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Tactical Recommendations

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Robeco, Unigestion, MSCI have minimum variance products ... Negative alpha (Sturgeon's law) High Alpha takes moderate intelligence, high initiative ... – PowerPoint PPT presentation

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Title: Tactical Recommendations


1
Tactical Recommendations
  • Eric Falkenstein

2
CAPM does not work
  • Higher vol generally lower return
  • ? avoid high volatility/beta stocks!

3
Minimum Variance Portfolios
  • Minimize the variance of subsets of popular
    indices
  • Lower vol, dont lower return
  • Robeco, Unigestion, MSCI have minimum variance
    products
  • Haugen and Baker (1991), Jagannathan and Ma
    (2003), Schwarz (2000), Clarke, DeSilva and
    Thorley (2006), Blitz and van Vliet (2007)

4
Minimum Volatility Portfolios
  • Take subset of popular stock indices
  • Find minimum variance weightings
  • TT covariance matrix
  • Use Jones heteroskedasticity consistent
    principle components algorithm (Jones 2002) to
    get factors
  • This produces the TK set of factors, F
  • regress each security against these factors to
    get the factor sensitivities for each security,
  • Create new covariance matrix

5
MVP Construction
  • Find weights with added constraints
  • No shorts
  • Cap on weight of 2 for SP500, 4 for other
    indices
  • Stocks found generally at max limit for longs
  • Redo each 6 months based on daily data from prior
    year

6
Indexes are not near 'Efficient'
7
Total Return for MVPs vs. Indices
8
Beta Arbitrage
  • If CAPM does not work, and equity premium is
    positive
  • Long 3 units 0.5 Beta stock, Short 1 unit 1.5
    beta stock
  • Zero Beta, long 2 units of stock!
  • Better if long beta has lower returns

E(R)
Rf
1.0
Beta
9
Beta Strategies
Data from 1962-2009, monthly returns, annualized
used top 80 of NYSE market cap (about 1500
stocks today)
10
Tracking Error to Beta Portfolios
11
Beta Arbitrage Beta0 Strats
Each strategy has a beta of 0, and is dollar long
12
Tracking Error to Beta Arb Portfolios
13
Tracking Error to Beta Arb Portfolios
14
Beta Arb Summary
  • Benchmark SP500
  • Sharpe 0.27
  • Beta 1.0
  • Return 10.3
  • For retail investors Beta 1.0 portfolio
  • Sharpe 0.37
  • Beta 1.0
  • Return 12.6
  • For business school grads Beta 0.5 portfolio
  • Sharpe 0.51
  • Beta 0.57
  • Return 11.5
  • MVPs have similar dominance to low beta focus
  • For finance professor Long Beta 0.5 short SP500
    index
  • Sharpe Information Ratio 0.39
  • Beta 0.0
  • Return 3.3risk free rate

15
Investment Advisor
  • Assume people want to do what everyone else is
    doing
  • Appealing asset allocation based on consensus,
    not volatility
  • Sell idea of trading envy for greed
  • MVPs
  • Beta Arbitrage
  • Will deviate from the benchmark

16
Seeking Alpha
  • If your investments success is unaffected by
    anything skill you have, you are gambling
  • Eg, lottery tickets
  • Sharpegt1 strategies are not sold in mass
  • People only sell to a general audience
  • Low alpha (eg, index funds, MVPs)
  • Negative alpha (Sturgeons law)
  • High Alpha takes moderate intelligence, high
    initiative
  • Hate, but dont fear, failure.
  • Optimal search for ones niche implies failure

17
Finance is mainly about people, not math
  • Most value-add in finance about brand, scope,
    scale, relationshipsnot trenchant forecasting
    ability
  • Realize people are engaging in a repeated game,
    looking for a niche
  • Dont be too cynical
  • Liars Poker everyones a fraud, investing is a
    scam
  • Must accept a certain level of alpha duplicity
  • Big company standard politics, need to be
    popular with customers and colleagues, not right
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