Fundamental Valuation

A subscriber asked about current evidence that high-dividend stocks outperform the market. To investigate, we compare performances of 10  exchange-traded funds (ETFs) holding high-dividend stocks to that of SPDR S&P 500 (SPY) as a proxy for the U.S. stock market. The  high-dividend stock ETFs, from oldest to newest, are:

• iShares Select Dividend ETF (DVY)
• Invesco Dividend Achievers ETF (PFM)
• SPDR S&P Dividend ETF (SDY)
• Vanguard Dividend Appreciation Index Fund (VIG)
• Vanguard High Dividend Yield ETF (VYM)
• WisdomTree Dividend ex-Financials ETF (DTN), dead since January 2022
• iShares Core High Dividend ETF (HDV)
• Schwab U.S. Dividend Equity ETF (SCHD)
• ProShares S&P 500 Dividend Aristocrats ETF (NOBL)
• iShares Core Dividend Growth ETF (DGRO)

For each of these ETFs, we compare average monthly total (dividend-reinvested) return, standard deviation of 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. Using monthly total returns for the 10 high-dividend stock ETFs and SPY over available sample periods through September 2023, we 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 September 2023, we find that: Keep Reading

A subscriber asked how Apple Inc. (AAPL) stock behaves around unveiling of new iPhone models. To investigate, we identify 19 distinct iPhone series release dates from 6/29/07 through 9/16/22 and calculate average daily cumulative returns for AAPL from 21 trading days before release date (Day 0) through 63 trading days after release date. Two pairs of iPhone release dates overlap somewhat for this specification. As a benchmark, we calculate average daily cumulative returns for AAPL during this interval for all trading days. In case there is some confounding factor (seasonal?), we repeat these calculations for Invesco QQQ Trust (QQQ). Using the selected iPhone series release dates and daily dividend/split-adjusted prices for AAPL and QQQ from the end of May 2007 through mid-December 2022, we find that: Keep Reading

Do stock prices confirm that firms with high market power maintain profitability during times of high inflation because they can raise prices, while those with low market power cannot? In their August 2023 paper entitled “Stagflationary Stock Returns and the Role of Market Power”, Benjamin Knox and Yannick Timmer study effects of inflation news on stocks of firms ranked by market power. They define:

• Inflation news as the difference between total consumer price index (CPI) releases and the median inflation forecast from Bloomberg back to 1997, and before that from Haver Analytics back to 1977.
• Market power as firm ability to set its price above marginal costs (markup), estimated as sales over cost of goods sold multiplied by the output elasticity of inputs (from a production function estimate).

They decompose stock returns into risk premium, risk-free rate and cash flow news components. They designate firms above the 75th (below the 25th) percentile of market power as high-market power (low-market power) firms to assess stock price responses to inflation news. Using total CPI releases, associated median inflation forecasts, accounting data for a broad sample of U.S. common stocks and daily returns for both individual stocks and the broad U.S. stock market during 1977 through 2022, they find that: Keep Reading

During 1989 through 2019, the S&P 500 Index generated 5.5% real annual return, compared to just 2.5% annual real growth in U.S. gross domestic product (GDP). How can this disconnect happen? Can it continue? In the June 2023 version of his paper entitled “End of an Era: The Coming Long-Run Slowdown in Corporate Profit Growth and Stock Returns”, Michael Smolyansky examines interactions between U.S. stock market performance and declines in interest rates and corporate tax rates over the last three decades. He focuses on S&P 500 non-financial stocks adjusted for index additions/deletions and for changes in firm shares outstanding, allowing computation of per share metrics. He decomposes stock returns into: (1) change in price-earnings ratio (P/E);  (2) change in earnings before interest and taxes (EBIT); (3) change in interest expenses; and, (4) change in effective corporate tax rate. Using the specified annual data during 1962 through 2019, he finds that: Keep Reading

How do recent advances in Generative Artificial Intelligence (AI), as epitomized by ChatGPT, impact firm valuations? In their May 2023 paper entitled “Generative AI and Firm Values”, Andrea Eisfeldt, Gregor Schubert and Miao Ben Zhang quantify workforce exposures to AI for publicly traded U.S. companies and translate those exposures into firm valuation effects. Specifically, they:

1. Measure job task AI exposures by asking ChatGPT to assess whether each of 19,265 occupational tasks could be done by the current ChatGPT or an enhanced future ChatGPT.
2. Measure occupational AI exposures by aggregating task-level AI exposures to occupations per the O*NET database.
3. Measure firm AI exposures by mapping occupations to publicly-traded firms based on millions of public employee profiles such as those in LinkedIn using data from Revelio Labs. They validate this measure based on mentions of AI in 2023 company earnings announcement call transcripts.
4. Quantify effects of AI exposure on firm valuations by examining how stocks of firms with varying exposures to AI react to the release of ChatGPT on November 15, 2022.

