Value Premium

Does timing of factor premiums work? In his June 2016 paper entitled “My Factor Philippic”, Clifford Asness addresses three critiques of the exploitability of stock factor premiums:

1. Most factors are currently very overvalued (expected premiums are small), perhaps because of crowded bets on them.
2. Factor portfolios may therefore crash.
3. In fact, increasing factor valuations account for most of the historical premium (there are no essential premiums).

He considers five long-short factors: (1) value based on book-to-price ratio (B/P); (2) value based on sales-to-price ratio (S/P); (3) momentum (total return from 12 months ago to one month ago); (4) profitability (gross profits-to-assets); and, (5) betting-against-beta (long leveraged low-beta assets and short high-beta assets). He calculates each factor premium as average return to a capitalization-weighted portfolio that is each month long (short) the third of large-capitalization U.S. stocks with the best (worst) expected returns based on that factor. He estimates the time-varying valuation of a factor via a value spread, the ratio of the capitalization-weighted B/P (or S/P) of the long side of the factor portfolio to that of its short side. He tests a simple factor timing strategy that holds no position if the factor’s value spread is at its historical median and scales linearly up (down) to a 100% (-100%) position in the factor portfolio as the factor’s value spread increases to its 95th (decreases to its 5th) historical percentile. The initial look-back interval is 20 years (such that testing begins in 1988), expanding as more data become available. Using the specified factor premium data for January 1968 through January 2016, he finds that: Keep Reading

Does the January (turn-of-the-year) stock return anomaly affect value and momentum strategies applied at the country stock market level? In his June 2015 paper entitled “The January Seasonality and the Performance of Country-Level Value and Momentum Strategies”, Adam Zaremba investigates this question using four value and two momentum firm/stock metrics. The four value metrics, each measured over four prior quarters with a one-quarter lag and weighted by company according to the methodology of the associated stock index, are:

1. Earnings-to-price ratio (EP).
2. Earnings before interest, taxes, depreciation and amortization (EBITDA)-to-enterprise value (EV) ratio (EBEV).
3. EBITDA-to-price ratio (EBP).
4. Sales-to-EV ratio (SEV).

The two momentum metrics are:

1. Stock index return from 12 months ago to one month ago (LtMom).
2. Stock index return from 12 months ago to six months ago (IntMom).

He assesses strategy performance via returns in U.S. dollars in excess of one-month U.S. Treasury bill yield from hedge portfolios that are each month long (short) the equally weighted fifth of country stock indexes with the highest (lowest) expected returns based on each metric. He first reviews performances for all months and then focuses on turn-of-the-year (December and January) performances. Using monthly data for 78 existing and discontinued country stock market indexes during June 1995 through May 2015, he finds that: Keep Reading

How good can factor investing get? In his May 2016 paper entitled “Quantitative Style Investing”, Mike Dickson examines strategies that:

1. Aggregate return forecasting power of four or six theoretically-motivated stock factors (or characteristics) via monthly multivariate regressions.
2. Use inception-to-date simple averages of regression coefficients, starting after the first 60 months and updating annually, to suppress estimation and sampling error.
3. Create equally weighted portfolios that are long (short) the 50%, 20%, 10%, 4%, 2% or 1% of stocks with the highest (lowest) expected returns.

The six stock characteristics are: (1) market capitalization; (2), book-to-market ratio; (3) gross profit-to-asset ratio; (4) investment (annual total asset growth); (5) last-month return; and, (6) momentum (return from 12 months ago to two months ago). He considers strategies employing all six characteristics (Model 1) or just the first four, slow-moving ones (Model 2). He considers samples with or without microcaps (capitalizations less than the 20% percentile for NYSE stocks). He estimates trading frictions as 1% of the value traded each month in rebalancing to equal weight. Using monthly data for a broad sample of U.S. common stocks during July 1963 through December 2013 (with evaluated returns commencing July 1968), he finds that: Keep Reading

Do smart beta indexes efficiently exploit factor premiums? In his April 2016 paper entitled “Factor Investing with Smart Beta Indices”, David Blitz investigates how well smart beta indexes, which deviate from the capitalization-weighted market per mechanical rules, capture corresponding factor portfolios. He consider five factors: value, momentum, low-volatility, profitability and investment. He measures their practically exploitable premiums via returns on long-only value-weighted or equal-weighted portfolios of the 30% of large-capitalization U.S. stocks with the most attractive factor values. He tests six smart beta indexes:

1. Russell 1000 Value.
2. MSCI Value Weighted.
3. MSCI Momentum.
4. S&P Low Volatility.
5. MSCI Quality.
6. MSCI High Dividend.

