Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for September 2022 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for September 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.

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:

Keep Reading

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:

Keep Reading

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

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:

Keep Reading

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

Effects of Capitalizing Intangibles on Factor Models of Stock Returns

Under current U.S. accounting rules, many investments in innovation, human resources and brand that are crucial to long-term competitiveness immediately reduce operating profits and earnings (are expensed rather than capitalized). Does failure to incorporate such intangible investments in firm investment and valuation ratios (book-to-market, profitability and return on equity) harm equity investment decisions? In their January 2021 paper entitled “Intangible Capital in Factor Models”, Huseyin Gulen, Dongmei Li, Ryan Peters and Morad Zekhnini study impacts of capitalizing intangible investments on three widely used factor models of stock returns: 3-factor (market, size, book-to-market)5-factor (adding profitability and investment); and, q-factor (market, size, investment, profitability). They focus on effects of intangibles on book-to-market ratio, investment and profitability. Using accounting data and stock returns for a broad sample of U.S. firms during July 1977 through December 2018, they find that: Keep Reading

Poor Firm Management and Stock Returns

Do negative environmental, social and governance (ESG) incidents (environmental pollution,
poor employment conditions or anti-competitive practices) indicate poor firm management and therefore underperforming stocks? In his February 2021 paper entitled “ESG Incidents and Shareholder Value”, Simon Glossner analyzes ESG incident data to determine whether: (1) history is predictive of future ESG incidents; (2) high incident rates impact firm performance: and, (3) the stock market prices incidents. Using over 80,000 incident news items, firm information and stock returns for 2,848 unique U.S. public firms starting January 2007 and a smaller sample for European firms starting January 2009, all through December 2017, he finds that: Keep Reading

Remaking Value Investing

Value investing performance over the past two decades is poor. Is this underperformance a temporary consequence of an unusual macro environment, or a reflection of permanent economic/equity market changes. In their February 2021 paper entitled “Value Investing: Requiem, Rebirth or Reincarnation?”, Bradford Cornell and Aswath Damodaran survey the history and alternative approaches to value investing, with focus on its failure in recent decades. They then discuss how value investing must adapt to recover. Based on the body of value investing research through 2020, they conclude that: Keep Reading

Valuation-based Stock Market Return Expectations

What performance should investors expect from the S&P 500 Index based on price-to-earnings (P/E) and Cyclically-Adjusted Price-to-Earnings (CAPE, or P/E10)? In their November 2020 paper entitled “Extreme Valuations and Future Returns of the S&P 500”, Shaun Rowles and Andrew Mitchell take a layered “regression upon a regression” approach to predict S&P 500 Index returns and level. First, to estimate future returns, they run a linear regression on P/E, P/E10, S&P 500 dividend yield, inflation, 10-year U.S. Treasury note yield, historical 1-year, 3-year, 5-year and 10-year S&P 500 Index returns and percentiles of many of these variables within their respective historical distributions. Then, they run separate linear regressions to predict 1-year, 3-year, 5-year and 10-year future annualized returns. Finally, they run a linear regression to model current S&P 500 Index level for comparison to actual current level. Using Robert Shiller’s U.S. stock market and economic data spanning January 1871 through June 2020, they find that: Keep Reading

Intangible Value Factor

Intangible assets derive largely from investments in employees, brand and knowledge that are expensed rather than booked. Despite large and growing importance of intangible assets, traditional measures of firm value ignore them. Are firm value assessments therefore defective? In their October 2020 paper entitled “Intangible Value”, Andrea Eisfeldt, Edward Kim and Dimitris Papanikolaou evaluate a value factor that includes intangible assets in book equity for each firm (HMLINT) following exactly the methodology used to construct the widely accepted Fama-French value factor (HMLFF). They measure intangible assets based on flows of Selling, General, and Administrative (SG&A) expenses. Using firm accounting data and associated monthly stock returns and Fama-French 5-factor model data for a broad sample of U.S. stocks during January 1975 through December 2018, they find that:

Keep Reading

Login
Daily Email Updates
Filter Research
  • Research Categories (select one or more)