Fundamental Valuation

Economic data arrive too slowly to help investors navigate crises such as the 2019 coronavirus (COVID-19) outbreak. Are there data that support quick reactions? In their March 2020 paper entitled “Coronavirus: Impact on Stock Prices and Growth Expectations”, Niels Gormsen and Ralph Koijen employ equity index dividend futures by maturity to understand the evolution of investor reactions to COVID-19 outbreak and subsequent policy actions. They argue that a stock market decline means that expected future dividends fall and/or the discount rate for future dividends rises, differently by maturity. These changes in expectations affect stock market valuation. Using daily dividend futures closing mid-quotes in the U.S. and settlement prices in the EU during January 2006 through March 25, 2020, they find that:

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A subscriber asked about current evidence that high-dividend stocks outperform the market. To investigate, from a practical perspective, we compare performances of five high-dividend stock exchange-traded funds (ETFs) with relatively long histories to that of SPDR S&P 500 (SPY) as a proxy for the U.S. stock market. The five high-dividend stock ETFs are:

• iShares Select Dividend (DVY), with inception November 2003.
• PowerShares Dividend Achievers ETF (PFM), with inception September 2005.
• SPDR S&P Dividend ETF (SDY), with inception November 2005.
• WisdomTree Dividend ex-Financials ETF (DTN), with inception June 2006.
• Vanguard High Dividend Yield ETF (VYM), with inception November 2006.

For each of these ETFs, we compare average monthly total (dividend-reinvested) return, standard deviation of total monthly returns, monthly reward-risk ratio (average monthly return divided by standard deviation), compound annual growth rate (CAGR) and maximum drawdown (MaxDD) to those for SPY over matched sample periods. We also look at alphas and betas for the five ETFs based on simple regressions of monthly returns versus SPY returns. Using monthly total returns for the five high-dividend stock ETFs and SPY over available sample periods through February 2020, we find that:

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Why does the coincident relationship between U.S. aggregate corporate earnings growth and stock market return change from negative in older research to positive in recent research? In their January 2020 paper entitled “Assessing the Structural Change in the Aggregate Earnings-Returns Relation”, Asher Curtis, Chang‐Jin Kim and Hyung Il Oh examine when the change in the aggregate earnings growth-market returns relationship occurs. They then examine factors explaining the change based on asset pricing theory (expected cash flow and expected discount rate). They calculate aggregate earnings growth as the value-weighted average of year-over-year change in firm quarterly earnings scaled by beginning-of-quarter stock price. They consider only U.S. firms with accounting years ending in March, June, September or December, and they exclude firms with stock prices less than $1 and firms in the top and bottom 0.5% of quarterly earnings growth. They calculate corresponding quarterly stock market returns from one month prior to two months after fiscal quarter ends to capture earnings announcement effects. Using quarterly earnings and returns data as specified for a broad sample of U.S. public firms from the first quarter of 1970 through the fourth quarter of 2016, they find that:

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Do investors systematically and exploitably underreact to deviations in firm fundamentals from recent averages? In their January 2020 paper entitled “Anchoring on Past Fundamentals”, Doron Avramov, Guy Kaplanski and Avanidhar Subrahmanyam investigate how deviations of quarterly firm accounting variables from averages over recent quarters relate to future returns across stocks. They first construct a stock performance deviation index (PDI) based on seven variables: (1) cash and short-term investments, (2) retained earnings, (3) operating income, (4) sales, (5) capital expenditures, (6) invested capital and (7) inventories. They then each month for each stock starting June 1977:

• Calculate the deviation for each variable as the difference between its most recent quarterly value and its average over the preceding three quarters, scaled by total assets.
• Rank each deviation (in percentiles) relative to deviations for the same variable for all stocks.
• Calculate PDI for a stock as the equally weighted average of percentile rankings across all seven variables.

They extend this approach to a more comprehensive fundamental-based deviation index (FDI) that considers deviations of all Compustat accounting variables plus 14 commonly used accounting ratios, with weights of deviation percentile rankings optimized via least absolute shrinkage and selection operator (LASSO) regression starting January 1979. For all variables, if the exact release date is unavailable, they assume a 60-day delay in release. For portfolio tests, they calculate returns to hedge portfolios that are long (short) stocks in the top (bottom) tenth, or decile, of PDIs or FDIs, with holding intervals ranging from one to 24 months. Using monthly data needed to construct PDI, FDI and 30 style, technical, fundamental and liquidity control variables across a broad sample of reasonably liquid U.S. common stocks with positive book values and prices over $5 during January 1976 through October 2017, they find that: Keep Reading

In response to the U.S. stock market timing backtest in “Usefulness of P/E10 as Stock Market Return Predictor”, a subscriber suggested combining a 10-month simple moving average (SMA10) technical signal with a P/E10 (or Cyclically Adjusted Price-Earnings ratio, CAPE) fundamental signal. Specifically, we test:

• SMA10 – bullish/in stocks (bearish/in government bonds) when prior-month stock index level is above (below) its SMA10.
• SMA10 AND Binary 20-year Bond – in stocks only when both SMA10 and P/E10 Binary 20-year signals are bullish, and otherwise in bonds. The latter rule is bullish when last-month P/E10 is below its rolling 20-year monthly average.
• SMA10 OR Binary 20-year Bond – in stocks when one or both of the two signals are bullish, and otherwise in bonds.
• NEITHER SMA10 NOR Binary 20-year Bond – in stocks only when neither signal is bullish, and otherwise in bonds.

