long-term averages of secular assumptions about capital market returns and an age-varying asset mix that becomes more conservative as the investment horizon shortens. At each age, the representative investor experiences gains and losses depending on how the current portfolio wealth differs from the expected wealth determined by the wealth reference plan. At any time, when the wealth represented by the portfolio s actual value falls below its expected wealth for instance, during a significant stock market decline the deviation from this wealth reference point is considered to be more painful to investors than the satisfaction generated by a comparable wealth gain (see Exhibit 4). As a result, the satisfaction experienced by an investor at a given age can be defined by the following utility function: V(PW RefW) = { PW RefW if PW > RefW λ(pw RefW) if PW RefW } Glossary of terms V: the utility, or satisfaction, experienced by an investor at a given age in comparison to the wealth reference point. PW: the current wealth in the retirement portfolio. RefW: the wealth level under the wealth reference plan. λ: the coefficient of loss aversion, typically estimated to be around two. The coefficient of loss aversion measures aversion to risk over a single evaluation period (one year). The current evidence suggests that this static measure of risk aversion does not vary systematically with age. In dynamic, multiperiod settings, experienced gains and losses accumulate over the multiple periods, depending on the length of the planning horizon. Consequently, an investor in their 20s will have a different dynamic risk tolerance from an 80-year-old investor, even though each investor has similar static risk tolerance. 2. Significant market events: evaluates worst-case equity market decline scenarios The investment elements of our quantitative framework focus on the outcomes that investors would have experienced during historical periods of significant market stress. We concentrate on significant declines in equity values because equities historically have accounted for the majority of risk in a diversified portfolio. As a baseline for our risk capacity framework, we have evaluated the established utility function using actual market performance 5

6 leadership series JANUARY 2016 from the 20 worst periods for global equity returns during the past 90 years Time horizon: recognition of how time horizon can influence one s risk capacity As shown earlier, when an investment horizon is long (greater than 20 years), an investor can be less sensitive to significant market declines associated with a portfolio with greater volatility, while potentially benefiting from the long-term capital appreciation. As the investment horizon shortens with age, an investor has less time to recover from losses, so the portfolio s volatility needs to be reduced. Therefore, given a long-term need for returns in the accumulation period and a need for increased capital protection in the retirement period, the overall risk is reduced over time in the glide path to connect these two stages which defines the transition period. Why these risks matter The overall objective of the glide path is to establish an appropriate age-based asset allocation strategy for achieving the income replacement goal 8 in retirement, which we state as replacing approximately 45% of one s final preretirement salary as income throughout the retirement period. Given the need for improved investor savings behaviors today, high investment returns are needed to provide a reasonable likelihood for achieving success, while maintaining appropriate risk consistent with the goal. Therefore, a framework for balancing the three key elements outlined above with the needed investment returns is critical to establishing the asset allocation strategy. A framework for balancing the need for return with loss aversion, extreme events, and time horizon Fidelity s quantitative risk framework is designed to evaluate investors experience and sensitivity to losses, both at the time of a market decline and in subsequent periods. The framework focuses on producing outcomes such that the time to recover from significant market declines is consistent with the objectives and time horizon for investors of different ages. 7 Worst equity market periods defined as worst peak-to trough declines based on a blend of 25% TSE 300 Index, 37.5% Fama French U.S. Market Value -Weighted Index and 37.5% MSCI ACWI Ex Canada U.S. (MSCI EAFE prior to 1987). Additional information can be provided upon request. 8 See Footnote #5. Fidelity s quantitative risk framework is designed to capture investors experience and sensitivity to losses, both at the time of a market decline and in subsequent periods. To accomplish this goal, we impose robust controls 9 to identify the asset allocation strategy expected to produce a high level of investor satisfaction over the set of worst historical equity market experiences. The robust control approach seeks to limit the pain of falling behind the wealth reference plan over the set of scenarios, considering the total return required by the target date strategy s overall retirement income objective. The outcome of this process is an age-specific risk capacity that is translated into a risk boundary for the glide path. Specifically, our risk boundary framework encompasses the following steps: Combine the behavioral and investment market elements into a calculation of a hypothetical investor s utility during each of the worst 20 equity market declines. For investors at various ages, evaluate what the portfolio balance, expected cash flows, and experience would have been during a defined time horizon, using a range of potential asset allocation strategies over that horizon. Throughout the process, our loss aversion characteristic remains constant, while risk capacity diminishes over time due to an investor s shrinking time horizon. For each investor, the utility at the end of each year is calculated by determining whether the portfolio s value is above or below its expected level. The overall utility, or satisfaction, for the investor s experience can be calculated by aggregating the utility values over the relevant horizon. Building the risk boundary through backward induction For each hypothetical investor, we identify and select the asset allocation path that provides a high level for one s average 9 In this application of a risk framework for Fidelity s ClearPath Portfolios, robust control, a branch of academic control theory, emphasizes performance stability/robustness over a set of worst case equity market scenarios. 6

