Equity Premium

Can investors reasonably anticipate the signals (stock rankings) for stock anomalies that are based on firm financial information. In their August 2024 paper entitled “Predicting Anomalies”, Boone Bowles, Adam Reed, Matthew Ringgenberg and Jake Thornock investigate whether: (1) stock returns follow predictable patterns before availability of anomaly trading signals; and, (2) anomaly trading signals are themselves predictable. They focus on a set 28 published anomalies that are entirely based on publicly available information in quarterly financial statements. They each quarter for each anomaly reform a hedge portfolio that is long (short) the tenth of stocks with the highest (lowest) expected returns. They consider four models to predict stock rankings for each anomaly: (1) a first-order autoregression that projects strength of signals; (2) a first-order autoregression that projects stock rankings; (3) a machine learning model that uses past anomaly signals and rankings; and, (4) a (martingale) model that assumes anomaly portfolio rankings for next quarter will be the same as current rankings. Using as-published specifications for each of the 28 anomalies plus daily returns and quarterly/annual financial reports for a broad sample of U.S. stocks during January 1990 through December 2019, they find that: Keep Reading

Can market efficiency be falling despite ubiquitous data, computing and networking? In his August 2024 paper entitled “The Less-Efficient Market Hypothesis”, Clifford Asness argues that markets have become less efficient in the relative pricing of common stocks over recent decades. To make his argument, he relies on the ratio of expensive stock valuations to cheap stock valuations (the value spread). He considers two versions of this spread, one based on the conventional price-to-book ratio to measure value and the other based on five industry-neutral value metrics. He discusses three potential reasons why the value spread is rising. He closes with advice for value investors. Reflecting on 35 years of research experience, he concludes that:

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Short-term reversal is a widely accepted stock return anomaly, with the long-only version glibly termed “buy the dip.” Is short-term reversal readily exploitable? As a live test, we look at the performance of Vesper U.S. Large Cap Short-Term Reversal Strategy ETF (UTRN). This fund seeks to capture bounces of stocks with recent sharp declines by each week:

• Calculating for the 500 largest U.S. stocks a metric similar to the Sharpe ratio but using an asymmetric volatility to find overreaction dips in downtrending stocks (the Chow ratio).
• Initially buying the 25 stocks with the lowest Chow ratios.
• Selling any holdings for which the Chow ratio has risen out of the bottom 50 and replacing them with bottom 25 stocks.

The restriction to large stocks and the differing buy and sell rules suppress trading frictions/portfolio turnover. The benchmark is SPDR S&P 500 ETF Trust (SPY). Using monthly dividend-adjusted returns for UTRN and SPY from the inception of the former in September 2018 through August 2024, we find that: Keep Reading

Why have stock return anomalies generally degraded over recent decades? In their August 2024 paper entitled “What Drives Anomaly Decay?”, Jonathan Brogaard, Huong Nguyen, Tālis Putniņš and Yuchen Zhang examine why stock return anomalies decay by:

• Decomposing returns into market-wide, public firm-specific and private firm-specific elements.
• Separating cash flow and discount rate effects within each of these three components.
• Accounting for noise.

This breakdown lets them determine whether changes in anomaly returns over time derive from anomaly publication, identifiable liquidity shocks (such as stock price decimalization) or a more general increase market efficiency. They apply this approach to daily returns of long-short (hedge) portfolios, reformed monthly, for 204 stock return anomalies from Open Source Asset Pricing. Using the required firm characteristics and daily prices for all NYSE/AMEX/NASDAQ common stocks during 1956 through 2021 (an average 4,029 firms per year and a total of 16,966 firms), they find that:

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How do returns of different asset classes recently interact with the Effective Federal Funds Rate (EFFR)? We focus on monthly changes (simple differences) in EFFR  and look at lead-lag relationships between change in EFFR and returns for each of the following 10 exchange-traded fund (ETF) asset class proxies:

• Equities:
  • SPDR S&P 500 (SPY)
  • iShares Russell 2000 Index (IWM)
  • iShares MSCI EAFE Index (EFA)
  • iShares MSCI Emerging Markets Index (EEM)
• Bonds:
  • iShares Barclays 20+ Year Treasury Bond (TLT)
  • iShares iBoxx $ Investment Grade Corporate Bond (LQD)
  • iShares JPMorgan Emerging Markets Bond Fund (EMB)
• Real assets:
  • Vanguard REIT ETF (VNQ)
  • SPDR Gold Shares (GLD)
  • Invesco DB Commodity Index Tracking (DBC)

Using end-of-month EFFR and dividend-adjusted prices for the 10 ETFs during December 2007 (limited by EMB) through August 2024, we find that: Keep Reading

