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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.

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Predicted Factor/Smart Beta Alphas

Which equity factors have high and low expected returns? In their February 2017 paper entitled “Forecasting Factor and Smart Beta Returns (Hint: History Is Worse than Useless)”, Robert Arnott, Noah Beck and Vitali Kalesnik evaluate attractiveness of eight widely used stock factors. They measure alpha for each factor conventionally via a portfolio that is long (short) stocks with factor values having high (low) expected returns, reformed systematically. They compare factor alpha forecasting abilities of six models:

  1. Factor return for the last five years.
  2. Past return over the very long term (multiple decades), a conventionally used assumption.
  3. Simple relative valuation (average valuation of long-side stocks divided by average valuation of short-side stocks), comparing current level to its past average.
  4. Relative valuation with shrunk parameters to moderate forecasts by dampening overfitting to past data.
  5. Relative valuation with shrunk parameters and variance reduction, further moderating Model 4 by halving its outputs.
  6. Relative valuation with look-ahead full-sample calibration to assess limits of predictability. 

They employ simple benchmark forecasts of zero factor alphas. Using 24 years of specified stock data (January 1967 – December 1990) for model calibrations, about 20 years of data (January 1991 – October 2011) to generate forecasts and the balance of data (through December 2016) to complete forecast accuracy measurements, they find that: Keep Reading

Factor/Smart Beta Portfolio Implementation Details

How should factor-based (style) investors proceed after picking a factor to exploit? In their September 2017 paper entitled “Craftsmanship Alpha: An Application to Style Investing”, Ronen Israel, Sarah Jiang and Adrienne Ross survey style portfolio implementation options. They start with a brief discussion of style portfolios types and then focus on portfolio design and implementation. They note trade-offs associated with many options. Based on past research and examples, they conclude that: Keep Reading

Factor/Smart Beta Investing Unsustainably Faddish?

Does transient factor popularity drive factor/smart beta portfolio performance by pushing valuations of associated stocks up and down? In their February 2016 paper entitled “How Can ‘Smart Beta’ Go Horribly Wrong?”, Robert Arnott, Noah Beck, Vitali Kalesnik and John West examine degrees to which factor hedge portfolio and stock factor tilt (smart beta) backtests are attractive due to:

  1. Steady and clearly sustainable factor premiums; or,
  2. Changes in factor relative valuations, measured as average price-to-book value ratio of stocks with high expected returns (factor portfolio long side) divided by average price-to-book ratio of stocks with low expected returns (factor portfolio short side). This ratio tends to increase (decrease) as investor assets move into (out of) factor portfolios.

They consider six long-short factor hedge portfolios: value, momentum, market capitalization (size), illiquidity, low beta and gross profitability. They also consider six smart beta portfolios, which they (mostly) require to sever the relationship between stock price and portfolio weight and to have low turnover, substantial market breadth, liquidity, capacity, transparency, ease of testing and low fees: equal weight, fundamental index, risk efficient, maximum diversification, low volatility and quality. Using specified annual and monthly factor measurement data and returns for a broad sample of U.S. stocks during January 1967 through September 2015, they find that: Keep Reading

Factor Tilts of Broad Stock Indexes

Do broad (capitalization-weighted) stock market indexes exhibit factor tilts that may indicate concentrations in corresponding risks? In their August 2017 paper entitled “What’s in Your Benchmark? A Factor Analysis of Major Market Indexes”, Ananth Madhavan, Aleksander Sobczyk and Andrew Ang examine past and present long-only factor exposures of several popular market capitalization indexes. Their analysis involves (1) estimating the factor characteristics of each stock in a broad index; (2) aggregating the characteristics across all stocks in the index; and (3) matching aggregated characteristics to a mimicking portfolio of five indexes representing value, size, quality, momentum and low volatility styles, adjusted for estimated expense ratios. For broad U.S. stock indexes, the five long-only style indexes are:

  • Value – MSCI USA Enhanced Value Index.
  • Size –  MSCI USA Risk Weighted Index.
  • Quality – MSCI USA Sector Neutral Quality Index.
  • Momentum –  MSCI USA Momentum Index.
  • Low Volatility – MSCI USA Minimum Volatility Index.