7 TARGET DATE EVOLUTION: HOW RISK CAPACITY ANALYSIS DIFFERENTIATES FIDELITY S GLIDE PATH utility over all the historical periods of market stress. This asset allocation path sets a long-term risk capacity level that seeks to protect the portfolio and the outcome for each investor during historical periods of market stress. For example, at age 84, an investor in Fidelity s ClearPath Portfolios has an assumed remaining planning horizon of 10 years (see Exhibit 5). Following a quantitative process known as backward induction (i.e., determining the asset allocation for investors at younger ages by using the asset allocation for investors at older ages as an end point), we evaluate a range of possible allocation paths that invest in different combinations of stocks, bonds, and short-term assets over time, finishing at a conservative portfolio allocation (i.e., 21% equities, with 4% expected volatility (standard deviation of returns) 10 at the assumed end age of 93. For each allocation path, the investor s utility values are calculated and evaluated, based on what the experience would have been during the 20 worst historical periods. We then select the allocation path that produces a high level of success over the periods. The risk capacity of an 84-year-old is low due to the investor s short time horizon, which results in selecting a path that maintains a conservative allocation over this entire period. For this investor, the risk capacity framework provides a guideline that recognizes the short time horizon and attempts to protect the investor from significant market declines when losses would be the most harmful. 10 The analysis framework used to develop Fidelity s ClearPath Portfolios begins by focusing on the allocations for each of the end points. These two portfolios the accumulation portfolio, which is focused on capital appreciation, and the retirement portfolio, which seeks a balance among total return, high current income (yield), and capital preservation are developed to achieve distinct goals at opposite ends of the risk spectrum and investor time horizon. The portfolios serve as anchors for the glide path allocation in the most aggressive target date portfolio (for younger investors) and the most conservative target date portfolio (for older investors). Accumulation portfolio: Based on Fidelity s long-term capital market assumptions, combined with stochastic and empirical modeling, the strategic allocation for the accumulation portfolio includes 92% in equities and 8% in fixed income, with a long-term expected volatility of approximately 13% (expressed via standard deviation). Retirement portfolio: The strategic allocation for the retirement portfolio includes 21% equities, 35% bonds, and 44% short-term investments, with a long-term expected volatility of approximately 4% (expressed via standard deviation). The expected volatility of these portfolios was determined based on the long-term historical volatility of three asset categories: equities, investment-grade bonds, and money market securities (or cash equivalent). The same process is applied for investors of differing starting ages and time horizons. At the beginning of retirement, an investor still has a reasonably long time horizon, and is starting to withdraw assets from the portfolio. For this investor, the risk capacity framework provides an upper boundary that is consistent with a balanced portfolio that gradually becomes more conservative as the time horizon shortens. By comparison, an investor in his or her later working life has a longer time horizon and continues to make contributions to the portfolio for a number of years until retirement. The results of our analysis illustrate that younger investors have greater risk capacity and more time to recover from periods of market stress. At these life-cycle stages, the risk capacity boundary serves primarily as a reminder that allocations that become too conservative reduce the probability of successfully achieving the long-term retirement objectives. Exhibit 5 is an illustrative diagram that shows how the application of this framework at various ages leads to a guideline for risk capacity at each age in the time horizon. The capacity for risk diminishes as an investor ages because the planning horizon shortens and withdrawals increase as a percentage of total wealth. It is important to note that this diagram is simplified to convey the process of how the risk boundary is constructed through backward induction. We evaluate the risk boundary for multiple interval age assumptions to understand the nature of the way risk capacity changes with adjustments in time horizons. Fidelity s quantitative risk framework is designed to capture investors experience and sensitivity to losses, both at the time of a market decline and in subsequent periods. Applying risk capacity in Fidelity s glide path design The analysis that supports the glide path for Fidelity s ClearPath Portfolios utilizes Fidelity s capital market assumptions +, investor/ participant behavior assumptions 11, and risk capacity methodology as research components that inform the decision-making process (for more details, see Leadership Series paper, Target Date Evolution: Enhancements to Fidelity s ClearPath Portfolios ). 11 See Footnote #5. 7

