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Tactical Recommendations

Tactical Recommendations. Eric Falkenstein. CAPM does not work. Higher vol generally lower return  avoid high volatility/beta stocks!. Minimum Variance Portfolios. Minimize the variance of subsets of popular indices Lower vol, don’t lower return

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Tactical Recommendations

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  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, don’t 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 • T×T covariance matrix • Use Jones’ heteroskedasticity consistent principle components algorithm (Jones 2002) to get factors • This produces the T×K 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 S&P500, 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: Beta=0 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: S&P500 • 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: 3.3%+risk 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 investment’s success is unaffected by anything skill you have, you are gambling • Eg, lottery tickets • Sharpe>1 strategies are not sold in mass • People only sell to a general audience • Low alpha (eg, index funds, MVPs) • Negative alpha (Sturgeon’s law) • High Alpha takes moderate intelligence, high initiative • Hate, but don’t fear, failure. • Optimal search for one’s niche implies failure

  17. Finance is mainly about people, not math • Most value-add in finance about brand, scope, scale, relationships—not trenchant forecasting ability • Realize people are engaging in a repeated game, looking for a niche • Don’t be too cynical • Liar’s Poker: everyone’s 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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