Fundamental Valuation

Do long lags between end of firm quarterly and annual financial reporting periods and issuance of SEC-required financial reports (10-Q and 10-K) indicate internal firm inefficiencies and/or reluctance to disclose adverse performance? In their August 2019 paper entitled “Filing, Fast and Slow: Reporting Lag and Stock Returns”, Karim Bannouh, Derek Geng and Bas Peeters study the impact of reporting lag (number of days between the end of reporting period and filing date of the corresponding report) on future stock returns. They focus on firms with market capitalizations greater than $750 million that have deadlines of 40 days after quarter end for quarterly reports and 60 days after year end for annual reports (accelerated filers). They each month:

1. Sort stocks into fifths, or quintiles, based on reporting lag separately for the most recent 10-K and the most recent 10-Q filings.
2. Reform a portfolio that is long (short) the equal-weighted quintile with the shortest (longest) lags.

They measure risk-adjusted portfolio performance via monthly gross 1-factor (market), 3-factor (plus size and book-to-market) and 4-factor (plus momentum) alphas. Using 10-K and 10-Q filings from the SEC and monthly characteristics and stock returns for a broad (but groomed) sample of U.S. accelerated filers (roughly 1,500 stocks), and a comparable sample of European stocks, during 2007 through 2018 period, they find that:

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Can investors effectively use firm characteristics to screen European stocks? In their August 2019 paper entitled “Predictability and the Cross-Section of Expected Returns: Evidence from the European Stock Market”, Wolfgang Drobetz, Rebekka Haller, Christian Jasperneite and Tizian Otto examine the power of 22 firm characteristics to predict stock returns individually and jointly. They assume market-based characteristics are available immediately and accounting-based characteristics are available four months after firm fiscal year end. For multi-characteristic predictions, they consider 5-characteristic, 8-characteristic and 22-characteristic models. For regression-based forecasts, they use either 10-year rolling or inception-to-date monthly inputs. For economic tests, they form equal-weighted or value-weighted portfolios that are each month long (short) the tenth, or decile, of stocks with the the highest (lowest) expected next-month returns based on 22-characteristic regression outputs. To estimate net performance, they apply one-way trading frictions of 0.57%. Using groomed monthly data for all firms in the STOXX Europe 600 index during January 2003 through December 2018, they find that:

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How does the market react when firms announce adoption of blockchain technology? In the May 2019 draft of their paper entitled “Bitcoin Speculation or Value Creation? Corporate Blockchain Investments and Stock Market Reactions”, Don Autore, Nicholas Clarke and Danling Jiang study stock price reactions to initial public announcements of investments in blockchain technology by listed U.S. firms. Their key metric is buy-and-hold abnormal return (BHAR) relative to each of five benchmarks: (1) portfolios of stocks matched on size and book-to-market (BM); (2) portfolios of stocks matched on market beta; 3) a broad value-weighted market index; (4) iShares Global Financials ETF (IXG); and, (5) iShares Global Tech ETF (IXN). Their announcement event windows is five trading days before initial public announcement of an investment in blockchain technology (-5) to 65 trading days after (65). Using dates of initial public announcements of investments in blockchain technology and contemporaneous daily returns for 207 stocks listed on NYSE and NASDAQ during October 2008 through March 2018, they find that:

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Are stock quality strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider four 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.
• 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 focus on daily return statistics, along with cumulative returns and maximum drawdowns. We use three benchmarks according to fund descriptions: SPDR S&P 500 (SPY), Vanguard Russell 1000 Index Fund ETF (VONE) and iShares Russell 3000 ETF (IWV). Using daily returns for the four stock quality ETFs and benchmarks as available through most of July 2019, we find that: Keep Reading

How does the U.S. stock market earnings yield (inverse of price-to-earnings ratio, or E/P) interact with the U.S. inflation rate over the long run? Is any such interaction exploitable? To investigate, we employ the long run dataset of Robert Shiller. Using monthly data for the S&P Composite Stock Index, estimated aggregate trailing 12-month earnings and dividends for the stocks in this index, and estimated U.S. Consumer Price Index (CPI) during January 1871 through June 2019 (over 148 years), and estimated monthly yield on 1-year U.S. Treasury bills (T-bills) since January 1951, we find that:

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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. Operating under a 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? Using recent actual and forecasted earnings and dividend data from Standard & Poor’s, 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 to provide investment results that exceed broad U.S. Equity benchmark indices at equivalent levels of volatility.” 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 dividend-adjusted closes of AIEQ and SPY from AIEQ inception (October 18, 2017) through June 2019, we find that: Keep Reading

Does duration (relative arrival sequence) of firm cash flows explain many widely accepted equity factor returns? In their April 2019 paper entitled “Duration-Driven Returns”, Niels Gormsen and Eben Lazarus investigate whether firm cash flow duration explains value, profitability, investment, low risk, idiosyncratic volatility and payout factor returns. They measure cash flow duration monthly via multiple regressions that relate analyst long-term growth estimates for each firm to its profitability, investment, low risk (market beta), idiosyncratic volatility and payout. They then each month for U.S. and global stocks separately reform four value-weighted sub-portfolios:

1. Above-median NYSE market capitalization and top 30% of duration.
2. Above-median NYSE market capitalization and bottom 30% of duration.
3. Below-median NYSE market capitalization and top 30% of duration.
4. Below-median NYSE market capitalization and bottom 30% of duration.

They specify the duration factor as return to a portfolio that is each month long (short) the two equal-weighted long-duration (short-duration) sub-portfolios. As a robustness test, they separately analyze a sample of single-stock dividend futures (dividend strips, claims to dividends to be paid out during a given calendar year), which allow varying duration characteristics while keeping maturity of cash flows fixed. Using monthly data for a broad sample of U.S. stocks starting August 1963, monthly data for global stocks starting July 1990, and annual data for 150 single-stock dividend futures with up to 5-year maturity starting January 2010, all through December 2018, they find that: Keep Reading

Are variables determined in published papers to be statistically significant predictors of stock market returns really useful to investors? In their November 2018 paper entitled “On the Economic Value of Stock Market Return Predictors”, Scott Cederburg, Travis Johnson and Michael O’Doherty assess whether strength of in-sample statistical evidence for 25 stock market predictors published in top finance journals translates to economic value after accounting for some realistic features of returns and investors. Predictive variables include valuation ratios, volatility, variance risk premium, tail risk, inflation, interest rates, interest rate spreads, economic variables, average correlation, short interest and commodity prices. Their typical investor makes mean-variance optimal allocations between the stock market and a risk-free security (yielding a fixed 2% per year) via Bayesian inference based on a vector autoregression model of market return-predictor dynamics. The investor has moderate risk aversion and a 1-month or longer investment horizon (reallocates monthly). Stock market returns and predictors exhibit randomly varying volatility. They focus on annual certainty equivalent return (CER) gain, which incorporates investor risk aversion, to quantify economic value of market predictability. Using monthly U.S. stock market returns and data required to construct the 25 predictive variables as available (starting as early as January 1927 and as late as June 1996 across variables) through December 2017, they find that:

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How should investors assess whether the market is fairly valuing cryptocurrencies such as Bitcoin? In his March 2019 paper entitled “Bitcoin Spreads Like a Virus”, Timothy Peterson offers a way to value Bitcoin based on Metcalf’s Law (network economics) and  a Gompertz function (often used to describe biological activity). The former model estimates fair price based on number of active users, and the latter model estimates the growth rate of active users. Using findings from prior research plus daily Bitcoin price and active account data from coinmetrics.io and blockchain.info during July 2010 through February 2019, he finds that: Keep Reading