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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Momentum Strategy, Value Strategy and Trading Calendar Updates

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

We have updated the Trading Calendar to incorporate data for June 2015.

Tilting or Indexing, Fundamentally?

Are there gradual steps toward a fundamental stock index that work just as well? In their April 2015 draft paper entitled “Decomposing Fundamental Indexation”, Gregg Fisher, Ronnie Shah and Sheridan Titman compare fundamental indexing strategies to strategies that tilt a market index toward high fundamental-to-price stocks. Fundamental indexing strategies weight stocks by firm fundamentals instead of market capitalizations, ignoring any information in stock prices. The tilt strategies adjust market weights with multipliers linearly scaled to fundamental-to-price ratios across a universe of stocks. Reflecting extreme fundamentals ratios for smaller stocks, the range of multipliers for stocks in the upper (lower) half of market capitalizations is 0 to 2 (0 to 4). After applying multipliers, tilt the strategies normalize weights so that they sum to 100%. Rebalancing for all portfolios is annual on the last day in April, incorporating a minimum four-month lag between the end of the financial reporting period and portfolio formation. Using data for a broad sample of U.S. common stocks during May 1975 through December 2014, they find that: Keep Reading

Tactical U.S. Stock Market Allocations Based on Valuation Ratios

Do simple stock market valuation ratios work for tactical allocation? In his April 2015 paper entitled “Multiples, Forecasting, and Asset Allocation”, Javier Estrada investigates whether investors can outperform a 60-40 stocks-bonds benchmark portfolio via tactical strategies based on one of three simple stock market valuation ratios: (1) dividend-price ratio (D/P); (2) price-earnings ratio (P/E); or, (3) cyclically adjusted price-earnings ratio (CAPE, or P/E10). The valuation‐based strategies take aggressive (conservative) stances when stocks are cheap (expensive) via combinations of the following rules:

  • Designate stocks as cheap (expensive) when a valuation ratio is below (above) its inception-to-date mean by one standard deviation (1SD) or two standard deviations (2SD).
  • Use 60-40 stocks-bonds allocations when stocks are not cheap or expensive. When stocks are cheap (expensive), shift toward stocks (bonds) by 20% to 80-20 (40-60) or by 30% to 90-10 (30-70). 
  • Rebalance either annually or monthly.

For the benchmark portfolio and the valuation-based portfolios when in 60-40 stance, rebalancing occurs only when the stock allocation drifts below 55% or above 65%. To accrue at least 20 years of data for initial valuations, strategy performance measurements span 1920 through 2014 (95 years). Calculations lag dividends and earnings by three months to ensure real-time availability. Testing ignores trading frictions and tax implications. Using monthly S&P 500 Index total returns and the yield on 90-day U.S. Treasury bills (T-bills) during September 1899 through December 2014, he finds that: Keep Reading

Cash Flow Part of Profitability as a Stock Return Predictor

Is the part of profitability based on cash flow more informative than the part based on accruals? In their March 2015 paper entitled “Accruals, Cash Flows, and Operating Profitability in the Cross Section of Stock Returns”, Ray Ball, Joseph Gerakos, Juhani Linnainmaa and Valeri Nikolaev investigate the power of the cash flow part of profitability to predict stock returns. They compare its predictive power to those of overall operating profitability and of the accruals part of profitability. Using monthly returns and annual firm accounting data (lagged six months) for a broad sample of U.S. common stocks during July 1963 through December 2013, they find that: Keep Reading

Mojena Market Timing Model

The Mojena Market Timing strategy (Mojena), developed and maintained by professor Richard Mojena, is a method for timing the broad U.S. stock market based on a combination of 11 monetary, fundamental, technical and sentiment indicators to predict changes in intermediate-term and long-term market trends. He adjusts the model annually to incorporate new data year by year. Professor Mojena offers a hypothetical backtest of the timing model since 1970 and a live investing test since 1990 based on the S&P 500 Index (with dividends). To test the robustness of the strategy’s performance, we consider a sample period commencing with availability of SPDR S&P 500 (SPY) as a conveniently investable proxy for the S&P 500 Index. As benchmarks, we consider both buying and holding SPY (Buy-and-Hold) and trading SPY with crash protection based on the 10-month simple moving average of the S&P 500 Index (SMA10). Using the trade dates from the Mojena Market Timing live test, daily dividend-adjusted closes for SPY and daily yields for 13-week Treasury bills (T-bills) over the period 1/29/93 through January 2015 (22 years), we find that: Keep Reading

Quality-enhanced Size Effect

Given the conflicting evidence about the import of the size effect, is there a way investors can extract a reliable premium from small stocks? In their January 2015 draft paper entitled “Size Matters, If You Control Your Junk”, Clifford Asness, Andrea Frazzini, Ronen Israel, Tobas Moskowitz and Lasse Pedersen examine whether controlling for firm quality mitigates the following seven unfavorable empirical findings that the size effect:

  1. Is weak overall in the U.S.
  2. Has not worked out-of-sample and varies significantly over time.
  3. Only works for extremely small stocks.
  4. Only works in January.
  5. Only works for market capitalization-based measures of size.
  6. Is subsumed by illiquidity.
  7. Is weak internationally.

They control for quality using a Quality-Minus-Junk (QMJ) factor based on profitability, profit growth, safety and payout. They use a portfolio test approach, ranking stocks into value-weighted tenths (deciles) each month to examine differences among stocks sorted by factor. Focusing on returns and factor metrics for a broad sample of U.S. common stocks during July 1957 (when quality metrics become available) through December 2012 and for 23 other developed country stock markets during January 1983 through December 2012, they find that: Keep Reading

Quality as Discriminator of Country Stock Markets

Can investors usefully apply stock quality metrics to entire country stock markets? In his December 2014 paper entitled “Country Selection Strategies Based on Quality”, Adam Zaremba investigates whether quality metrics effectively predict country stock market index performance. He also examines whether (1) quality-size and quality-value double sorts enhance country-level value and size strategies; and, (2) high-quality markets offer a hedge during times of market distress. He considers six quality metrics: accruals, cash (cash divided by total assets), profitability (return on assets), leverage (total assets divided by common equity), payout (dividends as a fraction of income) and turnover (dollar volume of trading divided by market capitalization). Firm metric aggregation weightings are those used in constructing respective country indexes. After lagging the time series by three months to avoid a look-ahead bias, he forms capitalization-weighted portfolios of country markets by ranking them into fifths (quintiles) based on quality metric sorts. He identifies times of market distress based on: the spread between U.S. LIBOR and U.S. Treasury bill yields; VIX; the spread between U.S. corporate BBB bond and 10-year U.S. Treasury note yields; and, the spread between U.S. Treasury 10-year and 2-year note yields. Using stock market index returns and accounting data in U.S. dollars across 77 country stock markets during February 1999 through September 2014 as available, and contemporaneous market distress indicator values, he finds that: Keep Reading

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

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