Equity Premium

How big is the stock liquidity premium and does it subsume other variables widely used to estimate future returns? In their December 2014 paper entitled “A Comparative Analysis of Liquidity Measures”, Yuping Huang and Vasilios Sogiakas investigate the relationships of excess (relative to the risk-free rate) stock returns to three pairs of monthly liquidity metrics:

• Transaction cost: (1) average daily absolute bid-ask spread; or, (2) relative spread (average daily absolute spread divided by stock price).
• Trading activity: (3) turnover ratio (shares traded divided by shares outstanding); or, (4) average daily dollar volume.
• Price impact: (5) average absolute daily return divided by dollar volume; or, (6) average daily ratio of absolute return divided by daily turnover ratio.

They also examine the interaction of these liquidity metrics with widely used risk factors (market capitalization or size, book-to-market ratio and momentum) and other predictive variables (price, earnings yield and dividend yield). They base some analyses on average gross returns of equally weighted portfolios reformed monthly by ranking stocks into fifths (quintiles) by prior-month liquidity metrics. Analyses exploring interaction of liquidity metrics with other factors/variables employ multivariate regressions. In grooming/processing data, they exclude stocks with extremely low and high prices, liquidity metrics, factors and predictive variables. Using daily bid-ask spreads during 1991 through 2011 and monthly values of other trading metrics and factors/variables as described above during 1962 through 2011 for a broad (but filtered) sample of U.S. stocks (an average of 2,050 stocks each month), they find that: Keep Reading

How do the major components of U.S. stock market performance behave over time? In his October 2014 paper entitled “Long-Term Sources of Investment Returns and a Simple Way to Enhance Equity Returns”, Baijnath Ramraika decomposes long-term returns from the U.S. stock market (as proxied by Robert Shiller’s S&P Composite Index) into four components:

1. Dividend yield
2. Inflation
3. Real average change in 10-year earnings (E10)
4. Change in the Cyclically Adjusted Price-Earnings ratio (CAPE, or P/E10)

He further segments this decomposition by decade. Using his decomposition by decade for 1881 through 2010 (13 decades), we find that: Keep Reading

Is equity factor investing a straightforward path to premium capture and diversification? In their October 2014 paper entitled “Facts and Fantasies About Factor Investing”, Zelia Cazalet and Thierry Roncalli summarize the body of research on factor investing and provide examples to address the following questions:

1. What is a risk factor?
2. Do all risk factors offer attractive premiums?
3. How stable and robust are these premiums?
4. How can investors translate academic risk factors into portfolios?
5. How should investors allocate to different factors?

They define risk factor investing as the attempt to enhance returns in the long run by capturing systematic risk premiums. They focus on the gap between retrospective (academic) analysis and prospective portfolio implementation. They summarize research on the following factors: market beta, size, book-to-market ratio, momentum, volatility, liquidity, carry, quality, yield curve slope, default risk, coskewness and macroeconomic variables. Based on the body of factor investing research and examples, they conclude that: Keep Reading

Are the widely used Fama-French three-factor model (market, size, book-to-market ratio) and the Carhart four-factor model (adding momentum) the best factor models of stock returns? In their September 2014 paper entitled “Digesting Anomalies: An Investment Approach”, Kewei Hou, Chen Xue and Lu Zhang construct the q-factor model comprised of market, size, investment and profitability factors and test its ability to predict stock returns. They also test its ability to account for 80 stock return anomalies (16 momentum-related, 12 value-related, 14 investment-related, 14 profitability-related, 11 related to intangibles and 13 related to trading frictions). Specifically, the q-factor model describes the excess return (relative to the risk-free rate) of a stock via its dependence on:

1. The market excess return.
2. The difference in returns between small and big stocks.
3. The difference in returns between stocks with low and high investment-to-assets ratios (change in total assets divided by lagged total assets).
4. The difference in returns between high-return on equity (ROE) stocks and low-ROE stocks.

They estimate the q-factors from a triple 2-by-3-by-3 sort on size, investment-to-assets and ROE. They compare the predictive power of this model with the those of the Fama-French and Carhart models. Using returns, market capitalizations and firm accounting data for a broad sample of U.S. stocks during January 1972 through December 2012, they find that: Keep Reading

Does the Capital Asset Pricing Model (CAPM) make predictions useful to investors? In his October 2014 paper entitled “CAPM: an Absurd Model”, Pablo Fernandez argues that the assumptions and predictions of CAPM have no basis in the real world. A key implication of CAPM for investors is that an asset’s expected return relates positively to its expected beta (regression coefficient relative to the expected market risk premium). Based on a survey of related research, he concludes that: Keep Reading

Can the U.S. stock market continue to deliver its historical return? In the preliminary draft of his paper entitled “A Pragmatist’s Guide to Long-run Equity Returns, Market Valuation, and the CAPE”, John Golob poses two questions:

1. What long-run real return should investors expect from U.S. equities?
2. Do popular metrics reliably indicate when the U.S. equity market is overvalued?

