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Factor Investing with ETFs PowerPoint Presentation
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Factor Investing with ETFs

Factor Investing with ETFs

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Factor Investing with ETFs

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  1. Factor Investing with ETFs • Samuel Lee, ETF Strategist

  2. Outline • Model-based investing 101 • Factor-based view of the world • Practical factor investing • Presenter: Alex Bryan • Parting thoughts • Shameless product pitch

  3. Why go quant?

  4. Human foibles • Overconfidence • Most people think they’re above-averagedrivers, lovers, socializers, workers, etc. • Privilege vivid, resonant ideas and thoughts over statistical facts • Over-reliant on narrative thinking

  5. Models usually beat intuition • Paul Meehl, “Clinical vs. Statistical Prediction: A Theoretical Analysis and a Review of the Evidence”, 1954 • Found that in 20 studies, overall simple models beat trained experts • William Grove, et. al., “Clinical Versus Mechanical Prediction: A Meta-Analysis”, 2000 • Of 136 studies, 46% favored models, 48% tied, 6% favored humans • Philip Tetlock, “Expert Political Judgment”, 2006 • 20-year forecasting study on 300 experts; simple prediction models won.

  6. Imitating the best • Top investors are model-driven. • Warren Buffett: buy when price < intrinsic value with margin of safety • Jeremy Grantham: buy when current valuation < historical averages • Ray Dalio: Economy is a transaction-based machine driven by short-term debt cycle, long-term debt cycle, productivity growth • Jim Simons: ??? Black box models

  7. On data-mining/data-snooping

  8. Data-snooping • Definition: Testing many different models to obtain a desired outcome.

  9. The problem with scienceas practiced • Many studies turn out to be false. • John P.A. Ioannidis, “Why Most Published Research Findings are False”, 2005 • Argues current study methodologies don’t have enough statistical power and are biased to false positives. • Pharma giant Bayer couldn’t replicate 2/3rds of 67 studies it tried to replicate (2011) • Amgen couldn’t replicate more 90+% of 53 “landmark” papers in cancer research (2012)

  10. Skewed incentives • Publication bias: Statistically significant positive findings more likely to be published. • Encourages data-snooping by researchers to get published.

  11. Quantitative strategies: witchcraft? • Even worse incentives to data-snoop. • Joel M. Dickson, et. al., “Joined at the Hip: ETF and Index Development”, 2012 • Back-tested equity indexes beat U.S. market 12.25% annualized in back-tests, returned -0.26% annualized live.

  12. Here’s a back-test from 1997-2006 showing 10% ann. alpha

  13. Here’s subsequent live performance for the index

  14. Hallmarks of a good back-test • From most to least important: • Strong economic intuition • Intellectually honest source. Credibility! • Simple and transparent methodology. • Large sample size that spans many decades and many countries. • Economically and statistically significant results. • Then find multiple independent researchers coming to the same conclusion!

  15. Academia has a decent record in producing models • R. David McLean and Jeffrey Pontiff, "Does Academic Research Destroy Stock Return Predictability“, 2013. • Independently replicated 82 characteristics purported to predict stock returns. • Of 72 that could be replicated, returns after study on average decayed 35%--10% from statistical bias, 25% from arbitrage.

  16. The big two, value and momentum • Value – Tendency for stocks cheap by fundamental measures to outperform stocks expensive by such measures. • Usually defined as having low price/book. Low price/earnings, price/cashflow, price/sales and the like also work. • Momentum – Tendency for performance to persist. • Relative momentum assets are ones with highest relative 12-month returns. • Time-series momentum assets have positive 12-month returns.

  17. How a value strategy usually works • Define a universe of stocks. • Calculate a valuation ratio, most commonly price/book, for each stock. • Sort stocks. • Select a basket of the “cheapest”. • Hold for one year and repeat.

  18. High book/price (value) stock portfolios have outperformed Source: French Data Library

  19. How a stock-based momentum strategy usually works • Define a universe of stocks. • Calculate past return, usually 12 months, excluding the last month. • Sort stocks. • Select a basket of the highest-return. • Hold for one month and repeat.

  20. High momentum stock portfolios have outperformed Source: French Data Library

  21. Why believe these back-tests? • Strong economic intuition. • Value effect: Investors overextrapolate recent trends, leading to stocks overshooting to up or downside. • Momentum effect: Investors underreact to new information, and herd into same stocks/positions. • Identified by numerous credible and independent researchers. • Simple and transparent methodology. • Found in almost every country studied (except for Japan). • Statistically and economically significant.

  22. The factor-model view of the world

  23. What is a factor? • Acharacteristic that explains an asset's returns. • Risk factors: • Market/economic growth • Inflation • Duration • Illiquidity • Behavioral “factors”: • Value? • Momentum

  24. Factors : assets :: nutrients : foods • In factor theory, an asset’s expected returns are derived wholly from its exposure to various risk factors. • Assets are bundles of factors. • Risk factors represent unique and different kinds of “bad times.”

