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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SACEVS Input Risk Premiums and FFR

The “Simple Asset Class ETF Value Strategy” 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 Federal Funds Rate (FFR)? Using end-of-month values of the three risk premiums, FFR, total 12-month U.S. inflation and core 12-month U.S. inflation during January 1990 (limited by availability of specific FFR targets) through June 2015 (306 months), we find that: Keep Reading

Equity Factor Investing Update

Has (hypothetical) equity factor investing worked as well in recent years as indicated in past studies? In his July 2015 paper entitled “Factor Investing Revisited”, David Blitz updates his prior study quantifying the performance of allocations to U.S. stocks based on three factor premiums: (1) value (high book-to-market ratio); (2) momentum (high return from 12 months ago to one month ago); and, (3) low-volatility (low standard deviation of total returns over the last 36 months). He considers two additional factor allocations: (4) operating profitability (high return on equity); and, (5) investment (low asset growth). He specifies each factor portfolio as the 30% of U.S. stocks with market capitalizations above the NYSE median that have the highest expected returns, reformed monthly for momentum and low-volatility and annually for the other factors. He considers both equal-weighted and value-weighted portfolios for each factor. He also summarizes recent research on the role of small-capitalization stocks, factor timing, long-only versus long-short portfolios, applicability to international stocks and applicability to other asset classes. Using value, momentum, profitability and investment factor portfolio returns from Kenneth French’s library and low-volatility portfolio returns as constructed from a broad sample of U.S. stocks during July 1963 through December 2014, he finds that: Keep Reading

Country Stock Market Factor Strategies

Do factors that predict returns in U.S. stock data also work on global stock markets at the country level? In the May 2015 version of their paper entitled “Do Quantitative Country Selection Strategies Really Work?”, Adam Zaremba and Przemysław Konieczka test 16 country stock market selection strategies based on relative market value, size, momentum, quality and volatility. For each of 16 factors across these categories, they sort country stock markets into fifths (quintiles) and measure the factor premium as return on the highest minus lowest quintiles. They consider equal, capitalization and liquidity (average turnover) weighting schemes within quintiles. They look at complementary large and small market subsamples, and complementary open (easy to invest) and closed market subsamples. Using monthly total returns adjusted for local dividend tax rates in U.S. dollars for 78 existing and discontinued country stock indexes (primarily MSCI) during 1999 through 2014, they find that: Keep Reading

Enhanced Value Strategies for U.S. Stocks

What is the best way to implement a value strategy for U.S. stocks? In their May 2015 paper entitled “Optimizing Value”, Ran Leshem, Lisa Goldberg and Alan Cummings investigate how the choice of value metric and implementation approach affect value strategy performance. They first compare book value-to-price ratio (B/P) and earnings-to-price ratio (E/P) based on returns for portfolios of the top 30% of stocks based on each metric, reformed frictionlessly each month since 1951. They then compare practical implementations that reform portfolios of S&P 500 stocks quarterly since 1973 (with round-trip trading friction 0.12%) by: (1) selecting the top 30% stocks based on the value metrics; or, (2) tilting S&P 500 Index weights based on the metrics. Finally, they add constraints to avoid value portfolio sector concentrations. Using Ken French’s value factor data since 1951 and data for S&P 500 stocks since 1973, both through December 2013, they find that: Keep Reading

Summarizing Value (and Momentum) Investing

When does value investing work and how does it work best? In the April 2015 initial draft of their paper entitled “Fact, Fiction, and Value Investing”, Clifford Asness, Andrea Frazzini, Ronen Israel and Tobias Moskowitz address areas of confusion about value investing. They describe value as the tendency of cheap securities to outperform expensive ones based on some valuation method. They broadly specify the value premium as the return achieved by holding or overweighting cheap securities and shorting or underweighting expensive ones. They focus on systematic (mechanical), diversified value strategies based on quantified metrics such as book-to-market ratio or earnings-price ratio. Their context is firm belief that such strategies are great investments. Based on academic studies and simple tests with recent data, largely from Kenneth French’s data library, they conclude that: Keep Reading

Carry and Trend Implications for Future Returns Across Asset Classes

Are positive carry and positive trend conditions consistently favorable across asset classes? In their March 2015 paper entitled “Carry and Trend in Lots of Places”, Vineer Bhansali, Josh Davis, Matt Dorsten and Graham Rennison employ futures prices to investigate whether the adages “don’t pay too much to hold an investment” and “don’t fight the trend” actually work across four major asset classes: equities, bonds, commodities and currencies. For testing, they select five liquid markets with relatively long futures histories within each asset class. They define carry as annualized excess return assuming that spot prices do not change. They define trend as positive (negative) if the futures price today is above (below) its one-year trailing moving average. They specify four states for each market:

  1. Positive carry and positive trend (Carry + / Trend +).
  2. Positive carry and negative trend (Carry + / Trend -).
  3. Negative carry and positive trend (Carry – / Trend +).
  4. Negative carry and negative trend (Carry – / Trend -).

They then calculate average subsequent daily excess returns for each market by state and annualize results. Using daily futures data as available and some simulated futures data (from spot prices) for 20 major markets across four asset classes during 1960 through 2014, they find that: Keep Reading

Measuring the Value Premium with Style ETFs

Do popular style-based exchange-traded funds (ETF) confirm the existence of a reliably exploitable value premium? To investigate, we compare the difference in returns (value minus growth) 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)

Using monthly dividend-adjusted closing prices for these ETFs during August 2001 (the earliest month available for IWP-IWS) through January 2015 (162 months), we find that: Keep Reading

Interaction of Calendar Effects with Other Anomalies

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

Investor Return versus Mutual Fund Performance

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

Adding Profitability and Investment to the Three-factor Model

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

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