Do changes in the Effective Federal Funds Rate (EFFR), the actual cost of short-term liquidity derived from a combination of market demand and Federal Reserve open market operations designed to maintain the Federal Funds Rate (FFR) target, predictably influence the U.S. stock market over horizons up to a few months? To investigate, we relate smoothed (volume-weighted median) monthly levels of EFFR to monthly U.S. stock market returns (S&P 500 Index or Russell 2000 Index) over available sample periods. Using monthly data as specified since July 1954 for EFFR and the S&P 500 Index (limited by EFFR) and since September 1987 for the Russell 2000 Index, all through August 2024, we find that: Keep Reading

Can machine learning exploit interactions between many equity factors and many potential factor return predictors to create an attractive factor timing strategy? In their August 2024 paper entitled “Optimal Factor Timing in a High-Dimensional Setting”, Robert Lehnherr, Manan Mehta and Stefan Nagel apply machine learning with mean-variance optimization to time equity factors when the numbers of factors and potential factor return predictors are large. They consider both a small set of four (size, book-to-market, profitability and investment) and a much larger set of 131 factors. They focus on a small set of 11 potential predictors (five economic variables and six specific to the small set of factors) but consider also a much larger set augmenting those 11 with many other factor specific variables. They simplify outputs by suppressing the weakest signals. Their machine learning process each year uses an expanding window of at least 20 years for training, one year for validation and one year for testing. They focus on Sharpe ratio as the essential performance metric. Their benchmarks are annually rebalanced: (1) equal weighting of factors; and, (2) straightforward mean-variance optimization of factors that ignores interactions between factors and potential predictors. To estimate net performance, they apply 0.1% portfolio reformation frictions at the portfolio of factors (not factor) level. Using monthly factor portfolio returns and economic variable values during January 1965 through December 2022, with 1986 the first year of portfolio testing, they find that: Keep Reading

How has the immediate price impact associated with a stock entering or leaving the S&P 500 evolved? In the March 2024 revision of their paper entitled “The Disappearing Index Effect”, Robin Greenwood and Marco Sammon revisit abnormal returns during the trading day after S&P 500 additions and deletions and investigate four potential drivers of findings. Using announcement dates, implementation dates and daily returns for 732 S&P 500 additions and 726 S&P 500 deletions, and holdings of large U.S. equity funds, during 1980 through 2020, they find that:

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Do stock markets following predictable long boom and bust periods? In the August 2024 draft of their paper entitled “The Anatomy of Lost Stock Market Decades”, Todd Feldman and Brian Yang examine the regularity/frequency of bull periods (strong gains) and lost periods (no gains) of at least 10 years. They also test two metrics to identify when the S&P 500 Index is in a bull or lost period: (1) the ratio of the S&P 500 Index level to a dividend discount model (DDM) valuation of the index; and, (2) an exponential cumulative loss metric calibrated via a 20-year moving average (weighting recent losses more than older losses to sharpen regime shift detection). Using monthly stock market levels from Global Financial Data for the U.S., Canada, Japan, Australia, Germany and France and Robert Schiller’s data for the S&P Composite Index from the 1800s through 2023, they find that:

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Are preferred stock strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider seven of the largest preferred stock ETFs, all currently available, in order of longest to shortest available histories:

• Invesco Financial Preferred (PGF) – generally invests at least 90% of assets in preferred securities of financial institutions.
• iShares Preferred and Income Securities (PFF) – tracks an index composed of U.S. dollar-denominated preferred and hybrid securities.
• Invesco Preferred (PGX) – tracks performance of fixed-rate preferred securities issued in the U.S.
• SPDR ICE Preferred Securities (PSK) – seeks exposure to non-convertible preferred securities with par $25 and aggregate minimum par value $250 million.
• VanEck Vectors Preferred Securities ex Financials (PFXF) – tracks performance of U.S. exchange-listed hybrid debt, preferred stock and convertible preferred stock issued by non-financial corporations.
• First Trust Preferred Securities and Income (FPE) – normally invests at least 80% of assets (including borrowings) in preferred securities and income-producing debt securities, including corporate bonds, high-yield securities and convertible securities.
• Global X U.S. Preferred (PFFD) – invests in a broad basket of U.S. preferred stocks.

We use a monthly rebalanced portfolio of 60% SPDR S&P 500 (SPY) and 40% iShares iBoxx $ Investment Grade Corporate Bond (LQD) (60-40) as a simple hybrid benchmark for all these funds except PGF, for which we use Financial Select Sector SPDR (XLF). We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the preferred stock ETFs and benchmarks as available through August 2024, we find that: Keep Reading