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Portfolio Construction and Evaluation
The document outlines a portfolio construction process aimed at achieving global diversification while addressing client concerns about loss. It details asset allocation, capital market forecasting, and mean-variance optimization, along with a proposed moderate portfolio strategy. The analysis emphasizes the importance of managing exposure to loss and assesses potential wealth outcomes over a defined investment horizon.
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Portfolio Construction and Evaluation
- 1. 1© 2017 WindhamCapital Management, LLC 1 Portfolio Construction and Evaluation Jonathan Kazarian Windham Client Consultant 04/27/2017
- 2. 2© 2017 WindhamCapital Management, LLC Goal: Generate an efficient portfolio which satisfies our client’s needs.
- 3. 3© 2017 WindhamCapital Management, LLC Hypothetical Client One million in investable assets Requests global diversification but maintains a home bias Concerned about losses, particularly of 15% or more Goal: double value over 12 years
- 4. 4© 2017 WindhamCapital Management, LLC Agenda i. Discuss the asset allocation process ii. Develop Capital Market Forecasts iii. Construct a Mean Variance efficient portfolio iv. Evaluate portfolios using exposure to loss and potential wealth analytics
- 5. 5© 2017 WindhamCapital Management, LLC Asset Allocation • Identify eligible asset classes • Estimate return, risk and correlations • Generate efficient portfolios • Select portfolios that match our tolerance for risk
- 6. 6© 2017 WindhamCapital Management, LLC Eligible Asset Classes • Improve portfolio efficiency by raising return or lowering risk • Independent of other asset classes • Constituents of an asset class must be relatively similar • Have the capitalization capacity to absorb a meaningful fraction of our portfolio Mark Kritzman, “Toward Defining an Asset Class”, Journal of Alternative Investments, Summer 1999
- 7. 7© 2017 WindhamCapital Management, LLC Capital Market Forecasting “The process of selecting a portfolio may be divided into two stages. The first stage starts with observation and experience and ends with beliefs about the future performance of available securities. The second stage starts with relevant beliefs about future performances and ends with choice of portfolio. This paper is concerned with the second stage.” Harry Markowitz, “Portfolio Selection,” Journal of Finance, March 1952
- 8. 8© 2017 WindhamCapital Management, LLC WPA (Capital Market Forecasting)
- 9. 9© 2017 WindhamCapital Management, LLC Standard Deviation Not static throughout time Chow, G., Jacquier, E., Kritzman, M., and Lowry, K., Optimal Portfolios in Good Times and Bad, Financial Analysts Journal, May/June 1999 Data from 09/1991 - 06/2016, Turbulence Threshold at 20% • The table above shows standard deviation estimates for both normal and turbulent regimes • Volatility rises during times of turbulence Asset Class Full Sample Turbulent Large Cap Stock 16.05% 21.73% Small Cap Stocks 20.75% 26.54% Foreign Stocks 17.53% 22.66% Commodities 21.65% 28.18% Real Estate 22.24% 33.16% US Bonds 3.82% 6.06% Cash 0.64% 0.67%
- 10. 10© 2017 WindhamCapital Management, LLC Correlations Chow, G., Jacquier, E., Kritzman, M., and Lowry, K., Optimal Portfolios in Good Times and Bad, Financial Analysts Journal, May/June 1999 Data from 06/1991 - 06/2016, Turbulence Threshold at 20% Full Sample Large Cap Stock Small Cap Stocks Foreign Stocks Commodities Real Estate US Bonds Small Cap Stocks 0.83 1.00 Foreign Stocks 0.79 0.71 1.00 Commodities 0.26 0.29 0.37 1.00 Real Estate 0.54 0.61 0.51 0.17 1.00 US Bonds 0.06 -0.04 0.06 0.00 0.16 1.00 Cash 0.02 -0.01 -0.01 0.07 -0.04 0.13 Turbulence Large Cap Stock Small Cap Stocks Foreign Stocks Commodities Real Estate US Bonds Small Cap Stocks 0.80 1.00 Foreign Stocks 0.80 0.73 1.00 Commodities 0.38 0.46 0.44 1.00 Real Estate 0.61 0.69 0.60 0.35 1.00 US Bonds 0.19 0.15 0.19 0.01 0.21 1.00 Cash 0.18 0.14 0.12 0.25 0.22 0.25
