Value Premium

Do stock return anomalies exhibit January and month-of-quarter (first, second or third, excluding January) effects? In his February 2015 paper entitled “Seasonalities in Anomalies”, Vincent Bogousslavsky investigates whether the following 11 widely cited U.S. stock return anomalies exhibit these effects:

1. Market capitalization (size) – market capitalization last month.
2. Book-to-market – book equity (excluding stocks with negative values) divided by market capitalization last December.
3. Gross profitability – revenue minus cost of goods sold divided by total assets.
4. Asset growth – Annual change in total assets.
5. Accruals – change in working capital minus depreciation, divided by average total assets the last two years.
6. Net stock issuance – growth rate of split-adjusted shares outstanding at fiscal year end.
7. Change in turnover – difference between turnover last month and average turnover the prior six months.
8. Illiquidity – average illiquidity the previous year.
9. Idiosyncratic volatility – standard deviation of residuals from regression of daily excess returns on market, size and book-to-market factors.
10. Momentum – past six-month return, skipping the last month.
11. 12-month effect – average return in month t−k*12, for k = 6, 7, 8, 9, 10.

Each month, he sorts stocks into tenths (deciles) based on each anomaly variable and forms portfolios that are long (short) the decile with the highest (lowest) values of the variable. He updates all accounting inputs annually at the end of June based on data for the previous fiscal year. Using accounting data and monthly returns for a broad sample of U.S. common stocks during January 1964 to December 2013, he finds that: Keep Reading

Does the average mutual fund investor accrue the average fund performance, or do investor timing practices alter the equation? In their July 2014 paper entitled “Timing Poorly: A Guide to Generating Poor Returns While Investing in Successful Strategies“, Jason Hsu, Brett Myers and Ryan Whitby compare the average dollar-weighted and buy-and-hold returns of different U.S. equity mutual fund styles, with focus on the value style. Dollar weighting adjusts the return stream based on the timing and magnitude of fund flows and is a more accurate measure than buy-and-hold of the returns realized by fund investors who may trade in and out of funds. Using monthly returns, monthly total assets and quarterly fund style information for a broad sample of U.S. equity mutual funds during 1991 through 2013, they find that: Keep Reading

Does adding profitability and asset growth (investment) factors improve the performance of the widely used Fama-French three-factor (market, size, book-to-market) model of stock returns? In the September 2014 version of their paper entitled “A Five-Factor Asset Pricing Model” Eugene Fama and Kenneth French assess whether extensions of their three-factor model to include profitability and investment improves model predictive power. They measure profitability as prior-year revenue minus cost of goods sold, interest expense and selling, general and administrative expenses divided by book equity. They define investment as prior-year growth in total assets divided by total assets. Using returns and stock/firm characteristics for a broad sample of U.S. stocks during July 1963 through December 2013 (606 months), they find that: Keep Reading

Do predictive powers of the size, value and momentum factors observed for individual stocks translate to the country level? In the November 2014 version of his paper entitled “Country Selection Strategies Based on Value, Size and Momentum”, Adam Zaremba investigates country-level value, size and momentum premiums, and tests whether the value and momentum premiums are equally strong across markets of different sizes and evaluates a country-level multi-factor asset pricing model. He measures factors at the country level as:

• Value: aggregate book-to-market ratio, with aggregate 12-month earnings-to-price-ratio, cash flow-to-price ratio and dividend yield as alternatives where available.
• Size: total market capitalization of country stocks.
• Momentum: cumulative return over preceding 12, 9, 6 or 3 months excluding the last month to avoid short-term reversal.

He relies on capitalization-weighted, U.S. dollar-denominated gross total return MSCI equity indexes as available, with Dow Jones and STOXX indexes as fallbacks (an average 56 indexes per month over time). He includes discontinued country indexes. He uses one-month LIBOR as the risk-free rate. Each month, he ranks countries by value, size and momentum into value-weighted or equal-weighted fifths (quintiles). He also performs double-sorts first on size and then on value or momentum. Using monthly firm/stock data for listed stockswithin 78 country indexes as available during February 1999 through September 2014 (147 months), he finds that: Keep Reading

Are gains from tax-loss harvesting, the systematic taking of capital losses to offset capital gains, additive to or subtractive from premiums from portfolio tilts toward common factors such as value, size, momentum and volatility (smart beta)? In their October 2014 paper entitled “Factor Tilts after Tax”, Lisa Goldberg and Ran Leshem look at the effects on portfolio performance of combining factor tilts and tax-loss harvesting. They call the incremental return from tax-loss harvesting tax alpha, which (while investor-specific) is typically in the range 1%-2% per year for wealthy investors holding broad capitalization-weighted portfolios. They test six long-only factor tilts based on Barra equity factor models: (1) value (high earnings yield and book-to-market ratio); (2) momentum (high recent past return); (3) value/momentum; (4) small/value; (5) quality (value stocks with low earnings variability, leverage and volatility); and, (6) minimum volatility/value (low volatility with diversification constraint and value tilt). Their overall benchmark is the MSCI All Country World Index (ACWI). Their tax alpha benchmark derives from a strategy that harvests losses in a capitalization-weighted portfolio (no factor tilts) without deviating far from the overall benchmark. The rebalancing interval is monthly for all portfolios. Using monthly returns for stocks in the benchmark index during January 1999 through December 2013, they find that: Keep Reading

