Fundamental Valuation

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. Under 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 and how are they changing during recent months? Using recent actual and forecasted earnings and dividend data from Standard & Poor’s and associated S&P 500 Index levels as available through September 9, 2022, 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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Will equity funds “managed” by artificial intelligence (AI) outperform human investors? To investigate, we consider the performance of AI Powered Equity ETF (AIEQ). Per the offeror, the EquBot model supporting AIEQ: “…leverages IBM’s Watson AI to conduct an objective, fundamental analysis of U.S. domiciled common stocks, including Special Purpose Acquisitions Corporations (“SPAC”), and real estate investment trusts (“REITs”) based on up to ten years of historical data and apply that analysis to recent economic and news data… Each day, the EquBot Model…identifies approximately 30 to 200 companies with the greatest potential over the next twelve months for appreciation and their corresponding weights, targeting a maximum risk adjusted return versus the broader U.S. equity market. …The EquBot model limits the weight of any individual company to 10%. At times, a significant portion of the Fund’s assets may consist of cash and cash equivalents.” We use SPDR S&P 500 (SPY) as a simple benchmark for AIEQ performance. Using daily and monthly dividend-adjusted closes of AIEQ and SPY from AIEQ inception (October 18, 2017) through June 2022, we find that: Keep Reading

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

Are stock quality strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider five ETFs, all currently available (from oldest to youngest):

• Invesco S&P 500 Quality ETF (SPHQ) – seeks to track performance of S&P 500 stocks with the highest quality scores based on firm return on equity, accruals ratio and financial leverage ratio, reformed semi-annually. The benchmark is SPDR S&P 500 (SPY).
• iShares Edge MSCI USA Quality Factor ETF (QUAL) – seeks to track performance of U.S. large-capitalization and mid-capitalization stocks selected based return on firm equity, earnings variability and debt-to-equity. The benchmark is SPY.
• iShares Edge MSCI Intl Quality Factor ETF (IQLT) – seeks to track performance of large-capitalization and mid-capitalization developed international stocks screened for attractive return-on-equity, earnings variability and debt-to-equity. The benchmark is iShares MSCI ACWI ex U.S. ETF (ACWX).
• Fidelity Quality Factor ETF (FQAL) – seeks to track performance of U.S. large-capitalization and mid-capitalization stocks with a higher firm quality profile than the broader market. The benchmark is Vanguard Russell 1000 Index Fund ETF (VONE).
• Vanguard U.S. Quality Factor ETF (VFQY) – applies a rules-based quantitative model to select U.S. common stocks with strong fundamentals (strong profitability and healthy balance sheets) across market capitalizations, sectors and industry groups. The benchmark is iShares Russell 3000 ETF (IWV).

We calculate monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the stock quality ETFs and benchmarks as available through June 2022, we find that:

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“SACEMS with Margin” investigates the use of target 2X leverage via margin to boost the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS). “SACEVS with Margin” investigates the use of target 2X leverage via margin to boost the performance of the “Simple Asset Class ETF Value Strategy” (SACEVS). In response, a subscriber requested a sensitivity test of 1.25X, 1.50X and 1.75X leverage targets. To investigate effects of these leverage targets, we separately augment SACEVS Best Value, SACEMS EW Top 2 and the equally weighted combination of these two strategies by: (1) initially applying target leverage via margin; (2) for each month with a positive portfolio return, adding margin at the end of the month to restore target leverage; and, (3) for each month with a negative portfolio return, liquidating shares at the end of the month to pay down margin and restore target leverage. Margin rebalancings are concurrent with portfolio reformations. We focus on gross monthly Sharpe ratio, compound annual growth rate (CAGR) and maximum drawdown (MaxDD) for committed capital as key performance statistics. We use the 3-month Treasury bill (T-bill) yield as the risk-free rate. Using monthly total (dividend-adjusted) returns for the specified assets since July 2002 for SACEVS and since July 2006 for SACEMS, both through May 2022, we find that:

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Is leveraging with margin a good way to boost the performance of the “Simple Asset Class ETF Value Strategy” (SACEVS)? To investigate effects of margin, we augment SACEVS by: (1) initially applying 2X leverage via margin (limited by Federal Reserve Regulation T); (2) for each month with a positive portfolio return, adding margin at the end of the month to restore 2X leverage; and, (3) for each month with a negative portfolio return, liquidating shares at the end of the month to pay down margin and restore 2X leverage. Margin rebalancings are concurrent with portfolio reformations. We focus on gross monthly Sharpe ratio, compound annual growth rate (CAGR) and maximum drawdown (MaxDD) for committed capital as key performance statistics for Best Value (which picks the most undervalued premium) and Weighted (which weights all undervalued premiums according to degree of undervaluation) variations of SACEVS. We use the 3-month Treasury bill (T-bill) yield as the risk-free rate and consider a range of margin interest rates as increments to this yield. Using monthly total returns for SACEVS and monthly T-bill yields during July 2002 through May 2022, we find that:

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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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