Fundamental Valuation

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 September 2022, 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 September 2022, we find that: Keep Reading

A subscriber asked about the lead-lag relationship between S&P 500 earnings and S&P 500 Index returns. To investigate, we relate actual aggregate S&P 500 operating and as-reported earnings to S&P 500 Index returns at both quarterly and annual frequencies. Earnings forecasts are available well in advance of returns. Actual earnings releases for a quarter occur throughout the next quarter. Using quarterly S&P 500 earnings and index levels during March 1988 through June 2022 and September 2022, respectively, we find that: Keep Reading

What kind of firm growth should long-term investors value most? In his August 2022 paper entitled “The Role of Net Income Growth in Explaining Long-Horizon Stock Returns”, Hendrik Bessembinder relates decade compound stock returns to same-decade growth in firm net income, sales and assets and same-decade average profitability (income-to-assets ratio). His universe consists of U.S. common stocks/decades with available data and inflation-adjusted (to end-of-sample) market capitalization at least $500 million at the beginning of a decade. He further excludes firm/decades with net income-to-total assets ratio at the beginning of the decade less than 0.15% (the bottom percentile of profitable firms). He calculates both decade excess returns (relative to U.S. Treasury bill return) and decade returns relative to that of the value-weighted U.S. stock market, both expressed on an annualized basis. For growth variables, he measures growth based on differences between the last and first years of a decade, and ultimately excludes the top and bottom 1% to mitigate effects of extreme outliers. For each decade and each variable, he then ranks stocks into fifths (quintiles) and calculates the difference in average returns between the highest and lowest quintiles. He looks at the full sample of five decades and subsamples of the early decades (1970s and 1980s) and recent decades (1990s, 2000s, 2010s). Using the specified accounting data and associated stock returns during January 1970 through December 2019, he finds that:

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Which variables deserve greatest focus when predicting stock market returns? In their July 2022 paper entitled “Searching for the Best Conditional Equity Premium Model”, Hui Guo, Saidat Sanni and Yan Yu exhaustively explore combinations of 18 previously identified potential stock market return predictors to isolate the most powerful subset. They focus on a best subset selection method with a penalty on complexity, thereby suppressing data snooping bias and selecting a manageable subset. For robustness, they consider four alternative variable selection methods. Using quarterly U.S. data for these 18 variables and S&P 500 Index levels/returns during 1947 through 2020, they find that: Keep Reading

Do the Best Brands, as published annually by Interbrand based on net present value of predicted incremental earnings due to brand, offer superior investment performance due to pricing power and superior operating practices? In their June 2022 paper entitled “Is Buffett Right? Brand Values and Long-run Stock Returns”, Hamid Boustanifar and Young Dae Kang examine the investment performance of Best Brands. Best Brands companies must be global, have publicly available financial data, be visible and have the expectation of positive long-term profitability above the cost of capital). Up to 2007 (subsequently), Interbrand published Best Brand lists in July or August (late September or October). The authors each year reform a Best Brands portfolio limited to U.S. firms the first day of the month after publication, thereby excluding immediate announcement effects on stock prices. For stocks encompassing multiple brands (e.g., Google and YouTube for Alphabet), they map brands to stocks by summing brand values. Using firm characteristics, accounting data and stock prices for a broad sample of U.S. stocks during 2000 (the first Best Brands list) through 2020, they find that:

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A subscriber suggested looking at the S&P 500 price-to-sales ratio (P/S) as an indicator for timing the U.S. stock market. To investigate, we relate P/S and change in P/S to S&P 500 Index (SP500) returns, as follows:

1. Conduct lead-lag analyses for quarterly P/S versus quarterly SP500 returns, and for quarterly change in P/S versus SP500 returns.
2. Calculate average next-quarter SP500 returns by range of quarterly values of P/S and quarterly changes in P/S.

Using quarterly S&P 500 P/S data since the end of December 2000 and quarterly closing SP500 levels since December 1999, both through June 2022, we find that: Keep Reading

Is the Buffett Indicator, the ratio of total stock market capitalization to Gross Domestic Product (GDP), a useful indicator of future stock market performance internationally? In their March 2022 paper entitled “The Buffett Indicator: International Evidence”, Laurens Swinkels and Thomas Umlauft extend Buffett Indicator research from the U.S. to 14 international equity markets. Because the value of the indicator varies so much across countries at a given time (for example, 1.48 for the U.S. and 0.55 for Germany at the end of 2019), they first look at time-series predictability of returns by the Buffett Indicator within each country. They then compare predictive power of the Buffett Indicator to those of Shiller’s cyclically-adjusted price-to-earnings ratio (CAPE or P/E10) and mean-reversion in stock returns. Finally, they test a trading strategy that invests in the stock markets of those countries having low values of the Buffett Indicator relative to their respective (expanding window) histories. Using stock market valuation and earnings data and GDP series for 14 countries as available during 1973 through 2019, they find that:

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Can investors improve the performance of stock momentum portfolios by isolating stocks that “hold” their momentum? In their April 2022 paper entitled “Enduring Momentum”, Hui Zeng, Ben Marshall, Nhut Nguyen and Nuttawat Visaltanachoti exploit firm characteristics to identify stocks that continue to be winners or losers after selection as momentum stocks (stocks with enduring momentum). They measure momentum by each month ranking stocks into equal-weighted tenths, or deciles, based on past 6-month returns, with the top (bottom) decile designated winners (losers). They then develop a model that uses information from 37 firm characteristics to estimate each month the probability that each winner or loser stock will continue as a winner or loser during each of the next six months. They verify that the model reasonably predicts momentum persistence and proceed to test the economic value of the predictions by each month reforming an enduring momentum hedge portfolio that is long (short) the 10 equal-weighted winner (loser) stocks with the highest probabilities of remaining winners (losers) and holding the portfolio for six months. They compare the performance of this portfolio to that of a conventional momentum portfolio that is each month long the entire winner decile and short the entire loser decile, also held for six months. Using returns for a broad sample of U.S. common stocks priced over $1.00 and 37 associated firm characteristics during January 1980 through December 2018, they find that: Keep Reading

The “Simple Asset Class ETF Value Strategy” (SACEVS) allocates across 3-month Treasury bills (Cash, or T-bill), iShares 20+ Year Treasury Bond (TLT), iShares iBoxx $ Investment Grade Corporate Bond (LQD) and SPDR S&P 500 (SPY) according to the relative valuations of term, credit and equity risk premiums. Does applying a simple moving average (SMA) filter to SACEVS allocations improve its performance? Since many technical traders use a 10-month SMA (SMA10), we apply SMA10 filters to dividend-adjusted prices of TLT, LQD and SPY allocations. If an allocated asset is above (below) its SMA10, we allocate as specified (to Cash). This rule does not apply to any Cash allocation. We focus on gross compound annual growth rates (CAGR), maximum drawdowns (MaxDD) and annual Sharpe ratios (using average monthly T-bill yield during a year as the risk-free rate for that year) of SACEVS Best Value and SACEVS Weighted portfolios. We compare to baseline SACEVS as currently tracked and to the SMA rule applied to a 60%-40% monthly rebalanced SPY-TLT benchmark portfolio (60-40). Finally, we test sensitivity of main findings to varying the SMA lookback interval. Using SACEVS historical data, monthly dividend-adjusted closing prices for the asset class proxies and yield for Cash during July 2002 (the earliest all funds are available) through March 2022, we find that:

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