Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for May 2022 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for May 2022 (Final)
1st ETF 2nd ETF 3rd ETF

Fundamental Valuation

What fundamental measures of business success best indicate the value of individual stocks and the aggregate stock market? How can investors apply these measures to estimate valuations and identify misvaluations? These blog entries address valuation based on accounting fundamentals, including the conventional value premium.

Stock Index Earnings-Returns Lead-lag

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 2021 and September 2021, respectively, we find that: Keep Reading

Do High-dividend Stock ETFs Beat the Market?

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 return-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 September 2021, we find that:

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Examining Disruptive/Transformational Thematic Indexes

Leading index providers have introduced thematic stock indexes to address transformative macroeconomic, geopolitical or technological trends (for example, cybersecurity, robotics, autonomous vehicles and clean power). How do these indexes relate to standard asset pricing models? In his August 2021 paper entitled “Betting Against Quant: Examining the Factor Exposures of Thematic Indices”, David Blitz examines the performance characteristics of these indexes based on widely used factor models of stock returns and discusses why investors may follow these indexes via exchange-traded funds (ETF) despite unfavorable factor exposures. He considers 36 S&P indexes (narrower, equal-weighted) and 12 MSCI indexes (broader, capitalization-weighted) with at least three years of history. Using monthly returns for these 48 indexes and for components of the Fama-French 5-factor (market, size, book-to-market, profitability and investment) model and the momentum factor as available during June 2013 through April 2021, he finds that:

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Combining SMA10 and P/E10 Signals

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 June 2021, we find that:

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Modified Test of P/E10 Usefulness

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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Usefulness of P/E10 as Stock Market Return Predictor

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 June 2021, we find that: Keep Reading

Are Stock Quality ETFs Working?

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):

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 2021, we find that:

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Predicting Stock Market Crashes with Interpretable Machine Learning

Can machine learning-generated stock market crash predictions be amenable to human interpretation? In their June 2021 paper entitled “Explainable AI (XAI) Models Applied to Planning in Financial Markets”, Eric Benhamou, Jean-Jacques Ohana, David Saltiel and Beatrice Guez apply a gradient boosting decision tree (GBDT) to 150 technical, fundamental and macroeconomic inputs to generate daily predictions of short-term S&P 500 Index crashes. They define a crash as a 15-day S&P 500 Index return below its historical fifth percentile within the training dataset. The 150 model inputs encompass:

  1. Risk aversion metrics such as asset class implied volatilities and credit spreads.
  2. Price indicators such as returns, major stock index Sharpe ratios, distance from a long-term moving average and and equity-bond correlations.
  3. Financial metrics such as 12-month sales growth and price-to-earnings ratio forecasts.
  4. Macroeconomic indicators such Citigroup regional and global economic surprise indexes.
  5. Technical indicators such as market breath and index put-call ratio.
  6. Interest rates such as 10-year and 2-year U.S. Treasury yields and break-even inflation level.

They first rank and filter the 150 inputs based on GBDT to discard about two thirds of the variables. They then apply the Shapley value solution concept to identify the most important of the remaining variables and thereby support interpretation of methodology outputs. Using daily values of the 150 model inputs and daily S&P 500 Index roll-adjusted futures prices from the beginning of January 2003 through mid-January 2021 (with data up to January 2019 used for training, the next year for validation and the rest for testing), they find that:

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ESG News and Stock Returns

How do investors react to different kinds of firm-level environmental, social and governance (ESG) news? In their April 2021 paper entitled “Which Corporate ESG News does the Market React to?”, George Serafeim and Aaron Yoon examine stock returns around ESG news from analysts, media, advocacy groups and government regulators (but not firms themselves) as compiled/evaluated by TruValue Labs. Evaluation includes degree to which each news item is positive or negative. They segment the news sample based on materiality classifications from the Sustainability Accounting Standards Board (SASB). Using daily market-adjusted and industry-adjusted stock returns associated with 111,020 firm-day ESG news items spanning 3,126 companies during January 2010 through June 2018, they find that: Keep Reading

SACEVS with Margin

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 ratiocompound 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 February 2021, we find that:

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