For broad international indexes, they use corresponding long-only MSCI World style indexes. Using quarterly stock and index data from the end of March 2002 through the end of March 2017, they find that: Keep Reading

Global Smart Beta Strategy Diversification

Does global diversification improve smart beta (equity factor) investing strategies? In their September 2017 paper entitled “Diversification Strikes Again: Evidence from Global Equity Factors”, Jay Binstock, Engin Kose and Michele Mazzoleni examine effects of global diversification on equity factor hedge portfolios. They consider five factors:

  1. High-Minus-low Value (HML) – book equity divided by market capitalization.
  2. Small-Minus-Big Size (SMB) – market capitalization.
  3. Winners-Minus-Losers Momentum (WML) – cumulative return from 12 months ago to one month ago.
  4. Conservative-Minus-Aggressive Investment (CMA) – change in total assets.
  5. Robust-Minus-Weak Operating Profitability (RMW) – total sales minus cost of goods sold, selling, general, and administrative expenses and interest, divided by total assets.

They reform each factor portfolio annually at the end of June by: (1) resetting market capitalizations, segregating firms into large (top 90%) and small (bottom 10%); (2) separately for large and small firms, constructing high (top 30% of factor values) minus low (bottom 30%) long-short sub-portfolios; and, (3) averaging returns for the two sub-portfolios to generate factor portfolio returns. They lag firm accounting data by at least six months between fiscal year end and portfolio formation date. They define eight global regions: U.S., Japan, Germany, UK, France, Canada, Other Europe and Asia Pacific excluding Japan. When measuring diversification effects, they consider relatedness of country markets and variation over time. Using the specified firm accounting data and monthly stock returns during October 1990 through February 2016, they find that: Keep Reading

One, Three, Five or Seven Stock Return Factors?

How many, and which, factors should investors include when constructing multi-factor smart beta portfolios? In their August 2017 paper entitled “How Many Factors? Does Adding Momentum and Volatility Improve Performance”, Mohammed Elgammal, Fatma Ahmed, David McMillan and Ali Al-Amari examine whether adding momentum and low-volatility factors enhances the Fama-French 5-factor (market, size, book-to-market, profitability, investment) model of stock returns. They consider statistical significance, economic sense and investment import. Specifically, they:

  • Determine whether factor regression coefficient signs and values distinguish between several pairs of high-risk and low-risk style portfolios (assuming stock style portfolio performance differences derive from differences in firm economic risk).
  • Relate time-varying factor betas across style portfolios to variation in economic and market risks as proxied by changes in U.S. industrial production and S&P 500 Index implied volatility (VIX), respectively.
  • Test an out-of-sample trading rule based on extrapolation of factor betas from 5-year historical rolling windows to predict next-month return for five sets (book-to-market, profitability, investment, momentum, quality) of four style portfolios (by double-sorting with size) and picking the portfolio within a set with the highest predicted returns.

Using monthly factor return data during January 1990 through October 2016, they find that: Keep Reading

Stock Quality and Future Returns

Are high-quality stocks worth the price? In the June 2017 update of their paper entitled “Quality Minus Junk”, Clifford Asness, Andrea Frazzini and Lasse Pedersen investigate whether high-quality stocks outperform low-quality stocks. They define high-quality stocks as those that are profitable, growing, safe and well-managed. Specifically, they compute a single quality score for each stock by averaging scores for three components calculated as follows:

  • Profitability – average of rankings for (high) gross profits/assets, return on equity, return on assets, cash flow/assets, gross margin and fraction of earnings that is cash.
  • Growth – average of rankings for (high) prior five-year growth rates for each of the six profitability measures.
  • Safety – average of rankings for (low) market beta, idiosyncratic volatility, leverage, bankruptcy risk and volatility of return on equity.