- 11. 11© 2017 WindhamCapital Management, LLC Returns • We use Equilibrium Returns blended with views for specific asset classes • We estimate the market’s expected return equal to 7.5% and the risk-free return equal to 2.00% Data from 06/1991 - 06/2016 Asset Class Historical Equilibrium (Beta) View (Confidence) Blend Large Cap Stock 10.53% 8.94% (1.25) N/A 8.94% Small Cap Stocks 11.16% 10.18% (1.47) 11% (50%) 10.59% Foreign Stocks 6.64% 10.81% (1.58) N/A 10.81% Commodities 2.27% 8.31% (1.14) 5% (50%) 6.65% Real Estate 13.31% 9.37% (1.33) N/A 9.37% US Bonds 6.23% 2.65% (0.12) N/A 2.65% Cash 2.74% 2% (0) 1% (75%) 1.25%
- 12. 12© 2017 WindhamCapital Management, LLC Mean Variance Optimization “The process of selecting a portfolio may be divided into two stages. The first stage starts with observation and experience and ends with beliefs about the future performance of available securities. The second stage starts with relevant beliefs about future performances and ends with choice of portfolio. This paper is concerned with the second stage.” Harry Markowitz, “Portfolio Selection,” Journal of Finance, March 1952
- 13. 13© 2017 WindhamCapital Management, LLC WPA (Optimization)
- 14. 14© 2017 WindhamCapital Management, LLC Optimal Portfolios • The return and risk are derived using Equilibrium returns adjusted for our views and historical standard deviations and correlations Asset Class Conservative Moderate Aggressive Large Cap Stock 15.91% 20.44% 23.45% Small Cap Stocks 7.09% 8.12% 14.79% Foreign Stocks 19.17% 23.80% 31.87% Commodities 4.99% 6.26% 4.93% Real Estate 3.73% 5.02% 6.06% US Bonds 35.00% 35.00% 18.90% Cash 14.10% 1.36% 0.00% Return 6.03% 7.09% 8.50% Risk 7.97% 9.87% 12.76%
- 15. 15© 2017 WindhamCapital Management, LLC Portfolio Selection ■ Theoretical Approach: Risk Aversion ►“How many units of returns you are willing to give up in order to decrease risk by one unit” ■ In Practice: ►Exposure to Loss Probability of Loss Value at Risk ►Future Wealth “What’s your number? http://www.adrants.com/
- 16. 16© 2017 WindhamCapital Management, LLC Exposure to Loss ■We can estimate the likelihood that a portfolio with a particular expected return and standard deviation will experience a certain loss over a particular horizon. ►Probability of Loss ■Alternatively, we can easily estimate the largest loss a portfolio might experience given a certain level of confidence. ►Value-at-Risk ■For normal periods, risk parameters are based on the entire sample of returns ■For the turbulent regime, risk parameters are based on the turbulent sub-sample of returns
- 17. 17© 2017 WindhamCapital Management, LLC Exposure to Loss (Within Horizon) ■ Investors typically measure exposure to loss at the end of their investment horizon ►This ignores what may happen along the way; this is a dangerous oversight ■ Within Horizon risk provides a more realistic risk assessment Probability of a 10% Loss at the End of the Horizon is 20% Probability of a 10% Loss Within the Horizon is 80% Kritzman, M. and Risk, D., The Mismeasurement of Risk, Financial Analysts Journal, May/June 2002
- 18. 18© 2017 WindhamCapital Management, LLC Exposure to Loss (Probability of Loss) • Likelihood of losses increases during Turbulent periods • The current portfolio has less than a 1% chance of losing 15% or more at the end of 12 years • But there is over an 16% chance that the portfolio will depreciate by similar amounts along the way • This increases to nearly 40% in turbulent periods
- 19. 19© 2017 WindhamCapital Management, LLC Exposure to Loss (Value-at-Risk) • Again, we can observe drastic differences in value at risk • The worst outcome for a moderate investor given a 1% probability in normal periods is an increase in value of $31,000 • In comparison, the worst outcome in the interim period is a decline of at least $260,000 • When in periods of turbulence, the worst outcome increases to $444,000
