Value Premium

How can investors integrate global asset class diversification, pre-eminent factor premiums and crash protection? In his July 2016 paper entitled “The Trinity Portfolio: A Long-Term Investing Framework Engineered for Simplicity, Safety, and Outperformance”, Mebane Faber summarizes a portfolio combining these three principles, as follows:

1. Global diversification: Include U.S. stocks, non-U.S. developed markets stocks, emerging markets stocks, corporate bonds, 30-year U.S. Treasury bonds, 10-year foreign government bonds, U.S. Treasury Inflation-Protected Securities (TIPS), commodities, gold and Real Estate Investment Trusts (REIT) .
2. Value/momentum screens: For U.S. stocks, each month first rank stocks by value and momentum metrics and then pick those with the highest average ranks. For non-U.S. stocks, each month pick the cheapest overall markets. For bonds, each month pick those with the highest yields.
3. Trend following for crash avoidance: For each asset each month, hold the asset (cash) if its price is above (below) its 10-month SMA at the end of the prior month.

The featured “Trinity” portfolio allocates 50% to a sub-portfolio based on principles 1 and 2 and 50% to a sub-portfolio based on principles 1, 2 and 3. Using monthly returns for the specified asset classes during 1973 through 2015, he finds that: Keep Reading

Is there some stock value metric that is markedly superior to the conventional book-to-market ratio (BM) for identifying undervalued and overvalued stocks? In his July 2016 paper entitled “Value Investing with Dividend-to-Market Ratio”, Yiqing Dai tests the effectiveness of maximum payable dividend ratio (DM) ) as an alternative to book-to-market ratio for value investing. He specifies DM as (profitability – investment)/market value, the difference between earning power of firm assets and reinvestment required to generate future earnings. He specifies profitability as prior-year revenue minus prior-year cost of goods sold, selling, general and administrative expenses, research and development expenditures and interest expense. He specifies investment as prior-year book equity times change in total assets from two years ago to prior-year, divided by change in assets. Using the specified accounting data and monthly returns for a broad sample of non-financial U.S. stocks during July 1963 through December 2013, he finds that: Keep Reading

What is the best way to combine styles (smart betas) in one portfolio? In their June 2016 paper entitled “Long-Only Style Investing: Don’t Just Mix, Integrate”, Shaun Fitzgibbons, Jacques Friedman, Lukasz Pomorski and Laura Serban compare two approaches to long-only combined equity style investing:

1. Mixed portfolio – simply picks stocks from single-style portfolios.
2. Integrated portfolio – first combines single-style rankings into an overall score for each stock and then builds a portfolio based on top overall scores.

They focus on combining momentum stocks (highest return from 12 months ago to one month ago) and value stocks (high book-to-market ratio). They first employ simulated data to illustrate differences in stock selection between the two approaches. They then compare net performances for equally weighted, monthly rebalanced mixed and integrated combinations of liquid global stocks. Using monthly data for large-capitalization stocks from developed markets (roughly the MSCI World Index components) during February 1993 through December 2015, they find that: Keep Reading

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