Fundamental Valuation

What fundamental measures of business success best indicate the value of individual stocks and the aggregate stock market? How can investors apply these measures to estimate valuations and identify misvaluations? These blog entries address valuation based on accounting fundamentals, including the conventional value premium.

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RTV and REY Model Updates

We have updated the details of the Reversion-to-Value (RTV) Model and the Real Earnings Yield (REY) Model of the U.S. stock market to incorporate data for 2014.

Profitability Momentum as a Stock Return Indicator

Is firm profitability trend, or momentum, a useful indicator of future stock returns? In their December 2014 paper entitled “The Trend in Firm Profitability and the Cross Section of Stock Returns”, Ferhat Akbas, Chao Jiang and Paul Koch investigate the relationship between trend in firm profitability and stock returns, while controlling for level of profitability. They calculate gross profit quarterly as sales minus cost of goods sold, divided by total assets. They specify level of profitability as average gross profit over the past eight quarters. They specify trend in profitability as linear slope over the past eight quarters. They employ assumptions that ensure public availability of all data at the time of measurement, including a skip-month between portfolio formation and holding period. Using firm characteristics and returns for a broad sample of U.S. common stocks during January 1977 through December 2012, they find that: Keep Reading

Stock Market Valuation Ratio Trends

To determine whether the stock market is expensive or cheap, some experts use aggregate valuation ratios, either trailing or forward-looking, such as earnings-price ratio (E/P) and dividend yield. Operating under a belief that such ratios are mean-reverting, most imminently due to movement of stock prices, these experts expect high (low) future stock market returns when these ratios are high (low). Where are the ratios now? Using the most recent actual and forecasted earnings and dividend data from Standard & Poor’s, we find that: Keep Reading

Models, Trading Calendar and Momentum Strategy Updates

We have updated the S&P 500 Market Models summary as follows:

  • Extended Market Models regressions/rolled projections by one month based on data available through December 2014.
  • Updated Market Models backtest charts and the market valuation metrics map based on data available through December 2014.

We have updated the Trading Calendar to incorporate data for December 2014.

We have updated the the monthly asset class momentum winners and associated performance data at Momentum Strategy.

Average Investor Stock Allocation a Better Predictor than P/E10?

A subscriber suggested evaluation of average investor allocation to stocks as “The Single Greatest Predictor of Future Stock Market Returns”. For this evaluation, we test simple ways to time the broad U.S. stock market using the quarterly time series for average U.S. investor allocation to stocks as provided in the article. We assume that the dates in this series are the first days of measured quarters. Using this quarterly series and the contemporaneous S&P 500 Index during December 1951 through September 2014, and quarterly dividend-adjusted returns for SPDR S&P 500 ETF (SPY) and contemporaneous 13-week U.S. Treasury bill (T-bill) yields during March 1993 through September 2014, we find that: Keep Reading

When Consensus Earnings Forecast and Stock Return Diverge

Do changes in consensus analyst earnings forecasts that disagree with contemporaneous stock returns signal exploitable mispricings? In their November 2014 paper entitled “To Follow or Not to Follow – An Analysis of the Profitability of Portfolio Strategies Based on Analyst Consensus EPS Forecasts”, Rainer Baule and Hannes Wilke investigate the power of a variable that relates consensus earnings forecast momentum to stock price momentum to predict stock returns. Specifically, the variable is the ratio of (1+change in consensus earnings forecast) to (1+stock return) over the last six months. Their consensus earnings forecast metric is a rolling average of consensus estimates for the current and next years weighted according to proximity of the current-year forecast to the end of the firm’s fiscal year (for example, three months before the end of the fiscal year, the rolling 12-month metric is 3/12 of the forecast for the current year plus 9/12 of the forecast for next year). They measure predictive power via a portfolio that is each month long (short) the fifth of stocks with the highest (lowest) last-month variable values. They evaluate both raw excess portfolio performance (relative to the risk-free rate) and four-factor portfolio alpha (adjusting for market, size, book-to-market and momentum factors). They limit the stock universe to the widely covered and very liquid components of the S&P 100 Index. Using monthly analyst consensus earnings forecasts and total returns for S&P 100 stocks during February 1978 through December 2013 (a total of 278 stocks listed for at least one month), they find that: Keep Reading

Usefulness of P/E10 as Stock Market Return Predictor

Is P/E10 (or Cyclically Adjusted Price-Earnings ratio, CAPE) a useful indicator of U.S. stock market valuation? P/E10, as calculated in Robert Shiller’s data set, is the ratio of the inflation-adjusted S&P Composite Index level to the average monthly inflation-adjusted 12-month trailing earnings of index companies over the previous ten years. To investigate its usefulness, we consider in-sample regression and ranking and cumulative performance tests. Using Robert Shiller’s monthly estimates of the nominal and real S&P Composite Index (calculated as average of daily closes during the month), associated dividends, 12-month trailing real earnings and long-term interest rate as available during January 1871 through November 2014, we find that: Keep Reading

Components of U.S. Stock Market Returns by Decade

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

Revisiting Performance of Piotroski’s FSCORE

Is Piotroski’s FSCORE really as effective at picking stocks as indicated in the original study, which screens stocks with high book-to-market ratios to isolate those that will eventually provide high returns? In their April 2014 paper entitled “Implementability of Trading Strategies Based on Accounting Information: Piotroski (2000) Revisited”, Sohyung Kim and Cheol Lee re-examine the FSCORE testing methodology. Their broader goal is to investigate the effect of a problematic research approach used in many accounting-based studies. Specifically, they evaluate the difference in investment outcomes between:

  • Accounting studies that typically accumulate returns for individual stocks starting three or four months after their respective fiscal year-ends.
  • Finance studies that synchronize return accumulation with a common annual starting date (regardless of the fiscal year-ends of individual firms) via periodically reformed portfolios with a specified weighting scheme (such as equal weighting).

Accounting study assumptions may confound real-time position weighting due to lack of timely information on how many stocks should be in the portfolio, thereby incorporating look-ahead bias. The study replicates the original Piotroski methodology with equal position weighting, and then makes adjustments to synchronize return calculations by each year reforming a hedge portfolio that is long (short) the equally weighted stocks with high (low) FSCOREs based on a common starting date and most recently available firm fundamentals. Using monthly data for a broad sample of U.S. common stocks with enough information to calculate FSCOREs during 1976 through 2007, they find that:

Keep Reading

Value in Simplicity?

Does compounding rules tend to improve the performance of stock-picking strategies? In the October 2014 draft of their paper entitled “Does Complexity Imply Value, AAII Value Strategies from 1963 to 2013″, Wesley Gray, Jack Vogel and Yang Xu compare 13 stock strategies labeled as “Value” by the American Association for Individual Investors (AAII) to each other and to a simple “low-price” value strategy. The simple strategy each year selects the tenth of stocks with the highest Earnings Before Interest, Taxes, Depreciation and Amortization-to-Total Enterprise Value ratios (EBITDA/TEV), excluding financial firms. To ensure liquidity, they focus on stocks with market capitalizations above the NYSE 40th percentile. To ensure real-time availability of inputs, they lag firm accounting data. To assess performance consistency, they consider three subperiods: July 1963 through December 1980; January 1981 through December 1996; and, January 1997 through December 2013. Because portfolios are equally weighted, they include the S&P 500 equal-weight total return index (S&P 500 EW) as a benchmark. Using stock price and firm accounting data for a broad sample of U.S. common stocks during July 1963 through December 2013, they find that: Keep Reading

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