We use Robert Shiller’s S&P Composite Index to represent stocks. We estimate monthly levels of a simple 10-year government bond index and associated monthly returns using Shiller yield data as described in “Usefulness of P/E10 as Stock Market Return Predictor”. We consider buying and holding the S&P Composite Index and the P/E10 Binary 20-year Bond strategy as benchmarks. Using monthly data from Robert Shiller, including S&P Composite Index level, associated dividends, 10-year government bond yields and values of P/E10 as available during January 1871 through December 2019, we find that:

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Is there a better stock value ratio than commonly used ones such as book-to-market, dividend-to-price, earnings-to-price and cash flow-to-price ratios? In the January 2020 revision of his paper entitled “A New Value Strategy”, Baolian Wang investigates the effectiveness of cash-based operating profitability-to-price (COP/P) as a value ratio. He computes COP as operating profitability minus accruals, with operating profitability defined as revenue minus cost of goods sold and reported selling, general and administrative expenses (not including expenditures on research and development). He each year at the end of June sorts stocks into tenths, or deciles, based on COP/P and then calculates next-month excess returns for a value-weighted or equal-weighted hedge portfolio that is long (short) the decile with the highest (lowest) values of COP/P. Using monthly returns and annual, 6-month lagged and groomed accounting data for non-financial U.S. common stocks during 1963 through 2018 period, he finds that: Keep Reading

In response to the U.S. stock market timing backtest in “Usefulness of P/E10 as Stock Market Return Predictor”, a subscriber suggested a modification for exploiting P/E10 (or Cyclically Adjusted Price-Earnings ratio, CAPE). Instead of binary signals that buy (sell) stocks when P/E10 crosses below (above) its historical average, employ a scaled allocation to stocks that considers how far P/E10 is from average. Specifically:

• If P/E10 is more than 2 standard deviations below its past average, allocate 100% to the S&P Composite Index.
• If P/E10 is more than 2 standard deviations above its past average, allocate 0% to the S&P Composite Index.
• If P/E10 is between these thresholds, allocate a percentage (ranging from 100% to 0%) to the S&P Composite Index, scaled linearly.

To investigate, we backtest this set of rules. Using monthly data from Robert Shiller, including S&P Composite Index level, associated dividends, 10-year government bond yields and values of P/E10 as available during January 1871 through December 2019, we find that:

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Does P/E10 (or Cyclically Adjusted Price-Earnings ratio, CAPE) usefully predict U.S. stock market returns? Per Robert Shiller’s data, P/E10 is inflation-adjusted S&P Composite Index level divided by average monthly inflation-adjusted 12-month trailing earnings of index companies over the last ten years. To investigate its usefulness, we consider in-sample regression/ranking tests and out-of-sample cumulative performance tests. Using monthly values of P/E10, S&P Composite Index levels (calculated as average of daily closes during the month), associated dividends (smoothed), 12-month trailing real earnings (smoothed) and interest rates as available during January 1871 through December 2019, we find that: Keep Reading

Are there any seasonal, technical or fundamental strategies that reliably time the U.S. stock market as proxied by the S&P 500 Total Return Index? In the February 2018 version of his paper entitled “Investing In The S&P 500 Index: Can Anything Beat the Buy-And-Hold Strategy?”, Hubert Dichtl compares excess returns (relative to the U.S. Treasury bill [T-bill] yield) and Sharpe ratios for investment strategies that time the S&P 500 Index monthly based on each of:

• 4,096 seasonality strategies.
• 24 technical strategies (10 slow-fast moving average crossover rules; 8 intrinsic [time series or absolute] momentum rules; and, 6 on-balance volume rules).
• 18 fundamental variable strategies based on a rolling 180-month regression, with 1950-1965 used to generate initial predictions.

In all cases, when not in stocks, the strategies hold T-bills as a proxy for cash. His main out-of-sample test period is 1966-2014, with emphasis on a “crisis” subsample of 2000-2014. He includes extended tests on seasonality and some technical strategies using 1931-2014. He assumes constant stock index-cash switching frictions of 0.25%. He addresses data snooping bias from testing multiple strategies on the same sample by applying Hansen’s test for superior predictive ability. Using monthly S&P 500 Index levels/total returns and U.S. Treasury bill yields since 1931 and values of fundamental variables since January 1950, all through December 2014, he finds that:

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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 recent actual and forecasted earnings and dividend data from Standard & Poor’s, we find that: Keep Reading