Using occupational task descriptions, firm employee job descriptions and returns of associated stocks from ChatGPT release through March 31, 2023, they find that:

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A subscriber asked about boosting the performance of the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS), and thereby the Combined Value-Momentum Strategy (SACEVS-SACEMS), by substituting ProShares Ultra S&P500 (SSO) for SPDR S&P 500 ETF Trust (SPY) in these strategies whenever:

1. SPY is above its 200-day simple moving average (SMA200); and,
2. The CBOE Volatility Index (VIX) SMA200 is below 18.

Substitution of SSO for SPY applies to portfolio holdings, but not SACEMS asset ranking calculations. To investigate, we test all versions of SACEVS, SACEMS and monthly rebalanced 50% SACEVS-50% SACEMS (50-50) combinations. We limit SPY SMA200 and VIX SMA200 conditions to month ends as signals for next-month actions (no intra-month changes). We consider baseline SACEVS and SACEMS (holding SPY as indicated) and versions of SACEVS and SACEMS that always hold SSO instead of SPY as benchmarks. We look at average gross monthly return, standard deviation of monthly returns, monthly gross reward/risk (average monthly return divided by standard deviation), gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and gross annual Sharpe ratio as key performance metrics. In Sharpe ratio calculations, we employ the average monthly yield on 3-month U.S. Treasury bills during a year as the risk-free rate for that year. Using daily unadjusted SPY and VIX values for SMA200 calculations since early September 2005 and monthly total returns for SSO since inception in June 2006 to modify SACEVS and SACEMS inputs, all through February 2023, we find that: Keep Reading

Do factor investing (smart beta) mutual funds capture for investors the premiums found in academic factor research? In their November 2022 paper entitled “Factor Investing Funds: Replicability of Academic Factors and After-Cost Performance”, Martijn Cremers, Yuekun Liu and Timothy Riley analyze the performance of funds seeking to capture of published (long-side) factor premiums. They group factor investing funds into four styles: dividend, volatility, momentum and q-factor (profitability and investment). They separately measure how closely fund holdings adhere to the long sides of academic factor specifications. They measure fund outperformance (alpha) relative to the market factor via the Capital Asset Pricing Model (CAPM) and via a multi-factor model (CPZ6) that accounts for the market factor and for granular size/value interactions. Using monthly returns for 233 hand-selected factor investing mutual funds and for the academic research factors during January 2006 (16 funds available) through September 2020 (207 funds available), they find that:

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Can investors make the stock return momentum effect stronger/more reliable by isolating stocks for which many similar stocks exhibit very strong or very weak past returns? In his December 2022 paper entitled “Neighbouring Assets”, Sina Seyfi explores this question by sorting stocks based on average past returns of other stocks with the most similar sets of 94 characteristics (neighbor stocks). He measures similarity between two stocks as the aggregate distance of their normalized and winsorized (excluding top and bottom 1% of values) characteristics over a baseline rolling 10-year history. His baseline “neighborhood” is 1,000 stocks. His baseline past return metric is average monthly value-weighted return of neighbor stocks over the past year. He considers three stock universes, consisting of all NYSE/AMEX/NASDAQ stocks: (1) excluding the 5% with the smallest market capitalizations; (2) excluding those below the 20% breakpoint of NYSE market capitalizations; and, (3) excluding those below the median of NYSE market capitalizations. He each month sorts stocks into tenths (deciles) of average past return of neighborhood stocks and reforms a value-weighted portfolio that is long (short) those in the decile with the highest (lowest) neighbor-stock average past return. Using monthly characteristics and returns for the specified stocks during January 1970 (with portfolio formation commencing January 1980) through December 2021, he finds that: Keep Reading

Do stocks with high exposures to new technologies outperform? In her December 2022 paper entitled “New Technologies and Stock Returns”, Jinyoung Kim examines future returns of stocks with relatively high exposures to new technologies as measured via patent analysis. Each June, she applies machine learning to both textual and citation information to detect technology areas with high growth in new patents and identifies new technologies based on the invention descriptions. For each U.S. public company, she then estimates the intensity of firm exposure to the last three years of new technologies. Excluding firms with zero exposure, she relates new technologies exposure to stock returns by each June reforming a portfolio that is long (short) stocks with the lowest (highest) 30% of new technologies exposures. Using information for all publicized U.S. patents and patent applications since 1976 (plus information for related pre-1976 granted U.S. patents and published international patents) and monthly returns for associated stocks and widely accepted stock factors during July 1981 through June 2019, she finds that: Keep Reading