  25. Junk bonds stripped of interest-rate risk behave like stocks

  26. What is a (linear) factor model? • A theory about what explains an asset’s returns • Usually takes the form of a linear relationship

  27. Original factor model: CAPM • Asset’s expected returns determined by its covariance with market. where is the expected return of the asset, is the risk-free (cash) rate, is the asset’s beta to the market, and is the market return

  28. Lots of strict assumptions • Everyone rational • Risk averse • No one can influence prices • No transaction costs • Everything tradable • Investors only care about return and standard deviation • Everyone agrees on correlations, expected returns of all assets • One period • Investors can lend and borrow at risk-free rate

  29. Implications • All investors would own market portfolio. • Only differences are portion of cash + market portfolio + leverage. • Market portfolio is “mean-variance efficient”—no other combination can produce a superior risk-adjusted return

  30. Linear regression • Statistical procedure to estimate linear relationship between two sets of data. • Often interpreted as cause-effect relationship: x causes y. • CAPM is based on simple linear regression! • Example: How well does the market’s monthly excess return predict Fidelity Magellan’s FMAGX excess return?

  31. Fidelity Magellan versus market, April 2003-March 2013

  32. CAPM regression • Model parameters (betas) cannot be directly observed. They must be estimated. • CAPM regression: Where is a variable representing the excess return of the asset at time , is the intercept, is the asset’s estimated beta to the market, is the market’s excess return, and is a noise term.

  33. Abbreviated Excel Output

  34. CAPM doesn’t work! • Black, Jensen, Scholes, “The Capital Asset Pricing Model: Some Empirical Tests”, 1972. • Created portfolios of stocks sorted by CAPM beta. • Found that CAPM beta did not predict excess returns linearly, i.e., securities market line was too flat. • Low-volatility anomaly: Higher beta != higher returns

  35. Multi-factor models • Fama-French model: Added “size” and “value” to CAPM. • Fama and French argued size and value predicted and explained stock returns. • Carhart model: Added “momentum” to Fama-French model. • Jegadeesh and Titman “discovered” momentum effect. Carhart added it to FF.

  36. Carhart model • CAPM plus three new “factors”: value, size and momentum: Where (or “high minus low”) is the return of the value-factor-mimicking portfolio, (or “small minus big”) is the return of the size-factor-mimicking portfolio, and (“up minus down”) is the return of the momentum-factor-mimicking portfolio.

  37. Splitting the equity universe by size and value • Portfolios are intersections of size and value breakpoints, formed yearly at June end. • Market-cap breakpoint determined at June end. • B/P breakpoint determined previous fiscal year book/end of last year market-cap—can be up to 1.5 years stale! Median market cap Big value Small value 70th B/P percentile Big neutral Small neutral 30th B/P percentile Small growth Big growth

  38. Size factor construction • SMB, or small minus big, is the average return of three small portfolios minus average return of three big portfolios.

  39. Value factor construction • HML, or high minus low, is the average return of two value portfolios minus the average return of two growth portfolios.

  40. Splitting the universe by size and momentum • Portfolios are intersections of size and momentum breakpoints, formed monthly. • Market-cap breakpoint determined at June end. • Prior 2-12 returns are the 12-month returns of stocks, excluding most recent month. Median market cap Small up Big up 70th prior (2-12) percentile Small medium Big medium 30th prior (2-12) percentile Big down Small down

  41. Momentum factor construction • UMD, or up minus down, is the average return of two winner portfolios minus the average return of two loser portfolios.

  42. Momentum is the strongest; size is the weakest Source: French Data Library

  43. Carhart Regression Results for FMAGX

  44. Factor models raise the benchmark for active managers • A manager’s excess returns attributable to a factor is no longer “alpha” • Studies demonstrating active managers can’t outperform use factor models • Factor models have a hard time detecting statistically sig. alpha • Factor research is ongoing

  45. Quality/profitability • Firms with high profits, low leverage, low earnings variability, high payouts persistently outperform • “Gross profitability” defined as (revenues – cost of goods sold)/assets • Robert Novy-Marx, “The Other Side of value: The Gross Profitability Premium”, 2013 • Gross profitability is strong as value factor, but negatively related.

  46. Splitting the equity universe by size and gross profitability • Portfolios are intersections of size and profitability breakpoints, formed yearly at June end. • Market-cap breakpoint determined at June end. • GP/A breakpoint determined previous fiscal year book/end of last year market-cap—can be up to 1.5 years stale! Median market cap Small profitable Big profitable 70th GP/A percentile Big neutral Small neutral 30th GP/A percentile Small unprofitable Big unprofitable

  47. Profitability factor construction • PMU, or profitable minus unprofitable, is the average return of two profitable portfolios minus the average return of two unprofitable portfolios.

  48. Quality stocks took a beating after the Nifty Fifty craze Source: Robert Novy-Marx Data Library

  49. At what price quality • Jeremy Siegel, “Valuing Growth Stocks: Revisiting the Nifty Fifty”, 1998 • Argues high-quality “Nifty Fifty” stocks of early 1970s warranted high valuations

  50. GMO Quality III GQETX Regression Results, 3/03-12/12