Volatility Effects

How reliable and variable are the most widely accepted long-short factor premiums across asset classes? Can investors time factor premium? In their June 2019 paper entitled “Factor Premia and Factor Timing: A Century of Evidence”, Antti Ilmanen, Ronen Israel, Tobias Moskowitz, Ashwin Thapar and Franklin Wang examine multi-class robustness of and variation in four prominent factor premiums:

1. Value – book-to-market ratio for individual stocks; value-weighted aggregate cyclically-adjusted price-to-earnings ratio (P/E10) for stock indexes; 10-year real yield for bonds; deviation from purchasing power parity for currencies; and, negative 5-year change in spot price for commodities.
2. Momentum – past excess (relative to cash) return from 13 months ago to one month ago.
3. Carry – front-month futures-to-spot ratio for equity indexes since 1990 and excess dividend yield before 1990; difference in short-term interest rates for currencies; 10-year minus 3-month yields for bonds; and, percentage difference in prices between the nearest and next-nearest contracts for commodities.
4. Defensive – for equity indexes and bonds, betas from 36-month rolling regressions of asset returns versus equal-weighted returns of all countries; and, no defensive strategies for currencies and commodities because market returns are difficult to define.

They each month rank each asset (with a 1-month lag for conservative execution) on each factor and form a portfolio that is long (short) assets with the highest (lowest) expected returns, weighted according to zero-sum rank. When combining factor portfolios across factors or asset classes, they weight them by inverse portfolio standard deviation of returns over the past 36 months. To assess both overfitting and market adaptation, they split each factor sample into pre-discovery subperiod, original discovery subperiod and post-publication subperiod. They consider factor premium interactions with economic variables (business cycles, growth and interest rates), political risk, volatility, downside risk, tail risk, crashes, market liquidity and investment sentiment. Finally, they test factor timing strategies based on 12 timing signals based on 19 methodologies across six asset classes and four factors. Using data as available from as far back as February 1877 for 43 country equity indexes, 26 government bonds, 44 exchange rates and 40 commodities, all through 2017, they find that: Keep Reading

Do equity strategy portfolios characterized by aggressive (conservative) scaling when portfolio volatility is recently low (high) reliably beat unmanaged performance? In their March 2019 paper entitled “On the Performance of Volatility-Managed Portfolios”, Scott Cederburg, Michael O’Doherty, Feifei Wang and Xuemin Yan assess whether practical volatility management is systematically attractive. For each of 103 anomalies (nine widely used factors and 94 other published anomalies), they construct a hedge portfolio that is each month long (short) the value-weighted tenth of stocks with the highest (lowest) expected returns. They then construct volatility-managed versions of these portfolios based on inverse variance of daily portfolio returns the prior month. Focusing on gross Sharpe ratio, they compare head-to-head performances of volatility-managed portfolios and unmanaged counterparts. Focusing on gross Sharpe ratio and certainty equivalent return (CER), they also employ an historical training subsample to estimate mean-variance optimal allocations for: (1) a strategy that chooses among a given volatility-managed portfolio, its unmanaged counterpart and a risk-free asset; and, (2) a strategy chooses between only the unmanaged counterpart and the risk-free asset. Using daily returns for the 103 equity hedge portfolios, they find that:

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How should investors feel about factor/multi-factor investing? In their February 2019 paper entitled “Alice’s Adventures in Factorland: Three Blunders That Plague Factor Investing”, Robert Arnott, Campbell Harvey, Vitali Kalesnik and Juhani Linnainmaa explore three critical failures of U.S. equity factor investing:

1. Returns are far short of expectations due to overfitting and/or trade crowding.
2. Drawdowns far exceed expectations.
3. Diversification of factors occasionally disappears when correlations soar.

