# Value Premium

Is there a reliable benefit from conventional value investing (based on the book-to-market value ratio)? these blog entries relate to the value premium.

**March 21, 2019** - Value Premium

Do popular style-based exchange-traded funds (ETF) offer a reliable way to exploit the value premium? To investigate, we compare differences in returns (value-minus-growth, or V – G) for each of the following three matched pairs of value-growth ETFs:

- iShares Russell 2000 (Smallcap) Growth Index (IWO)
- iShares Russell 2000 (Smallcap) Value Index (IWN)
- iShares Russell Midcap Growth Index (IWP)
- iShares Russell Midcap Value Index (IWS)
- iShares Russell 1000 (Largecap) Growth Index (IWF)
- iShares Russell 1000 (Largecap) Value Index (IWD)

To aggregate, we define monthly value return as the equally weighted average monthly return of IWN, IWS and IWD and monthly growth return as the equally weighted average monthly return of IWO, IWP and IWF. Using monthly dividend-adjusted closing prices for these ETFs during August 2001 (limited by IWP and IWS) through February 2019, *we find that:* Keep Reading

**March 11, 2019** - Big Ideas, Equity Premium, Momentum Investing, Size Effect, Strategic Allocation, Value Premium, Volatility Effects

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:

- Returns are far short of expectations due to overfitting and/or trade crowding.
- Drawdowns far exceed expectations.
- 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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**March 7, 2019** - Buybacks-Secondaries, Momentum Investing, Technical Trading, Value Premium, Volatility Effects

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

**February 21, 2019** - Calendar Effects, Equity Premium, Momentum Investing, Value Premium, Volatility Effects

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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**February 12, 2019** - Momentum Investing, Size Effect, Value Premium

How well do mutual funds exploit theoretical (academic) equity factor premiums, and how well do investors exploit such exploitation? In their January 2019 paper entitled “Factor Investing from Concept to Implementation”, Eduard Van Gelderen, Joop Huij and Georgi Kyosev examine: (1) how performances of mutual funds that target equity factor premiums (low beta, size, value, momentum, profitability, investment) compare to that of funds that do not; and, (2) flow-adjusted performances, indicating how much of any outperformance accrues to fund investors. They classify funds empirically based on factor exposures. Using monthly returns and total assets and quarterly turnover and expense ratios for 3,109 actively managed long-only U.S. equity mutual funds with assets over $5 million (1,334 dead and 1,775 live) since January 1990 and for 4,859 (2,000 dead and 2,859 live) similarly specified global mutual funds since January 1991, all through December 2015, along with contemporaneous monthly equity factor returns, *they find that:* Keep Reading

**January 29, 2019** - Value Premium, Volatility Effects

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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**January 8, 2019** - Fundamental Valuation, Momentum Investing, Size Effect, Strategic Allocation, Value Premium

Does active stock factor exposure management boost overall portfolio performance? In their November 2018 paper entitled “Optimal Timing and Tilting of Equity Factors”, Hubert Dichtl, Wolfgang Drobetz, Harald Lohre, Carsten Rother and Patrick Vosskamp explore benefits for global stock portfolios of two types of active factor allocation:

- Factor timing – exploit factor premium time series predictability based on economic indicators and factor-specific technical indicators.
- Factor tilting – exploit cross-sectional (relative) attractiveness of factor premiums.

They consider 20 factors spanning value, momentum, quality and size. For each factor each month, they reform a hedge portfolio that is long (short) the equal-weighted fifth, or quintile, of stocks with the highest (lowest) expected returns for that factor. For implementation of factor timing, they consider: 14 economic indicators standardized by subtracting respective past averages and dividing by standard deviations; and, 16 technical indicators related to time series momentum, moving averages and volatilities. They suppress redundancy and noise in these indicators via principal component analysis separately for economic and technical groups, focusing on the first principal component of each group. They translate any predictive power embedded in principal components into optimal factor portfolio weights using augmented mean-variance optimization. For implementation of factor tilting, they overweight (underweight) factors that are relatively attractive (unattractive) based on valuations of factor top and bottom quintile stocks, top-bottom quintile factor variable spreads, prior-month factor returns (momentum) and volatilities of past monthly factor returns. Their benchmark portfolio is the equal-weighted combination of all factor hedge portfolios. For all portfolios, they assume: monthly portfolio reformation costs of 0.75% (1.15%) of turnover value for the long (short) side; and, annual 0.96% cost for an equity swap to ensure a balanced portfolio of factor portfolios. For monthly factor timing and tilting portfolios only, they assume an additional cost of 0.20% of associated turnover. Using monthly data for a broad sample of global stocks from major equity indexes and for specified economic indicators during January 1997 through December 2016 (4,500 stocks at the beginning and 5,000 stocks at the end), *they find that:* Keep Reading

