Fundamental Valuation

Can investors make the stock return momentum effect stronger/more reliable by isolating stocks for which many similar stocks exhibit very strong or very weak past returns? In his December 2022 paper entitled “Neighbouring Assets”, Sina Seyfi explores this question by sorting stocks based on average past returns of other stocks with the most similar sets of 94 characteristics (neighbor stocks). He measures similarity between two stocks as the aggregate distance of their normalized and winsorized (excluding top and bottom 1% of values) characteristics over a baseline rolling 10-year history. His baseline “neighborhood” is 1,000 stocks. His baseline past return metric is average monthly value-weighted return of neighbor stocks over the past year. He considers three stock universes, consisting of all NYSE/AMEX/NASDAQ stocks: (1) excluding the 5% with the smallest market capitalizations; (2) excluding those below the 20% breakpoint of NYSE market capitalizations; and, (3) excluding those below the median of NYSE market capitalizations. He each month sorts stocks into tenths (deciles) of average past return of neighborhood stocks and reforms a value-weighted portfolio that is long (short) those in the decile with the highest (lowest) neighbor-stock average past return. Using monthly characteristics and returns for the specified stocks during January 1970 (with portfolio formation commencing January 1980) through December 2021, he finds that: Keep Reading

Do stocks with high exposures to new technologies outperform? In her December 2022 paper entitled “New Technologies and Stock Returns”, Jinyoung Kim examines future returns of stocks with relatively high exposures to new technologies as measured via patent analysis. Each June, she applies machine learning to both textual and citation information to detect technology areas with high growth in new patents and identifies new technologies based on the invention descriptions. For each U.S. public company, she then estimates the intensity of firm exposure to the last three years of new technologies. Excluding firms with zero exposure, she relates new technologies exposure to stock returns by each June reforming a portfolio that is long (short) stocks with the lowest (highest) 30% of new technologies exposures. Using information for all publicized U.S. patents and patent applications since 1976 (plus information for related pre-1976 granted U.S. patents and published international patents) and monthly returns for associated stocks and widely accepted stock factors during July 1981 through June 2019, she finds that: Keep Reading

Do the widely used U.S. stock return factors exhibit long-term trend changes and shorter-term cyclic behaviors? In his November 2022 paper entitled “Trends and Cycles of Style Factors in the 20th and 21st Centuries”, Andrew Ang applies various methods to compare trends and cycles for equity value, size, quality, momentum and low volatility factors, with focus on a breakpoint at the end of 2000. He measures size using market capitalization, value using book-to-market ratio, quality using operating profitability, momentum using return from 12 months ago to one month ago and low volatility using idiosyncratic volatility relative to the Fama-French 3-factor (market, size, book-to-market) model of stock returns. He each month for each factor sorts stocks into tenths, or deciles, and computes gross monthly factor return from a portfolio that is long (short) the average return of the two deciles with the highest (lowest) expected returns. As a benchmark, he uses the value-weighted market return in excess of the U.S. Treasury bill yield. Using market and factor return data from the Kenneth French data library during July 1963 through August 2022, he finds that:

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Is market capitalization, shares outstanding times share price, really the total value of a firm? In his brief November 2022 paper entitled “The Market Capitalization Illusion”, J.B. Heaton examines the relationship between market capitalization and market value considering the slope of the demand curve for tradable assets. Based on the body of relevant research, he concludes 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 cash) when prior-month stock index level is above (below) its SMA10.
• SMA10 AND Binary 20-year – in stocks only when both SMA10 and P/E10 Binary 20-year signals are bullish, and otherwise in cash. The latter rule is bullish when last-month P/E10 is below its rolling 20-year monthly average.
• SMA10 OR Binary 20-year – in stocks when one or both of the two signals are bullish, and otherwise in cash.
• NEITHER SMA10 NOR Binary 20-year – in stocks only when neither signal is bullish, and otherwise in cash.

We use Robert Shiller’s S&P Composite Index to represent stocks. We consider buying and holding the S&P Composite Index and the standalone P/E10 Binary 20-year 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 September 2022, we find that:

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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