They focus on 15 factors most closely followed by investors: the market factor; a set of six factors from widely used academic multi-factor models (size, value, operating profitability, investment, momentum and low beta); and, a set of eight other popular factors (idiosyncratic volatility, short-term reversal, illiquidity, accruals, cash flow-to-price, earnings-to-price, long-term reversal and net share issuance). For some analyses they employ a broader set of 46 factors. They consider both long-term (July 1963-June 2018) and short-term (July 2003-June 2018) factor performances. Using returns for the specified factors during July 1963 through June 2018, they conclude that:

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Do country stock market anomalies have trends? In his March 2018 paper entitled “The Momentum Effect in Country-Level Stock Market Anomalies”, Adam Zaremba investigates whether country-level stock market return anomalies exhibit trends (momentum) based on their past returns. Specifically, he:

• Screens potential anomalies via monthly reformed hedge portfolios that long (short) the equal-weighted or capitalization-weighted fifth of country stock market indexes with the highest (lowest) expected gross returns based on one of 40 market-level characteristics/combinations of characteristics. Characteristics span aggregate market value, momentum, reversal, skewness, quality, volatility, liquidity, net stock issuance and seasonality metrics.
• Tests whether the most reliable anomalies exhibit trends (momentum) based on their respective returns over the past 3, 6, 9 or 12 months.
• Compares performance of a portfolio that is long the third of reliable anomalies with the highest past returns to that of a portfolio that is long the equal-weighted combination of all reliable anomalies.

He performs all calculations twice, accounting in a second iteration for effects of taxes on dividends across countries. Using returns for capitalization-weighted country stock market indexes and data required for the 40 anomaly hedge portfolios as available across 78 country markets during January 1995 through May 2015, he finds that: Keep Reading

Do very old data confirm reliability of widely accepted asset return factor premiums? In their January 2019 paper entitled “Global Factor Premiums”, Guido Baltussen, Laurens Swinkels and Pim van Vliet present replication (1981-2011) and out-of-sample (1800-1908 and 2012-2016) tests of six global factor premiums across four asset classes. The asset classes are equity indexes, government bonds, commodities and currencies. The factors are: time series (intrinsic or absolute) momentum, designated as trend; cross-sectional (relative) momentum, designated as momentum; value; carry (long high yields and short low yields); seasonality (rolling “hot” months); and, betting against beta (BAB). They explicitly account for p-hacking (data snooping bias) and further explore economic explanations of global factor premiums. Using monthly global data as available during 1800 through 2016 to construct the six factors and four asset class return series, they find that:

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Does choice of multi-asset portfolio rebalance date(s) materially affect performance? In their October 2018 paper entitled “Rebalance Timing Luck: The Difference Between Hired and Fired”, Corey Hoffstein, Justin Sibears and Nathan Faber investigate effects of varying portfolio rebalance date on performance. Specifically, they quantify noise (luck) from varying annual rebalance date for a 60% S&P 500 Index-40% 5-year constant maturity U.S. Treasury note (60-40) U.S. market portfolio. Using monthly total returns for these two assets during January 1922 through June 2018, they find that: Keep Reading

Subscribers asked whether risk parity might work better than equal weighting of winners within the Simple Asset Class ETF Momentum Strategy (SACEMS), which each month selects the best performers over a specified lookback interval from among the following eight asset class exchange-traded funds (ETF), plus cash:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 2000 Index (IWM)
SPDR S&P 500 (SPY)
iShares Barclays 20+ Year Treasury Bond (TLT)
Vanguard REIT ETF (VNQ)
3-month Treasury bills (Cash)

To investigate, we focus on the SACEMS Top 3 portfolio and compare equal weighting to risk parity weights. We calculate risk parity weights at the end of each month by:

• Calculating daily asset return volatilities over the last 63 trading days (about three months, as suggested). This step includes Cash, which has very low volatility.
• Picking the volatilities of the Top 3 momentum winners.
• Weighting each winner by the inverse of its volatility.
• Scaling winner weights such that the total of the three allocations is 100%. This step essentially puts the entire portfolio into Cash when any of the Top 3 is Cash.