Does compounding rules tend to improve the performance of stock-picking strategies? In the October 2014 draft of their paper entitled “Does Complexity Imply Value, AAII Value Strategies from 1963 to 2013”, Wesley Gray, Jack Vogel and Yang Xu compare 13 stock strategies labeled as “Value” by the American Association for Individual Investors (AAII) to each other and to a simple “low-price” value strategy. The simple strategy each year selects the tenth of stocks with the highest Earnings Before Interest, Taxes, Depreciation and Amortization-to-Total Enterprise Value ratios (EBITDA/TEV), excluding financial firms. To ensure liquidity, they focus on stocks with market capitalizations above the NYSE 40th percentile. To ensure real-time availability of inputs, they lag firm accounting data. To assess performance consistency, they consider three subperiods: July 1963 through December 1980; January 1981 through December 1996; and, January 1997 through December 2013. Because portfolios are equally weighted, they include the S&P 500 equal-weight total return index (S&P 500 EW) as a benchmark. Using stock price and firm accounting data for a broad sample of U.S. common stocks during July 1963 through December 2013, they find that: Keep Reading

What is the best way to capture the slowly realized and variable value premium? In his August 2014 paper entitled “Value Investing: Smart Beta vs. Style Indices”, Jason Hsu compares exploitation of the value premium by traditional style indexes and recent smart beta strategies. Traditional value indexes pick stocks with low price‐to‐book ratios (P/B) and weight them by market capitalization. Smart beta strategies generally ignore stock prices and weight stocks by fundamental metrics such as book values or total cash flows. Using P/B data and returns for broad market indexes, style indexes and smart beta strategies for periods of up to 30 years through the end of 2013, he finds that: Keep Reading

Is risk for long-term investors different from that for short-term investors? In his July 2014 paper entitled “Rethinking Risk (II): The Size and Value Effects”, Javier Estrada examines the riskiness of small stocks versus large stocks and value (high book-to-market ratio) stocks versus growth stocks based on conventional and unconventional metrics. Each year during 1927 through 2013, he makes initial investments of $100 in Fama-French small, large, value and growth stock portfolios and holds for 20 or 30 years to generate distributions of 68 or 58 terminal wealths for each style, respectively. He then calculates the following metrics for these two sets of portfolios:

• Mean (average) of terminal wealths by style.
• Median (midpoint) of terminal wealths by style.
• Average of the standard deviations of annual returns (SDD) by style.
• Standard deviation of the terminal wealths (SDE) by style.
• Lower‐tail Terminal Wealth (LTWx), the average terminal wealth in the lower x% of the distribution of terminal wealths (with x% being 1%, 5% or 10%) by style.
• Upper‐tail Terminal Wealth (UTWx), the average terminal wealth in the upper x% of the distribution of terminal wealths (with x% again being 1%, 5% or 10%) by style.

SDD is most like conventional risk (volatility), while the other metrics focus unconventionally on terminal wealth. Using annual gross total returns for Fama‐French U.S. style portfolios during 1927 through 2013, he finds that: Keep Reading

Do the behaviors of the most widely accepted stock market factors (size, book-to-market or value, and momentum) vary with the economic trend? In the June 2014 version of their paper entitled “Macroeconomic Determinants of Cyclical Variations in Value, Size and Momentum premium in the UK”, Golam Sarwar, Cesario Mateus and Natasa Todorovic examine differences in the sensitivities of UK equity market size, value and momentum factor returns (premiums) to changes in broad and specific economic variables. They define the broad economic state each month as upturn (downturn) when the OECD Composite Leading Indicator for the UK increases (decreases) that month. They also consider contributions of six specific variables to economic trend: GDP growth; unexpected inflation (change in CPI); interest rate (3-month UK Treasury bill yield); term spread (10-year UK Treasury bond yield minus 3-month UK Treasury bill yield); credit spread (Moody’s U.S. BBA yield minus 10-year UK government bond yield); and, money supply growth. They lag economic variables by one or two months to align their releases with stock market premium measurements. Using monthly UK size, value and momentum factors and economic data during July 1982 through December 2012, they find that: Keep Reading

Can investors exploit monthly persistence in the value premium for U.S. stocks? In his February 2014 paper entitled “Exploiting Factor Autocorrelation to Improve Risk Adjusted Returns”, Kevin Oversby investigates whether investors can exploit the fact that the Fama-French model high-minus-low (HML) value factor exhibits positive monthly autocorrelation (persistence). The HML factor derives from the difference in performance between portfolios of stocks with high and low book-to-market ratios. Prior published research indicates that the value premium concentrates in small firms, so he focuses on stocks with market capitalizations below the NYSE median. His test strategies each month invest in capitalization-weighted small value (small growth or small momentum) Fama-French portfolios when the prior-month sign of the HML factor is positive (negative). The strategies additionally retreat to a risk-free asset (such as U.S. Treasury bills) if the prior-month return for the test strategy is negative. Using HML factor values and monthly portfolio returns for small value, small growth and small momentum Fama-French portfolios, he finds that: Keep Reading