On Wednesday, February 13th we were joined by Jon Kazarian, Director of Business Development at Windham Labs, for a conversation on Portfolio Construction and Evaluation.
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
Client was financially and emotionally impacted by global financial crisis
It’s been about 10 years, as they’re nearing retirement they are worried about it happening again.
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
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
WPA AFTER THIS SLIDE
Out clients think about this as diversification but we think about it in terms of correlations
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
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.
Contact Windham for more information on this concept
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-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
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
Criticism of portfolio selection is that it relies to heavily on standard deviations – we propose
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.
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-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”,
One of the pitfalls in 2007 was people using short term trailing return to measure risk.
Look ishares risk profile for the MSCI Emerging Markets or EAFE ETF
Lowest risk profile, this is something we intrinsically know to be false
Managing client expectations
If you don’t start setting expectations now, what will happen in the inevitable downturn?
You’ll be in the same boat that you were in 2007 trying to convince clients not to sell everything at the bottom of the market
We’re not saying this is going to happen, but it could.