We use gross compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) to compare strategies. We check robustness by trying lookback intervals of one to 12 months for both momentum ranking and volatility estimation (increments of 21 trading days for the latter). Using monthly dividend-adjusted closing prices for asset class proxies and the yield for Cash during February 2006 (when all ETFs are first available) through December 2018, we find that: Keep Reading

Do investors systematically undervalue stocks that have relatively large book-to-market fluctuations? In their December 2018 paper entitled “The Value Uncertainty Premium”, Turan Bali, Luca Del Viva, Menna El Hefnawy and Lenos Trigeorgis test whether book-to-market volatility relates positively to future returns. They specify book-to-market volatility as standard deviation of daily estimated book-to-market ratios divided by their average over the past 12 months. They estimate book value using the most recent quarterly balance sheet plus analyst forecasts of net income minus expected dividends since that quarter. They lag all accounting data three months and analyst forecasts one month to avoid look-ahead bias. They then each month starting January 1986 rank stocks into tenths (deciles) by book-to-market volatility and reform a hedge portfolio that is long (short) the highest (lowest) decile. Using monthly and daily returns and firm accounting data for a broad sample of non-financial U.S. stocks and data for a large set of control variables during January 1985 through December 2016, they find that:

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Do unconventional portfolio construction techniques obscure how, and how well, betting against beta (BAB) works? In their November 2018 paper entitled “Betting Against Betting Against Beta”, Robert Novy-Marx and Mihail Velikov revisit the BAB factor, focusing on interpretation of three unconventional BAB construction techniques:

1. Rank weighting of stocks – BAB employs rank weighting rather than equal or value weighting, with each stock in high and low estimated beta portfolios weighted proportionally to the difference between its estimated beta rank and the median rank.
2. Hedging by leveraging – BAB seeks market neutrality by deleveraging (leveraging) the high (low) beta portfolio based on estimated betas rather than borrowing to buy the market portfolio to offset BAB’s short market tilt.
3. Novel beta estimation – BAB measures stock betas by combining market correlations based on five years of overlapping 3-day returns with volatilities based on one year of daily returns, rather than using slope coefficients of daily stock returns versus daily market returns.

Based on mathematical analysis and empirical results using returns for a broad sample of U.S. stocks during January 1968 through December 2017, they find that: Keep Reading

Are there advantages to using leveraged exchange-traded funds (ETF) to implement conventional asset class exposures? In their October 2018 paper entitled “A Portfolio of Leveraged Exchange Traded Funds”, William Trainor, Indudeep Chhachhi and Chris Brown investigate performance of diversified portfolios of 2X or 3X leveraged ETFs that limit exposures to those typically achieved with 1X ETFs. Specifically, when using 2X (3X) funds, allocations are only one half (one third) those for corresponding 1X ETFs. While this approach allows large allocations to a safe asset, it also exposes the portfolio to the higher expense ratios, internal financing costs, leverage decays and rebalancing frequencies of leveraged ETFs. The authors two strategic allocations:

1. Actual ETFs during 2010-2017 (see the first table below) – 1X portfolio allocations are 30% U.S. large caps, 10% U.S. midcaps, 10% U.S. small caps, 10% non-U.S. developed market stocks, 10% emerging market stocks, 5% real estate investment trusts (REIT), 5% >20-year U.S. Treasuries, 5% 7-year to 10-year U.S. Treasuries and 15% aggregate corporate bonds. “Savings” from holding leveraged ETFs goes to the aggregate bond ETF, for which there are no leveraged counterparts. Rebalancing occurs whenever equities combined deviate from the specified overall levels by more than 10%.
2. Simulated ETFs during 1946-2017 – 1X portfolio allocations are 50% S&P 500, 10% U.S. midcaps, 10% U.S. smallcaps, 15% >20-year U.S. Treasuries, 15% 7-year to 10-year U.S. Treasuries. An equal-weighted ladder of 1-year, 2-year, 5-year and 7-year U.S. Treasuries. “Savings” from holding leveraged ETFs goes to an equal-weighted ladder of 1-year, 2-year, 5-year, and 7-year treasury bonds.  Rebalancing occurs whenever equities combined deviate from the specified overall level by more than 10%.

Using daily returns for specified ETFs since 2010 and data required to simulate specified ETFs since 1946, all through December 2017, they find that: Keep Reading