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Smart Portfolio Construction: Minimum Volatility Strategies and Beta Arbitrage

Discover how to construct Minimum Variance Portfolios to minimize risk, implement Minimum Volatility Portfolios for better returns, and utilize Beta Arbitrage strategies in finance. Learn key insights and techniques for efficient portfolio management.

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Smart Portfolio Construction: Minimum Volatility Strategies and Beta Arbitrage

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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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