He notes that the body of relevant research presents no consensus on the answers to these questions, which both relate to long-term growth in corporate earnings per share. Recent forecasts for real stock market returns range from as low as 2% to about 6% (close to the 6.5% average since 1871), reflecting disagreements about how slow GDP growth, low dividends, share buybacks and the profitability of retained earnings affect earnings per share growth. The author introduces Federal Reserve Flow of Funds (U.S. Financial Accounts) and S&P 500 aggregate book value to gauge effects of stock buybacks. He also assesses the logic of using Shiller’s cyclically adjusted price-earnings ratio (CAPE or P/E10) as a stock market valuation metric. Using S&P 500 Index price and dividend data, related earnings data and U.S. financial and economic data as available during 1871 through 2013, he concludes that: Keep Reading

Is the capitalization-weighted market portfolio a lame benchmark? In his August 2014 paper entitled “It’s Easy to Beat the Market”, Moshe Levy tests the perception that it is hard to beat a capitalization-weighted portfolio and therefore that an index so weighted is a challenging benchmark. Specifically, he compares the gross risk-adjusted performance of a capitalization-weighted buy-and-hold portfolio to those of 1,000 random-weighted (normalized to 100%) buy-and-hold portfolios of the same stocks.To ensure liquidity, he restricts the portfolios to the 500 U.S. stocks with the largest market capitalizations at the beginning of 1998. If a stock is delisted during the sample period due to merger/acquisition or bankruptcy, he sets its weight to zero at that point and renormalizes residual portfolios to 100% [per an email exchange with the author]. He focuses on Sharpe ratio and terminal value of an initial investment as key performance metrics. He ignores trading frictions, arguing that no trading is involved other than initial purchases at portfolio formation and reinvestment of dividends. Using daily total (dividend-reinvested) returns for the specified stocks and the contemporaneous 30-day U.S. Treasury bill yield as the risk-free rate during January 1998 through December 2012, he finds that: Keep Reading

What is the best estimate of the Equity Risk Premium (ERP), the return in excess of the risk-free rate required as compensation for the risk of holding equity? In his August 2014 paper entitled “A History of the Equity Risk Premium and its Estimation”, Basil Copeland summarizes recent ERP estimates and explains how the historical equity return can overstate ERP in terms of unanticipated (anomalous) capital gains. He further describes the behavior of historical and expected ERP during 1872 through 2013 and estimates ERP for 2014 through 2023. He discusses ERP estimation issues such as geometric mean versus arithmetic mean and top-down versus bottom-up forecasts. Using data from Shiller for 1871-1959 and from Damodaran for 1960-2013, he finds that: Keep Reading

Is the U.S. stock market in a Federal Reserve-driven bubble that is about to burst? In his August 2014 paper entitled “Fed by the Fed: A New Bubble Grows on Wall St.”, Oliver Dettmann examines how shifts away from quantitative easing by central banks, and the introduction of rising interest rates, may affect current valuation levels of the U.S. stock market. He focuses on a discounted real earnings model, employing a range of optimistic, moderate and pessimistic scenarios. Based on estimates of S&P 500 real earnings growth and an implied earnings discount rate derived from a sample period of January 1974 through June 2014, he finds that: Keep Reading

Is there some better predictor of long-term stock market return than the widely cited cyclically adjusted price-earnings ratio (P/E10 or CAPE)? In the July 2014 version of his paper entitled “Forecasting Equity Returns: An Analysis of Macro vs. Micro Earnings and an Introduction of a Composite Valuation Model”, Stephen Jones compares how well several fundamental and economic factors predict real long-term (10-year) equity market total return, with focus on Market Value/Gross Domestic Product (MV/GDP). He compares the predictive power of MV/GDP to those of P/E10 and Tobin’s q. He then constructs a multi-variable forecasting model that includes MV/GDP, a demographic metric and personal income-related variables. Using U.S. data since 1954 for different input variables, he finds that: Keep Reading