Equity Premium

How should investors think about research using long-run financial data? In their October 2024 paper entitled “Long-Run Asset Returns”, David Chambers, Elroy Dimson, Antti Ilmanen and Paul Rintamäki survey the body of evidence on historical return premiums for stocks, bonds, real estate and commodities over the current and previous two centuries. They discuss benefits and pitfalls of long-run datasets and make suggestions on best practices. They also compare premium estimates from alternative data compilers. Based on the body of long-run asset class return research, they conclude that:

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Are the human choices in studies that apply machine learning models to forecast stock returns critical to findings? In other words, is there a confounding machine learning design choices zoo? In their November 2024 paper entitled “Design Choices, Machine Learning, and the Cross-section of Stock Returns”, Minghui Chen, Matthias Hanauer and Tobias Kalsbach analyze effects of varying seven key machine learning design choices: (1) machine learning model used, (2) target variable/evaluation metric, (3) target variable transformation (continuous or discrete dummy), (4) whether to use anomaly inputs from pre-publication subperiods or not, (5) whether to compress correlated features, (6) whether to sue a rolling or expanding training window and (7) whether to include micro stocks in the training sample. They examine all possible combinations of these choices, resulting in 1,056 machine learning models. For each machine learning model each month, they:

1. Rank stocks on each of 207 potential return predictors and map rankings into [-1, 1] intervals. In case of missing inputs, they set the ranking value to 0.
2. Apply rankings to predict a next-month target variable (return in excess of the risk-free rate, market-adjusted return or 1-factor model risk-adjusted return) for each stock with market capitalization above a 20% NYSE threshold during January 1987 through December 2021.
3. Reform a hedge portfolio that is long (short) the value-weighted tenth, or decile, of stocks with the highest (lowest) predicted target variable and compute next-month portfolio return.

Using monthly data as available for all listed U.S. common stocks during January 1957 through December 2021, they find that: Keep Reading

How do exchange-traded-funds (ETF) that employ artificial intelligence (AI) to pick assets perform? To investigate, we consider ten such ETFs, eight of which are currently available:

• • Amplify AI Powered Equity ETF (AIEQ) – picks U.S. stocks based on a quantitative model that runs on the IBM Watson platform.
  • QRAFT AI-Enhanced U.S. Large Cap ETF (QRFT) – picks large capitalization U.S. stocks via an investment strategy enhanced by the use of AI.
  • QRAFT AI-Enhanced U.S. Large Cap Momentum ETF (AMOM) – picks momentum stocks via an investment strategy enhanced by the use of AI.
  • VanEck Social Sentiment ETF (BUZZ) – picks U.S. stocks with the most positive sentiment as determined from online sources via a rules-based quantitative methodology.
  • WisdomTree International Al Enhanced Value Fund (AIVI) – picks developed market value stocks outside the U.S. and Canada via a quantitative AI model. [We use data only since retention of an AI-oriented advisor at the beginning of 2022.]
  • WisdomTree U.S. Al Enhanced Value Fund (AIVL) – picks U.S. value stocks via a quantitative AI model. [We use data only since retention of an AI-oriented advisor at the beginning of 2022.]
  • Teucrium Aila Long-Short Agriculture Strategy ETF (OAIA) – takes long and short positions in agricultural securities via a strategy employing machine learning. [Dead as of August 2024.]
  • Teucrium Aila Long-Short Base Metals Strategy ETF (OAIB) – takes long and short positions in base metals securities via a strategy employing machine learning. [Dead as of May 2024.]
  • LG QRAFT AI-Powered U.S. Large Cap Core ETF (LQAI) – – picks large capitalization U.S. stocks via an investment strategy enhanced by the use of AI.
  • Stocksnips AI-Powered Sentiment US ALL Cap ETF (NEWZ) – picks U.S. stocks by leveraging AI to generate News Media Sentiment Signals.

