Fundamental Valuation

Stock return anomaly studies based on firm accounting variables generally employ annually reformed portfolios that are long (short) the tenth of stocks expected to perform well (poorly). Does adding monthly portfolio updates based on technical stock price trend measurements boost anomaly portfolio performance? In the June 2015 version of their paper entitled “Anomalies Enhanced: The Use of Higher Frequency Information”, Yufeng Han, Dayong Huang and Guofu Zhou test eight equal-weighted long-short portfolios that combine annual screening based on a predictive accounting variable with monthly screening based on a simple moving average (SMA)-based stock price trend rule. The eight accounting variables (screened in June based on prior December data) are: (1) book-to-market ratio; (2) gross profitability; (3) operating profitability; (4) asset growth; (5) investment growth; (6) net stock issuance; (7) accruals; and, (8) net operating assets. The price trend screen excludes from the long (short) side of the portfolio any stock for which 50-day SMA is less than (greater than) 200-day SMA at the end of the prior month. Using accounting and daily price data for a broad sample of U.S. stocks during July 1965 through December 2013, they find that: Keep Reading

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

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

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

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

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

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

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

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

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