**November 27, 2018** - Fundamental Valuation, Momentum Investing, Value Premium, Volatility Effects

What is the best way to construct equity multifactor portfolios? In the November 2018 revision of their paper entitled “Equity Multi-Factor Approaches: Sum of Factors vs. Multi-Factor Ranking”, Farouk Jivraj, David Haefliger, Zein Khan and Benedict Redmond compare two approaches for forming long-only equity multifactor portfolios. They first specify ranking rules for four equity factors: value, momentum, low volatility and quality. They then, each month:

- Sum of factor portfolios (SoF): For each factor, rank all stocks and form a factor portfolio of the equally weighted top 50 stocks (adjusted to prevent more than 20% exposure to any sector). Then form a multifactor portfolio by equally weighting the four factor portfolios.
- Multifactor ranking (MFR): Rank all stocks by each factor, average the ranks for each stock and form an equally weighted portfolio of those stocks with the highest average ranks, equal in number of stocks to the SoF portfolio (again adjusted to prevent more than 20% exposure to any sector).

They consider variations in number of stocks selected for individual factor portfolios from 25 to 200, with comparable adjustments to the MFR portfolio. They assume trading frictions of 0.05% of turnover. Using monthly data required to rank the specified factors for a broad sample of U.S. common stocks and monthly returns for those stocks and the S&P 500 Total Return Index (S&P 500 TR) during January 2003 through July 2016, *they find that:* Keep Reading

**November 26, 2018** - Bonds, Calendar Effects, Equity Premium, Momentum Investing, Size Effect, Strategic Allocation, Value Premium

Is the U.S. equity turn-of-the-month (TOTM) effect exploitable as a diversifier of other assets? In their October 2018 paper entitled “A Seasonality Factor in Asset Allocation”, Frank McGroarty, Emmanouil Platanakis, Athanasios Sakkas and Andrew Urquhart test U.S. asset allocation strategies that include a TOTM portfolio as an asset. The TOTM portfolio buys each stock at the open on the last trading day of each month and sells at the close on the third trading day of the following month, earning zero return the rest of the time. They consider four asset universes with and without the TOTM portfolio:

- A conventional stocks-bonds mix.
- The equity market portfolio.
- The equity market portfolio, a small size portfolio and a value portfolio.
- The equity market portfolio, a small size portfolio, a value portfolio and a momentum winners portfolio.

They consider six sophisticated asset allocation methods:

- Mean-variance optimization.
- Optimization with higher moments and Constant Relative Risk Aversion.
- Bayes-Stein shrinkage of estimated returns.
- Bayesian diffuse-prior.
- Black-Litterman.
- A combination of allocation methods.

They consider three risk aversion settings and either a 60-month or a 120-month lookback interval for input parameter measurement. To assess exploitability, they set trading frictions at 0.50% of traded value for equities and 0.17% for bonds. Using monthly data as specified above during July 1961 through December 2015, *they find that:*

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**November 20, 2018** - Economic Indicators, Fundamental Valuation, Momentum Investing, Size Effect, Technical Trading, Value Premium

Which economic and market variables are most effective in predicting U.S. stock market returns? In his October 2018 paper entitled “Forecasting US Stock Returns”, David McMillan tests 10-year rolling and recursive (inception-to-date) one-quarter-ahead forecasts of S&P 500 Index capital gains and total returns using 18 economic and market variables, as follows: dividend-price ratio; price-earnings ratio; cyclically adjusted price-earnings ratio; payout ratio; Fed model; size premium; value premium; momentum premium; quarterly change in GDP, consumption, investment and CPI; 10-year Treasury note yield minus 3-month Treasury bill yield (term structure); Tobin’s q-ratio; purchasing managers index (PMI); equity allocation; federal government consumption and investment; and, a short moving average. He tests individual variables, four multivariate combinations and and six equal-weighted combinations of individual variable forecasts. He employs both conventional linear statistics and non-linear economic measures of accuracy based on sign and magnitude of forecast errors. He uses the historical mean return as a forecast benchmark. Using quarterly S&P 500 Index returns and data for the above-listed variables during January 1960 through February 2017, *he finds that:* Keep Reading