We use SPDR S&P 500 ETF Trust (SPY) for comparison, though it is not conceptually matched to some of the ETFs. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly total returns for the ten AI-powered ETFs and SPY as available through October 2024, we find that: Keep Reading

Is U.S. equity market valuation outrunning its productive value? For perspective, we compare the trajectories of S&P 500 (SP500) index, earnings and dividends over recent decades and look at some potential explanations for divergences. Using quarterly SP500 data and 10-year U.S. Treasury note (T-note) yield during March 1988 through September 2024 and Shiller data as available through October 2024, we find that: Keep Reading

Can investors safely use leverage to squeeze incremental return from low-volatility/factor-tilted stocks, thereby avoiding underperformance of these stocks during bull markets? In their October 2024 paper entitled “Low-Risk Alpha Without Low Beta”, David Blitz, Clint Howard, Danny Huang and Maarten Jansen exploit the low-volatility anomaly by leveraging multifactor, low-risk, global stock portfolios to a beta of 1.0 while controlling tracking error relative to a capitalization-weighted benchmark. Their portfolio formation rules are:

• The portfolio is long only and fully invested in liquid (large-capitalization) stocks.
• Maximum individual stock weight is the lower of 1.5% or 20 times its benchmark weight.
• Exposure to countries, regions and sectors may deviate at most 10% from benchmark weights.
• Portfolio beta (portfolio-weighted sum of historical stock betas for the last 156 weekly returns) must be less than 0.8 relative to the benchmark.
• Portfolio optimization involves trading off expected returns, benchmark tracking error and turnover. Expected stock returns derive from a multifactor score with 50% for low-risk (equal-weighted combination of past 260-day volatility, 156-week volatility, 260-day beta and 156-week beta), 16.67% for value (net payout yield), 16.67% for quality (gross profits to assets) and 16.67% for momentum (return from 12 months ago to one month ago).
• Use synthetic positions (for example, via equity options) to achieve leverage, with no cash collateral and financing costs equal to the risk-free rate.
• Rebalance at the end of each month but ignore slight deviations from target weights.

They separately discuss impacts of portfolio rebalancing frictions and additional leverage costs/penalties. They focus on developed markets but also look at an emerging markets sample and North American, European and Asia Pacific subsamples. Using daily and monthly data for developed market stocks since December 1985 and emerging market stocks since December 1995, all through December 2023, along with contemporaneous spreads and interest/Treasury bill rates, they find that: Keep Reading

A reader requested a test of the following hypothesis from the article “Gold’s Bluff – Is a 30 Percent Drop Next?” [no longer available]: “Ironically, gold is more than just a hedge against market turmoil. Gold is actually one of the most accurate indicators of the stock market’s long-term direction. The Dow Jones measured in gold is a forward looking indicator.” To test this assertion, we examine relationships between the spot price of gold and the level of the Dow Jones Industrial Average (DJIA). Using monthly data for the spot price of gold in dollars per ounce and DJIA over the period January 1971 through October 2024, we find that: Keep Reading

Do target retirement date funds, offering glidepaths that shift asset allocations away from equities and toward bonds as target dates approach, safely generate attractive returns? To investigate, we consider seven such mutual funds offered by Vanguard, as follows:

• • Vanguard Target Retirement 2020 Fund (VTWNX)
  • Vanguard Target Retirement 2025 Fund (VTTVX)
  • Vanguard Target Retirement 2030 Fund (VTHRX)
  • Vanguard Target Retirement 2035 Fund (VTTHX)
  • Vanguard Target Retirement 2040 Fund (VFORX)
  • Vanguard Target Retirement 2045 Fund (VTIVX)
  • Vanguard Target Retirement 2050 Fund (VFIFX)

We consider as benchmarks SPDR S&P 500 ETF Trust (SPY), iShares iBoxx $ Investment Grade Corporate Bond ETF (LQD) and both 80-20 and 60-40 monthly rebalanced SPY-LQD combinations. We look at monthly and annual return statistics, including compound annual growth rate (CAGR) and maximum drawdown (MaxDD). Using monthly total returns for SPY, LQD, three target retirement date funds since October 2003 and four target retirement date funds since June 2006 (limited by Vanguard inception dates), all through September 2024, we find that:

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How do exchange-traded-funds (ETF) focused on supplying renewable energy perform? To investigate, we consider nine of the largest renewable energy ETFs, all currently available, as follows:

• • iShares Global Clean Energy (ICLN)
  • Invesco Solar (TAN)
  • First Trust NASDAQ Clean Edge Green Energy Index (QCLN)
  • Invesco WilderHill Clean Energy (PBW)
  • ALPS Clean Energy (ACES)
  • Invesco Global Clean Energy (PBD)
  • SPDR S&P Kensho Clean Power (CNRG)
  • First Trust Global Wind Energy (FAN)
  • Global X Renewable Energy Producers (RNRG)

We use SPDR S&P 500 (SPY) as a benchmark, assuming investors look at renewable energy stocks to beat the market and not to beat the energy sector. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the nine renewable energy ETFs and SPY as available through September 2024, we find that: Keep Reading

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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