PM 5: Portfolio performance: empirical evidence

  • Created by: charlie
  • Created on: 20-05-18 18:07
X-Sectional evidence: Underperformance (not normal distribution)
High % of actively managed funds are outperformed by the benchmark
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X-Sectional evidence: Survivorship bias (upwards bias)
Many samples include only surviving funds (those that die are removed as time goes on)
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X-Sectional evidence: Fama 1) Aggregated returns on US Funds (steps)
1) Cap weighted portfolio of all active funds 2) Use FF-3 Factor model for risk-adjustment
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X-Sectional evidence: Fama 1) Aggregated returns on US Funds (results)
Net (after fees + expenses) alpha = -0.8% / Gross (before fees + expenses) alpha = 0% / Sensitivity of portfolio to market returns = 1
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X-Sectional evidence: Fama 2) Are winners skilled or lucky? (steps)
1) create a benchmark distribution (no skill/ all randomness) 2) compare wit actual distribution
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X-Sectional evidence: Fama 2) Are winners skilled or lucky? (step 1)
1) Create clone population of funds (mean alpha/ t(alpha) = 0) 2) Use bootstrap methodology (random sample of months) 3) Construct alpha + t(alpha) for samples 4) repeat 10,000 times
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X-Sectional evidence: Fama 2) Are winners skilled or lucky? (INVESTOR POV: net results
fund managers don't have enough skill to cover costs/ percentile for t(alpha) of actual < percentile for t(alpha) of randomness
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X-Sectional evidence: Fama 2) Are winners skilled or lucky? (ECONOMIC POV: gross)
small % of 'true losers' in extreme left tail (do worse than if due just to chance) small % of 'true winners' in extreme right tail (95th + percentile do better than if just due to chance)
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Performance persistence evidence: investment flows + compensation
Both +vely correlated + convex in returns (significant upside potential in gains + limited downside potential in losses)
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Performance persistence evidence: persistence is short lived
Best performers: disappear fastest (
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Performance persistence evidence: measured over shorter horizons
1) ST persistence at higher frequencies 2) Hold ST info 3) High turnover (changing holdings) (4) Investors cant predict LT
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Performance persistence evidence: reasons
1) Lack of real skills 2) Winner funds are victims of own success (limited superior investment opportunities = as AUM increase = harder to generate alpha)
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Performance persistence evidence: AUM trade-off
Economies of scale (lower fees due to fixed costs) Vs Diseconomies of scale (lower alpha due to investment opportunities)
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Other evidence of skill: Looking past alpha
Performance positively correlated with: 1) Education 2) Fund concentration ('best ideas' generate significant alpha but diluted by less successful positions)
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Other evidence of skill: less successful positions added due to...
1) Regulation (concentration risk) 2) Volatility decrease 3) Sharpe increase 4) AUM increase (get more as fixed fee%)
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Other evidence of skill: agency issue (conflicts of interest)
MANAGERS: like to increase fees (increase AUM by investing in more than just 'best ideas') INVESTORS: like increased returns/ alpha (will be optimal fund size)
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Empirical issues (statistical issues)
What do we use as rf rate?/ What do we use as (M)?/ Survivorship bias/ Inaccurate risk measurement/ fees arent fully transparent/ Stat. significance (outperformance or luck) takes a long time
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Conceptual issues (model issues)
CAPM model for expected returns isnt well-specified = erodes confidence on performance measures (Treynor & Jenson-alpha)/ Recent improvements: extension to include alpha-type measures (Carhart 4th momentum factor)
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Hedge Funds: Similarities
1) Distribution of returns is non-normal (use of derivatives/ shorting) 2) Mean-variance analysis doesnt work well 3) Constantly changing portfolio composition (can't fit linear characteristic lines)
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Hedge Funds: Increases performance measures
1) Increases Sharpe/ M: reducing total risk (illiquid assets/ stale prices) 2) Increases Treynore/ Jenson: reducing systematic risk (Reducing covariance/ correlation between asset + market)
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Hedge Funds: data-induced bias is worse due to lack of transparency
1) Survivorship bias (Hedge funds have higher returns) 2) Backfill bias (Hedge funds only report when returns are good)
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Other cards in this set

Card 2

Front

X-Sectional evidence: Survivorship bias (upwards bias)

Back

Many samples include only surviving funds (those that die are removed as time goes on)

Card 3

Front

X-Sectional evidence: Fama 1) Aggregated returns on US Funds (steps)

Back

Preview of the front of card 3

Card 4

Front

X-Sectional evidence: Fama 1) Aggregated returns on US Funds (results)

Back

Preview of the front of card 4

Card 5

Front

X-Sectional evidence: Fama 2) Are winners skilled or lucky? (steps)

Back

Preview of the front of card 5
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