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Performance Evaluation
Introduction• Complicated subject• Theoretically correct measures are difficult to
construct• Different statistics or measures are
appropriate for different types of investment decisions or portfolios
• Many industry and academic measures are different
• The nature of active managements leads to measurement problems
Abnormal PerformanceWhat is abnormal?Abnormal performance is measured:• Benchmark portfolio• Market adjusted• Market model / index model adjusted• Reward to risk measures such as the Sharpe
Measure:E (rp-rf) / p
Factors That Lead to Abnormal Performance
• Market timing• Superior selection
– Sectors or industries– Individual companies
Risk Adjusted Performance: Sharpe
1) Sharpe Index
rrpp - r - rff
rrpp = Average return on the portfolio= Average return on the portfolio
rrff = Average risk free rate= Average risk free rate
pp= Standard deviation of portfolio = Standard deviation of portfolio
returnreturn
pp
Risk Adjusted Performance: Treynor
2) Treynor Measure rrpp - r- rff
ßßpp
rrpp = Average return on the portfolio = Average return on the portfolio
rrff = Average risk free rate = Average risk free rate
ßßp p = Weighted average = Weighted average for portfoliofor portfolio
= r= rpp - [ r - [ rff + ß + ßpp ( r ( rmm - r - rff) ]) ]
3) 3) Jensen’s MeasureJensen’s Measure
pp
p
rrpp = Average return on the portfolio = Average return on the portfolio
ßßpp = Weighted average Beta = Weighted average Beta
rrff = Average risk free rate = Average risk free rate
rrmm = Avg. return on market index port. = Avg. return on market index port.
Risk Adjusted Performance: Jensen
= = Alpha for the portfolioAlpha for the portfolio
M2 Measure
• Developed by Modigliani and Modigliani• Equates the volatility of the managed portfolio
with the market by creating a hypothetical portfolio made up of T-bills and the managed portfolio
• If the risk is lower than the market, leverage is used and the hypothetical portfolio is compared to the market
M2 Measure: Example
Managed Portfolio Market T-bill
Return 35% 28% 6%
Stan. Dev 42% 30% 0%
Hypothetical Portfolio: Same Risk as Market
30/42 = .714 in P (1-.714) or .286 in T-bills
(.714) (.35) + (.286) (.06) = 26.7%
Since this return is less than the market, the managed portfolio underperformed
T2 (Treynor Square) Measure
• Used to convert the Treynor Measure into percentage return basis
• Makes it easier to interpret and compare• Equates the beta of the managed portfolio with the
market’s beta of 1 by creating a hypothetical portfolio made up of T-bills and the managed portfolio
• If the beta is lower than one, leverage is used and the hypothetical portfolio is compared to the market
T2 ExamplePort. P. Market
Risk Prem. (r-rf) 13% 10%
Beta 0.80 1.0
Alpha 5% 0%
Treynor Measure 16.25 10
Weight to match Market w = M/P = 1.0 / 0.8
Adjusted Return RP* = w (RP) = 16.25%
T2P = RP
* - RM = 16.25% - 10% = 6.25%
T2P = RP/Bp – RM = 13/.8 – 10 = 6.25%
Which Measure is Appropriate?It depends on investment assumptions1) If the portfolio represents the entire
investment for an individual, Sharpe Index compared to the Sharpe Index for the market.
2) If many alternatives are possible, use the Jensen or the Treynor measureThe Treynor measure is more complete because it adjusts for risk
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• Other Measures of Risk – A very popular measure of risk is called Value at
Risk or VAR. It is generally the amount you can lose at a particular confidence interval. It is only concerned with loss.
– At the 95% level, E(R) – 1.65(standard dev)– At the 99% level, E(R) – 2.33(standard dev)
14
– VAR Example
– Invest $100 at 10% with S.D. of 15%. What is the 95% Var over the year?
– 10% - 1.65(15%) = -14.75%– Var = 100 * -.1475 = $14.75
Semi-variance or semi-standard deviation
This statistic only measures variations below the mean or some threshold.
Semi-variance = 1/n * (summation of the average – rt)^2 where Where: n = the total number of observations below the meanrt = the observed valueaverage = the mean or target value of the data set. Standard deviation is just the square root of the above.
Semi-variance or semi-standard deviation
Example:You note the returns 3, 8, 3, 5, 11. Mean is 6. Thus
3 returns are less than the mean. The semi variance is [(6-3)^2 + (6-3)^2 + (6-5)^2]/3
= 6.33 and the semi-standard deviation is 6.33^.5 = 2.51. Compare this to the population standard deviation which is [(6-3)^2 + (6-8)^2 + (6-3)^2 + (6-5)^2+(6-11)^2]/5 = 9.6. Square root of 19.6 is 3.1%. Note that deviations above the mean are not considered, thus the large deviation(11 compared to 6) is ignored and the semi-standard deviation is actually smaller in this case.
Monte Carlo Methods• Monte Carlo methods can be used to ask what if
questions based on thousands of simulations. The range of values found during the simulations can be used to estimate possible outcomes and thus the risk associated with any position.
• For example, using the mean and standard deviation of the stock market, one could simulate possible return patterns over many years to see how a portfolio may be affected.
Limitations
• Assumptions underlying measures limit their usefulness
• When the portfolio is being actively managed, basic stability requirements are not met
• Practitioners often use benchmark portfolio comparisons to measure performance
Performance Attribution
• Decomposing overall performance into components
• Components are related to specific elements of performance
• Example components– Broad Allocation– Industry– Security Choice– Up and Down Markets
Process of Attributing Performance to Components
Set up a ‘Benchmark’ or ‘Bogey’ portfolio• Use indexes for each component• Use target weight structure
Appropriate Benchmark• Unambiguous – anyone can reproduce it• Investable• Measurable• Specified in Advance
Process of Attributing Performance to Components
• Calculate the return on the ‘Bogey’ and on the managed portfolio
• Explain the difference in return based on component weights or selection
• Summarize the performance differences into appropriate categories
Example, Performance Attributuion
Actual Ret Act. Wt Bmk Wt Index Ret
Semi-C 2% 0.7 0.6 2.5
Energy 1% 0.2 0.3 1.2
Bio-Tech 0.50% 0.1 0.1 0.5
Actual: (0.70 ´ 2.0%) + (0.20 ´ 1.0%) + (0.10 ´ 0.5%) = 1.65%
Benchmark: (0.60 ´ 2.5%) + (0.30 ´ 1.2%) + (0.10 ´ 0.5%) = 1.91%
Underperformance = 1.91% - 1.65% = 0.26%
Security Selection
Portfolio Sector Excess Man. Port
Sector Performance
Performance
Performance
Weight Contribution
Semi-C 2.00% 2.50% -0.50% 0.7 -0.35%
Energy1.00% 1.20% -0.20% 0.2 -0.04%
Bio-Tech
0.50% 0.50% 0.00% 0.1 0.00%
Contribution of security selection:
-0.39%
Sector Allocation
Actual Benchmark
Excess
Sector Weight Weight Weight Sector Ret. Contribution
Semi-C 0.7 0.6 0.1 2.50% 0.25%
Energy 0.2 0.3 -0.1 1.20% -0.12%
Biotech 0.1 0.1 0 0.50% 0.00%
Contribution of sector allocation:
0.13%
Lure of Active ManagementAre markets totally efficient?• Some managers outperform the market for extended
periods• While the abnormal performance may not be too
large, it is too large to be attributed solely to noise• Evidence of anomalies such as the turn of the year
existThe evidence suggests that there is some role
for active management