They consider two modes of analysis: quality-sorted portfolios and quality-minus-junk (QMJ) long-short factor portfolios. Quality-sorted portfolios are by value-weighted tenths (deciles), reformed at the end of each calendar month. QMJ factor portfolio return is the average return on two value-weighted top 30% of quality portfolios (big stocks and small stocks separately) minus the average return on two value-weighted bottom 30% of quality portfolios (big stocks and small stocks separately), reformed monthly by sorting first on size and then on quality. For both modes, global portfolios are value-weighted composites of country portfolios in U.S. dollars. Using characteristics and returns for a broad sample of U.S. stocks since June 1957 and samples of stocks from 24 developed markets (including the U.S.) since June 1989, and contemporaneous U.S. Treasury bill yield as the risk-free rate, all through December 2016, they find that:

Keep Reading

Exploiting Investor Attention to P/E

Do investors fixate on price-to-earnings ratio (P/E) and thereby create trading opportunities as P/Es change? In his June 2017 paper entitled “P/E Ratios and Value Investor Attention”, Jordan Moore examines market responses to U.S. common stocks sorted by earnings yield (inverse of P/E). He defines P/E as the ratio of stock price to the sum of the last available four quarters of net earnings excluding extraordinary items divided by current shares outstanding. For monthly tests, he assumes that earnings become available at the close of the last trading day of the reporting month. For daily and weekly tests, he assumes that earnings become available at the close of the first trading after earnings release date. He separately analyzes stocks with (published) positive and (generally unpublished) negative earnings yields. For comparison, he similarly calculates current monthly book-to-market ratios and sorts stocks by that alternative valuation metric. Using the specified accounting and price data for a broad sample of U.S. common stocks during 1973 through 2015, he finds that: Keep Reading

Carry Trade Across Futures Asset Classes

Does a carry trade derived from roll yields of futures/forward contracts work within asset classes (undiversified) and across asset classes (iversified)? In his May 2017 paper entitled “Optimising Cross-Asset Carry”, Nick Baltas explores the profitability of cross-sectional (relative) and time-series (absolute) carry strategies within and across futures/forward markets for currencies, stock indexes, commodities and government bonds. He posits that contracts in backwardation (contango) present a positive (negative) roll yield and should generally be overweighted (underweighted) in a carry portfolio. He considers three types of carry portfolios, each reformed monthly:

  1. Cross-sectional (XS) or Relative – Rank all assets within a class by strength of carry, demean the rankings such that half are positive and half are negative and then assign weights proportional to demeaned ranks to create a balanced long-short portfolio. Combine asset classes by applying inverse volatility weights (based on 100-day rolling windows of returns) to each class portfolio.
  2. Times-series (TS) or Absolute – Go long (short) each asset within a class that is in backwardation (contango), such that the class may be net long or short. Combine asset classes in the same way as XS.
  3. Optimized (OPT) – Apply both relative strength and sign of carry to determine gross magnitude and direction (long or short) of positions for all assets, and further apply asset volatilities and correlations (based on 100-day rolling windows of returns) to optimize return/risk allocations.

Using daily data for 52 futures series (20 commodities, eight 10-year government bonds, nine currency exchange rates versus the U.S. dollar and 15 country stock indexes) during January 1990 through January 2016, he finds that: Keep Reading

Improving the Magic Formula

What’s the best way to combine profitability and value in screening stocks? In their April 2017 paper entitled “The Magic Formula: Value, Profitability, and the Cross Section of Global Stock Returns”, Douglas Blackburn and Nusret Cakici compare performances of a portfolio based on the Magic Formula (MF) and a portfolio based on an Improved Magic Formula (IMF). Both portfolios are long profitable value (PV) stocks and short unprofitable growth (UG) stocks. For MF, profitability is return-on-capital, defined as earnings before interest and taxes (EBIT) divided by tangible capital. For IMF, profitability is gross profitability, gross profit divided by assets. For both, value is an earnings yield, defined as EBIT divided by enterprise value. Specifically, they each month reform a PV-UG portfolio by:

  1. Ranking stocks by the profitability metric seven months ago (ensuring availability of all inputs).
  2. Ranking stocks by earnings yield seven months ago.
  3. Adding the two rankings and sorting stocks into fifths (quintiles) by combined rank.
  4. Buy the top quintile, sell the bottom quintile (PV-UG) and hold for one month, employing either equal weighting or value weighting.

They test PV-UG portfolios separately in developed equity markets grouped into North America, Europe, Japan and Asia regions. Using the specified firm data and associated monthly stock excess returns (relative to the 1-month U.S. Treasury bill yield as the risk-free rate) in U.S. dollars for 23 developed stock markets during January 1991 through December 2016, they find that: Keep Reading

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