Fundamental Valuation

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

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 November 2020, we find that:

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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

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, we find that: Keep Reading

Will equity funds “managed” by artificial intelligence (AI) outperform human investors? To investigate, we consider the performance of AI Powered Equity ETF (AIEQ), which “seeks long-term capital appreciation within risk constraints commensurate with broad market US equity indices.” Per the offeror, the EquBot model supporting AIEQ: “…leverages IBM’s Watson AI to conduct an objective, fundamental analysis of U.S.-listed common stocks and real estate investment trusts…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 125 companies with the greatest potential over the next twelve months for appreciation and their corresponding weights… 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 October 2020, 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 2020 and September 2020, respectively, we find that: Keep Reading

Do stocks added to major indexes, such as the S&P 500 Index, exhibit exceptionally strong subsequent returns? In their July 2020 paper entitled “Does Joining the S&P 500 Index Hurt Firms?”, Benjamin Bennett, René Stulz and Zexi Wang investigate effects on firms/stocks of joining the S&P 500 Index and whether these effects change over time. They estimate abnormal stock performance using both market-adjusted returns and alphas from 3-factor (market, size, book-to-market), 4-factor (adding momentum) or 5-factor (adding profitability and investment instead of momentum) models of stock returns. Using monthly and daily fundamentals and price data for 659  firms/stocks added to the S&P 500 Index during 1997 through 2017, they find that:

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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.
• 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.
• 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.
• 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.
• 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.

Because some available sample periods are very short, we use daily return statistics, including compound annual growth rate (CAGR) and maximum drawdown (MaxDD). We use four benchmarks according to fund descriptions: SPDR S&P 500 (SPY), iShares MSCI ACWI ex U.S. ETF (ACWX), Vanguard Russell 1000 Index Fund ETF (VONE) and iShares Russell 3000 ETF (IWV). Using daily returns for the stock quality ETFs and benchmarks as available through June 2020, we find that:

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A subscriber, citing the weekly record of S&P 500 earnings growth estimates in the “FactSet Earnings Insight” historical series, wondered whether estimate trends/revisions are exploitable. To investigate, we collect S&P 500 quarterly year-over-year earnings growth estimates as recorded in this series. These data are bottom-up (firm by firm) aggregates, whether purely from analyst estimates (before any actual earnings releases), or a blend of actual earnings and estimates (during the relevant earnings season). Using these data and contemporaneous weekly levels of the S&P 500 Index during April 2011 through June 2020, we find that: Keep Reading