8 leadership series JANUARY 2016 The outcome of this process is an age-based asset allocation strategy that seeks to balance the need for total return and the need to limit the pain an investor experiences in the event of a market decline, all with respect to a wealth reference plan. Further, our risk capacity analysis considered the results of sensitivity testing 12 for each of the baseline assumptions. The expected long-term volatilities of the portfolios associated with Fidelity s ClearPath strategy provide a risk boundary along the age spectrum. The risk boundary acts as an upper boundary on the long-term portfolio risk (measured as standard deviation) for investors at each age. In this framework, the asset allocation for the retirement portfolio 13 serves as an anchor point for an investor 12 Sensitivity testing, or sensitivity analysis, in this context refers to evaluating outputs of a quantitative risk model by changing various assumptions (age, planning horizon, etc,) to understand the sensitivity of outcomes relative to changes in the assumptions. 13 See Footnote #10. at the end of the planning horizon (age 93). The backward induction process is applied at multiple ages and for multiple time horizons, with the accumulation portfolio 14 providing the allocation for younger investors. The allocation points are then linked across the different ages in the transition period to create one continuous allocation path. This asset allocation path defines the risk boundary at each age for the glide path (see Exhibit 6). While a more aggressive glide path may increase the likelihood for achieving successful outcomes, the risk boundary helps to provide protection for investors at each age during periods of market stress. As a consequence of this consideration, the slope of Fidelity s risk boundary the targeted level of portfolio volatility becomes more gradual during the decade prior to an assumed retirement date (Exhibit 6). In this period prior to retirement, the consideration of an investor s risk capacity, and the slope of the glide path, 14 See Footnote #10. Exhibit 5 The risk capacity framework identifies the limit on risk (i.e., portfolio volatility) for each age by selecting the allocation paths for investors of different ages that achieve favorable outcomes during historical periods of equity market declines. Step 3 Risk Boundary (HigherUtility Path) Possible Paths (Lower Utility) Portfolio Risk Capacity (%) Step 2 Step 1 Accumulation Transition Late Retirement Source: Illustrative example of how Fidelity uses backward induction process to identify the asset allocation path with a risk capacity limit at each age that seeks to achieve favorable outcome during historical periods of equity market stress. Accumulation : early working life; Retirement : late retirement years; Transition : years between Accumulation and Retirement. Source: Fidelity Investments. 8

9 TARGET DATE EVOLUTION: HOW RISK CAPACITY ANALYSIS DIFFERENTIATES FIDELITY S GLIDE PATH is critical to having sufficient income replacement in retirement. Hypothetically, an investor could simply switch from a portfolio allocation with high risk to a portfolio allocation with low risk in a very short period near the retirement date. However, a large adjustment to the asset allocation over a short age interval carries the risk of potentially locking in substantial losses if a market decline were to occur during this transition period. If the market decline were to continue for an extended period of time, the asset allocation strategy could adjust to become conservative too quickly, thereby locking in significant losses and diminishing the ability for the portfolio to recover. If the impact of losses due to market declines is to be limited, the slope of the glide path must be considered and balanced, particularly in the sensitive years around retirement when contributions end and withdrawals commence. When an investor reaches later retirement, the glide path reaches its most conservative asset allocation, as the investor s relatively short expected time horizon leaves less temporal or behavioral rationale for upside exposure. Asset allocation is translated into long-term volatility based on long-term capital market assumptions to arrive at the risk boundary. Risk capacity approach is unique Our refined assessment of risk capacity is unique (patent pending) in the industry, employing a combination of robust portfolio construction and risk preference analysis to develop a risk boundary across the age spectrum. This boundary considers both investor behavior and the market conditions experienced by investors, to manage asset longevity and stability during retirement. By accounting for the risk-bearing capacity of investors at each age, the framework strikes an appropriate balance between the total return needed for retirement wealth accumulation and the limitation of pain in the event of a market downturn. Exhibit 6 The risk capacity* analysis establishes a targeted level of portfolio volatility at each age in the life cycle. Risk capacity in Fidelity s glide path Expected Volatility (annualized standard deviation) 16% 14% 12% 10% 8% 6% 4% 2% 0% Higher Expected Risk Capacity Accumulation Transition Lower Expected Risk Capacity Retirement *Based on Fidelity s assumptions previously stated in this article. Expected portfolio volatility (risk capacity) is calculated using the equity rolldown that produces a high level of utility over the 20 market decline events in combination with the long-term capital market assumptions for asset return volatilities. Standard deviation: A statistical measure of spread or variability; the root mean square (RMS) deviation of the values from their arithmetic mean. Accumulation : Early working life. Retirement : Late retirement years. Transition : Years between Accumulation and Retirement. Source: Fidelity