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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.

Mutual Fund Exploitation of Equity Factor Premiums

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 returnsthey find that: Keep Reading

Book-to-Market Volatility as Stock Return Predictor

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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Does Active Stock Factor Timing/Tilting Work?

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:

  1. Factor timing – exploit factor premium time series predictability based on economic indicators and factor-specific technical indicators.
  2. 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

Separate vs. Integrated Equity Factor Portfolios

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

U.S. Equity Turn-of-the-Month as a Diversifying Portfolio

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:

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

They consider six sophisticated asset allocation methods:

  1. Mean-variance optimization.
  2. Optimization with higher moments and Constant Relative Risk Aversion.
  3. Bayes-Stein shrinkage of estimated returns.
  4. Bayesian diffuse-prior.
  5. Black-Litterman.
  6. 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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Most Effective U.S. Stock Market Return Predictors

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

Most Stock Anomalies Fake News?

How does a large sample of stock return anomalies fare in recent replication testing? In their October 2018 paper entitled “Replicating Anomalies”, Kewei Hou, Chen Xue and Lu Zhang attempt to replicate 452 published U.S. stock return anomalies, including 57, 69, 38, 79, 103, and 106 anomalies 57 momentum, 69 value-growth, 38 investment, 79 profitability, 103 intangibles and 106 trading frictions (trading volume, liquidity, market microstructure) anomalies. Compared to the original papers, they use the same sample populations, original (as early as January 1967) and extended (through 2016) sample periods and similar methods/variable definitions. They test limiting influence of microcaps (stocks in the lowest 20% of market capitalizations) by using NYSE (not NYSE-Amex-NASDAQ) size breakpoints and value-weighted returns. They consider an anomaly replication successful if average high-minus-low tenth (decile) return is significant at the 5% level, translating to t-statistic at least 1.96 for pure standalone tests and at least 2.78 assuming multiple testing (accounting for aggregate data snooping bias). Using required anomaly data and monthly returns for U.S. non-financial stocks during January 1967 through December 2016, they find that:

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SACEVS Input Risk Premiums and EFFR

The “Simple Asset Class ETF Value Strategy” (SACEVS) seeks diversification across a small set of asset class exchanged-traded funds (ETF), plus a monthly tactical edge from potential undervaluation of three risk premiums:

  1. Term – monthly difference between the 10-year Constant Maturity U.S. Treasury note (T-note) yield and the 3-month Constant Maturity U.S. Treasury bill (T-bill) yield.
  2. Credit – monthly difference between the Moody’s Seasoned Baa Corporate Bonds yield and the T-note yield.
  3. Equity – monthly difference between S&P 500 operating earnings yield and the T-note yield.

Premium valuations are relative to historical averages. How might this strategy react to increases in the Effective Federal Funds Rate (EFFR)? Using end-of-month values of the three risk premiums, EFFRtotal 12-month U.S. inflation and core 12-month U.S. inflation during March 1989 (limited by availability of operating earnings data) through September 2018, we find that: Keep Reading

Are Equity Multifactor ETFs Working?

Are equity multifactor strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider seven ETFs, all currently available (in order of decreasing assets):

  • Goldman Sachs ActiveBeta U.S. Large Cap Equity (GSLC) – holds large U.S. stocks based on good value, strong momentum, high quality and low volatility.
  • iShares Edge MSCI Multifactor USA (LRGF) – holds large and mid-cap U.S. stocks with focus on quality, value, size and momentum, while maintaining a level of risk similar to that of the market.
  • iShares Edge MSCI Multifactor International (INTF) – holds global developed market ex U.S. large and mid-cap stocks based on quality, value, size and momentum, while maintaining a level of risk similar to that of the market.
  • JPMorgan Diversified Return U.S. Equity (JPUS) – holds U.S. stocks based on value, quality and momentum via a risk-weighting process that lowers exposure to historically volatile sectors and stocks.
  • John Hancock Multifactor Large Cap (JHML) – holds large U.S. stocks based on smaller capitalization, lower relative price and higher profitability, which academic research links to higher expected returns.
  • John Hancock Multifactor Mid Cap (JHMM) – holds mid-cap U.S. stocks based on smaller capitalization, lower relative price and higher profitability, which academic research links to higher expected returns.
  • Xtrackers Russell 1000 Comprehensive Factor (DEUS) – seeks to track, before fees and expenses, the Russell 1000 Comprehensive Factor Index, which seeks exposure to quality, value, momentum, low volatility and size factors.

Because available sample periods are very short, we focus on daily return statistics, along with cumulative returns. We use four benchmarks according to fund descriptions: SPDR S&P 500 (SPY), iShares MSCI ACWI ex US (ACWX), SPDR S&P MidCap 400 (MDY) and iShares Russell 1000 (IWB). Using daily returns for the seven equity multifactor ETFs and benchmarks as available through September 2018, we find that: Keep Reading

Evolution of Quantitative Stock Investing

Quantitative investing involves disciplined rule-based approaches to help investors structure optimal portfolios that balance return and risk. How has such investing evolved? In their June 2018 paper entitled “The Current State of Quantitative Equity Investing”, Ying Becker and Marc Reinganum summarize key developments in the history of quantitative equity investing. Based on the body of research, they conclude that: Keep Reading

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