- 20. 20© 2017 WindhamCapital Management, LLC Wealth Analysis (Monte-Carlo Simulation) Procedure: 1. Randomly generate 2000 different asset return histories using our estimated return, risk and correlation. Each history is 20 years in length 2. For each month, calculate the portfolio return 3. Compound the portfolio wealth by this return
- 21. 21© 2017 WindhamCapital Management, LLC Additional Evaluation Measures ■ Factor Analysis ►Identify and measure common sources of risk and return for managers, asset classes, and portfolios ►Go beyond asset allocation to identify the underlying exposures to specific sources of risk ■ Risk Budgets and Value at Risk Sensitivity ►Evaluate the risk of each asset class in isolation ►How does changing our allocation impact our portfolio’s risk? ■ Stress Testing & Scenario Analysis ►Estimate potential economic loses in unfavorable markets ►Examine portfolio-specific weakness Ex. how sensitive are our portfolios to interest rate shocks? ■ Wealth Analysis incorporating Cash Flows ►Use simulation to analyze how cash flows will impact future wealth for different portfolios
- 22. 22© 2017 WindhamCapital Management, LLC Proposed Portfolio: Moderate Probability of 15% Loss at the End of a 12 year Horizon Probability of 15% Loss Within Horizon 12 year Horizon Value at Risk at the 1% Level at the End of a 12 year Horizon Value at Risk at the 1% Level Within the 12 year Horizon Median Wealth Potential in 12 years 0.19% 9.05% -$21,874 $266,668 $2,179,778 Asset Class Moderate Large Cap Stock 20.44% Small Cap Stocks 8.12% Foreign Stocks 23.80% Commodities 6.26% Real Estate 5.02% US Bonds 35.00% Cash 1.36% Return 7.09% Risk 9.87%
- 23. 23© 2017 WindhamCapital Management, LLC Thank You Upcoming Webinars: Windham Software Overview TODAY at 3 pm (ET) & Tuesday, May 2nd at 11am (ET) Register online at: www.windhamlabs.com/webinars Questions and feedback: info@windhamlabs.com
- 24. 24© 2017 WindhamCapital Management, LLC Disclaimer The information contained in this presentation (the “Presentation”) is prepared solely for informational purposes. The Presentation is neither an offer to buy or sell nor a solicitation of an offer to buy or sell any security, or interests or shares in any fund or strategy. Historical data and other information contained herein is believed to be reliable but no representation is made as to its accuracy or completeness or suitability for any specific purpose. Past performance is not indicative of future performance, which may vary. There can be no assurance that the strategies’ investment objectives will be achieved. All strategies in this Presentation place investor capital at risk. Future returns are not guaranteed and a loss of principal may occur. References to market or composite indices, benchmarks or other measures of relative market performance over a specified period of time are provided for your information only. Reference to an index does not imply that the Windham portfolio will achieve returns, volatility or other results similar to the index. The composition of a benchmark index may not reflect the manner in which a Windham portfolio is constructed in relation to expected or achieved returns, investment holdings, portfolio guidelines, correlations or tracking error targets, all of which are subject to change over time. Prospective investors should not rely on this Presentation in making any investment decisions. Windham’s portfolio risk management includes a process for managing and monitoring risk, but should not be confused with, and does not imply, low risk. Asset classes and proportional weightings in Windham portfolios may change at any time without notice. Windham does not provide tax advice to its clients and all investors are urged to consult with their tax advisors with respect to any potential investment. Please refer to Windham’s ADV Part 2A for additional information. Windham and its owners disclaim any and all liability relating to this Presentation, including without limitation any express or implied representations or warranties for statements contained in, and omissions from, this information.