Investments. AUTHORS Andrew Dierdorf, CFA l Portfolio Manager Andrew Dierdorf is a portfolio manager for Fidelity Investments. Mr. Dierdorf currently co-manages several multi-asset-class portfolios, including target date strategies. He joined Fidelity in Brett F. Sumsion, CFA l Portfolio Manager Brett Sumsion is a portfolio manager for Fidelity Investments. In this role, Brett is responsible for co-managing Fidelity s U.S. and Canadian target date portfolios. He joined Fidelity in Mathew R. Jensen, CFA l Director, Target Date Strategies Mathew Jensen is the director of target date strategies in the Global Asset Allocation (GAA) division of Fidelity Investments. Mr. Jensen leads investment strategy execution and product design and innovation across the company s target date offerings, and directs target date investment research and thought leadership. Emil Iantchev, PhD l Research Analyst Emil Iantchev is a research analyst for Fidelity Investments. He currently focuses on research projects within the Asset Allocation Research Team, a unit of the Global Asset Allocation division. He joined Fidelity in Other contributors to this article include: Srinivas Maloor, Senior Quantitative Analyst, Global Asset Allocation; Edward McLaughlin, CFA, Director, Investment Analysis, Fidelity Investments Canada; Jeffrey Peyer, CFA, Product Solutions Analyst, Fidelity Investments Canada. 9

10 leadership series JANUARY FMR LLC. All rights reserved For Canadian Investors For Canadian prospects only. Offered in each province of Canada by Fidelity Investments Canada ULC in accordance with applicable securities laws. Views expressed are as of the date indicated, based on the information available at that time, and may change based on market and other conditions. Unless otherwise noted, the opinions provided are those of the authors and not necessarily those of Fidelity Investments or its affiliates. Fidelity does not assume any duty to update any of the information. Past performance is no guarantee of future results. Neither asset allocation nor diversification ensures a profit or guarantees against loss. Investment decisions should be based on an individual s own goals, time horizon, and tolerance for risk. Target date portfolios are designed for investors expecting to retire around the year indicated in each portfolio s name. The portfolios are managed to gradually become more conservative over time as they approach the target date. The investment risk of each target date portfolio changes over time as its asset allocation changes. The portfolios are subject to the volatility of the financial markets, including that of equity and fixed income investments in the U.S. and abroad, and may be subject to risks associated with investing in high-yield, small-cap, and foreign securities. Principal invested is not guaranteed at any time, including at or after the portfolios target dates. Target date portfolios are designed to help achieve the retirement objectives of a large percentage of individuals, but the stated objectives may not be entirely applicable to all investors due to varying individual circumstances, including retirement savings plan contribution limitations. Standard deviation: A statistical measure of spread or variability; the root mean square (RMS) deviation of the values from their arithmetic mean. Important Information Information presented herein is for discussion and illustrative purposes only and is not a recommendation or an offer or solicitation to buy or sell any securities. Third-party marks are the property of their respective owners; all other marks are the property of FMR LLC. * The 20 worst equity market declines referenced in the article are based on monthly data for a blend of 25% TSE 300 Index, 37.5% Fama-French U.S. Market Value -Weighted Index and 37.5% MSCI ACWI Ex-Canada U.S. (MSCI EAFE prior to 1987). The 20 worst declines are represented by the following dates, starting with the first month of the downturn period: Sep. 1929, Apr. 1937, Apr. 1940, Sep. 1944, Dec. 1945, May 1956, Aug. 1957, Apr. 1962, Feb. 1966, Apr. 1969, Apr. 1973, Feb. 1976, Oct. 1978, Jan. 1981, Apr. 1984, Sep. 1987, Aug. 1998, Apr. 2000, Feb. 2007, Feb More information can be provided upon request. + Capital market assumptions are forward-looking statements, which are based upon certain assumptions of future events. Actual events are difficult to predict and may differ from those assumed. There can be no assurance that forward-looking statements will materialize or that actual returns or results will not be materially different than those presented. The CRSP U.S. Stock databases contain daily and monthly market and corporate action data for securities with primary listings on the NYSE. CRSP refers to the Center for Research in Security Prices, located at the University of Chicago. CRSP monthly stock price data cover the period from December 1925 through the present. Index or benchmark performance presented in this document do not reflect the deduction of advisory fees, transaction charges, and other expenses, which would reduce performance. Certain data and other information in this research paper were supplied by outside sources and are believed to be reliable as of the date presented. However, Fidelity has not verified and cannot verify the accuracy of such information. The information contained herein is subject to change without notice. Fidelity does not provide legal or tax advice, and you are encouraged to consult your own lawyer, accountant, or other advisor before making any financial decision. These materials contain statements that are forward-looking statements, which are based upon certain assumptions of future events. Actual events are difficult to predict and may differ from those assumed. There can be no assurance that forward-looking statements will materialize or that actual returns or results will not be materially different than those presented. 10