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## Factor Investing with ETFs

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**Factor Investing with ETFs**• Samuel Lee, ETF Strategist**Outline**• Model-based investing 101 • Factor-based view of the world • Practical factor investing • Presenter: Alex Bryan • Parting thoughts • Shameless product pitch**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**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.**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**Data-snooping**• Definition: Testing many different models to obtain a desired outcome.**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)**Skewed incentives**• Publication bias: Statistically significant positive findings more likely to be published. • Encourages data-snooping by researchers to get published.**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.**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!**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.**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.**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.**High book/price (value) stock portfolios have outperformed**Source: French Data Library**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.**High momentum stock portfolios have outperformed**Source: French Data Library**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.**What is a factor?**• Acharacteristic that explains an asset's returns. • Risk factors: • Market/economic growth • Inflation • Duration • Illiquidity • Behavioral “factors”: • Value? • Momentum**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.”**Junk bonds stripped of interest-rate risk behave like stocks****What is a (linear) factor model?**• A theory about what explains an asset’s returns • Usually takes the form of a linear relationship**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**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**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**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?**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.**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**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.**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.**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**Size factor construction**• SMB, or small minus big, is the average return of three small portfolios minus average return of three big portfolios.**Value factor construction**• HML, or high minus low, is the average return of two value portfolios minus the average return of two growth portfolios.**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**Momentum factor construction**• UMD, or up minus down, is the average return of two winner portfolios minus the average return of two loser portfolios.**Momentum is the strongest; size is the weakest**Source: French Data Library**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**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.**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**Profitability factor construction**• PMU, or profitable minus unprofitable, is the average return of two profitable portfolios minus the average return of two unprofitable portfolios.**Quality stocks took a beating after the Nifty Fifty craze**Source: Robert Novy-Marx Data Library**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