Editor's Notes
- #2 Full Name
- #3 In a moment I’ll outline our hypothetical client Keep in mind the goal we are trying to accomplish while constructing and evaluating our portfolios Switching between the WPA and PPT -Assist in construction process and provides a graphical interface to the analytics we’ll be reviewing Reminder about questions panel
- #4 Client was financially and emotionally impacted by global financial crisis
- #5 This is what we are going to do and here’s why we are going to do it What results do we see and what do they mean to us? Similar steps could be taken for an endowment Asset class level – not picking stocks or even ETFs – first thing we are going to do is asset allocation Important part of this process is establishing capital market forecasts Talk about Markowitz MVO – Construct a few portfolios – implement our investment knowledge to make “investable efficient portfolios” Propose alternative risk metrics than standard deviation Think not just about risk, also future wealth Other ways to evaluate include risk budgets and factor analysis
- #6 Lets drill down on the steps in the asset allocation process -Estimate cap market forecasts – forward looking estimation – critical to MVO -Use MVO to generate multiple portfolios along efficient frontier -PoL, VaR, Within Horizon, Mean Wealth Potential “What everyone did seem to know about is a study by Brinson, Beebower and Singer explaining that 91.5% of the quarterly variation in portfolio returns is due to asset allocation (the balance being attributed to security selection, market timing and other factors such as trade execution).” http://www.forbes.com/2010/06/08/value-at-risk-intelligent-investing-asset-allocation.html
- #7 WPA AFTER THIS SLIDE Out clients think about this as diversification but we think about it in terms of correlations
- #8 WPA AFTER THIS SLIDE Equilibrium return: -To estimate expected returns, we start by assuming markets are fairly prices; therefore, expected returns present fair compensation for the degree of risk each asset class contributes to a broadly diversified market portfolio. -These returns are called equilibrium returns, and we estimate them by first calculating the beta of each asset class with respect to a broad market portfolio based on historical standard deviations and correlations. -Then we estimate expected return for the market portfolio and the risk-free return. -We calculate the equilibrium return of each asset class as the risk-free return plus its beta times the excess return of the market portfolio -Admittedly, the markets are seldom if ever in equilibrium, but the pull in this direction is still very persistent -What we can do is adjust the expected return of each class to incorporate our views about departures from the fair value based on concept that return is proportional to risk -risk is relative to some market asset -equilibrium return believes you are only getting paid for the proportional risk -if you think that risk is based on noise you can enter your own views
- #10 What is turbulence? Why is it useful? -It’s important to note that standard deviations and correlations are not always stable through time. It is therefore useful to separate historical returns into those returns associated with normal times and those associated with periods market turbulence. -This measure of turbulence captures the statistical unusualness of a set of returns, given their historical pattern of behavior, including extreme price moves, decoupling of correlated assets, and convergence of uncorrelated assets. In layman terms, turbulence is way to mathematically identify unusual periods that tend to be associated with lower return to risk ratios. -This separate allows us to estimate these values for each regime and to stress test portfolios by measuring exposure to loss based on risk characterizes that prevail during turbulent periods. -as you would imagine, volatility rises during times of turbulence. This isn’t to say historical standard deviation is bad, just that we know that correlations and volatility are not static throughout time. We also know that those standard deviations are made up of a lot of noise. What we’re doing with the concept of turbulence is recognizing that most periods are just noise and not necessarily valuable in understanding the volatility of an asset class.
- #11 Contact Windham for more information on this concept
- #13 WPA AFTER THIS SLIDE -With this information, we use optimization to combine asset classes efficiently, so that for a particular level of expected return the efficiently combined asset classes offer the lowest level of risk (standard deviation) -When we plot several of these portfolios in dimensions of expected return and standard deviation we create the efficient frontier. Reminder that Markowitz is suggesting use of expected inputs, not historical means
- #15 Risk and return for each asset class – both increase Allocation drifts from less volatile asset classes to more volatile as you go from conservative to aggressive
- #16 Criticism of portfolio selection is that it relies to heavily on standard deviations – we propose
- #17 Probability of loss is used to determine the likelihood of a specified loss or gain over an investment horizon. Instead of evaluating the monetary loss or gain at a given confidence, an investor determines the probability that a specified monetary loss or gain will occur. Value at risk (VaR) is a method of assessing risk that estimates the worst expected loss over an investment horizon at a given confidence level. Value at risk uses the expected distribution of returns in order to estimate potential loss. We estimate value at risk from a portfolio’s expected return and standard deviation under the assumption that the portfolio’s returns are log-normally distributed.
- #18 WPA AFTER THIS SLIDE -Asset returns vary throughout an investment time-horizon. -Conventional value at risk and probability of loss only estimates total loss only at the end of an investment horizon without accounting for losses throughout the investment horizon. An investor may be very adverse to losses breaching a particular threshold and would therefore be interested in knowing the probability of breaching a certain level of loss at any moment during the horizon. -The likelihood of an end-of-horizon loss diminishes with time; the likelihood of a within-horizon loss never diminishes as a function of the length of the horizon (It increases at a decreasing rate but never decreases). -Only the first breach in the threshold is counted; once a path crosses the threshold line it counts toward the probability of the investment breaching the threshold within the time-horizon. -To estimate within-horizon variability, we use a statistic called “first-passage time probability”,
- #19 We’re not saying this is going to happen, but it could.
- #20 1 in 100 yr storm This is how much could be lost.