Using monthly data for the five factor portfolios and the six smart beta indexes as available through December 2015, he finds that: Keep Reading

How should investors think about stock factor investing? In his April 2016 paper entitled “The Siren Song of Factor Timing”, Clifford Asness summarizes his current beliefs on exploiting stock factor premiums. He defines factors as ways to select individual stocks based on such firm/stock variables as market capitalization, value (in many flavors), momentum, carry (yield) and quality. He equates factor, smart beta and style investing. He describes factor timing as attempting to predict and exploit variations in factor premiums. Based on past research on U.S. stocks mostly for the past 50 years, he concludes that: Keep Reading

Is there a most practical way to make value and momentum work together across stocks? In the April 2016 version of their paper entitled “Combining Value and Momentum”, Gregg Fisher,  Ronnie Shah and Sheridan Titman examine long-only stock portfolios that seek exposure to both value and momentum while suppressing trading frictions. They define value as high book-to-market ratio based on book value lagged at least four months. They define momentum as return from 12 months ago to one month ago. They consider two strategies for integrating value and momentum:

1. Each month, choose stocks with the highest simple average value and momentum percentile ranks. They suppress turnover with buy-sell ranges, either 90-70 or 95-65. For example, the 90-70 range adds stocks with ranks higher than 90 not already in the portfolio and sells stocks in the portfolio with ranks less than 70. 
2. After initially forming a value portfolio, each month buy stocks only when both value and momentum are favorable, and sell stocks only when both are unfavorable. This strategy weights value more than momentum, because momentum signals change more quickly than value signals. For this strategy, they each month calculate value and momentum scores for each stock as percentages of aggregate market capitalizations of other stocks with lower or equal value and momentum. They suppress turnover with a 90-70 or 95-65 buy-sell range, but the range applies only to the value score. There is a separate 50 threshold for momentum score, meaning that stocks bought (sold) must have momentum score above (below) 50.

They consider large-capitalization stocks (top 1000) and small-capitalization stocks (the rest) separately, with all portfolios value-weighted. They calculate turnover as the total amount bought or sold each month relative to portfolio size. They consider two levels of round-trip trading frictions based on historical bid-ask spreads and broker fees: high levels (based on 1993-1999 data) are 2.94% for small stocks and 1.06% for large stocks; low levels (based on 2000-2013 data) are 0.82% for small stocks and 0.41% large stocks. They focus on net Sharpe ratio as a performance metric. Using monthly data for a broad sample of U.S. common stocks during January 1974 through December 2013, they find that: Keep Reading

Can a typical investor exploit the high returns reported for Piotroski’s FSCORE strategy as applied to U.S. stocks? In their October 2015 paper entitled “The Piotroski F-Score: A Fundamental Value Strategy Revisited from an Investor’s Perspective”, Christopher Krauss, Tom Kruger and Daniel Beerstecher examine whether individual investors can exploit the American Association of Individual Investors’ (AAII) interpretation of this strategy (24% gross annual return over the last decade). They consider equal-weighted and value-weighted long-only (FSCORE 8 and 9) and long-short (short the S&P 500 Index) versions of the strategy, with monthly or weekly rebalancing. They first calculate gross performance and then progressively add realistic obstacles to/costs of trading. They assume average round-trip trading frictions of 0.2% for broker commissions plus 0.5% for bid-ask spreads (but no costs for shorting the S&P 500 Index). Using AAII’s FSCORE screen to generate monthly and weekly portfolios of U.S. stocks via AAII’s Stock Investor Pro platform matched to total stock returns from Datastream during January 2005 through April 2015, they find that: Keep Reading

What kinds of smart beta work best? In their January 2016 paper entitled “A Taxonomy of Beta Based on Investment Outcomes”, Sanne De Boer, Michael LaBella and Sarah Reifsteck compare and contrast smart beta (simple, transparent, rules-based) strategies via backtesting of 12 long-only smart beta stock portfolios. They assign these portfolios to a framework that translates diversification, fundamental weighting and factor investing into core equity exposure and style investing (see the figure below). They constrain backtests to long-only positions, relatively investable/liquid stocks and quarterly rebalancing, treating developed and emerging markets separately. Backtest outputs address gross performance, benchmark tracking accuracy and portfolio turnover. Using beta-related data for developed market stocks during 1979 through 2014 and emerging market stocks during 2001 through 2014, they find that: Keep Reading

Is outperformance of low-volatility stocks just a manifestation of the value premium (outperformance of stocks with high book-to-market ratios compared to stocks with low book-to-market ratios)? In his February 2016 paper entitled “The Value of Low Volatility”, David Blitz examines the interaction of the value premium with returns of long-only portfolios of low-volatility U.S. stocks over various sample periods. His low-volatility portfolios consist of the 30% of stocks with the lowest standard deviations of monthly total returns during the preceding 36 months, reformed monthly. He considers large and small stocks separately, delineated by median NYSE market capitalization, either value-weighted or equal-weighted. Using monthly data for a broad sample of U.S. stocks and the value premium during 1926 through 2014, he finds that: Keep Reading

Do simple ratios such as book-to-market value and earnings-to-market price really identify value stocks? In their January 2016 paper entitled “Facts About Fictional Value Investing”, U-Wen Kok, Jason Ribando and Richard Sloan examine the effectiveness of “value” investing as implemented via sorts on simple fundamental ratios. They investigate interactions of these ratios with firm capitalization and test whether it is the value numerator or the price denominator that drives mean reversion of extreme value ratios. Using data for a broad sample of U.S. stocks with focus on recent decades, they find that: Keep Reading