Fundamental Valuation

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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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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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

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

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

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

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 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 (HML^INT) following exactly the methodology used to construct the widely accepted Fama-French value factor (HML^FF). 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:

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“Evolution of Bitcoin as an Investment” suggests a shift toward acceptance of Bitcoin (BTC) as an investment asset, as do recent actions by some large investors. Grayscale Bitcoin Trust (GBTC) offers a way for investors to access BTC via a fund that manages BTC holdings. GBTC price generally carries a premium over its BTC holdings in consideration for this convenience (17% as of the end of 2020). Does variation in this premium indicate good times to buy and sell GBTC? To investigate, we use the ratio GBTC/BTC (with BTC divided by 1,000 because the prices greatly differ in scale) as an easy way to infer the premium. We then look at ways to exploit variation in the ratio to buy and sell GBTC. Because of the rapid evolution of Bitcoin, we limit analysis to recent data. Using daily closing prices of GBTC and BTC during 2019 through 2020, we find that: Keep Reading

How does the Cyclically Adjusted Price-to-Earnings ratio (CAPE, or P/E10) behave during the COVID-19 pandemic? What are its current implications? In the November 2020 revision of their paper entitled “CAPE and the COVID-19 Pandemic Effect”, Robert Shiller, Laurence Black and Farouk Jivraj examine behavior of CAPE during 2020 in the U.S., UK, Europe, Japan and China, highlighting the impact of the pandemic. They apply CAPE to generate current 2-year, 5-year and 10-year equity return forecasts based on full-sample regressions. They then extend the CAPE forecasting approach to forecast changes in excess real return of stocks over bonds (see the chart below) to explore why investors strongly prefer equities over bonds during the pandemic. Finally, they look at sector dynamics within each economy. Using Shiller data during January 1871 through September 2020, they find that: Keep Reading