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Do good firm environmental, social and governance (ESG) ratings signal attractive stock returns? If so, what is the best way to exploit the signals? In their February 2023 paper entitled “Quantifying the Returns of ESG Investing: An Empirical Analysis with Six ESG Metrics”, Florian Berg, Andrew Lo, Roberto Rigobon, Manish Singh and Ruixun Zhang test performance of long-short ESG portfolios of U.S., European and Japanese stocks based on proprietary ESG scores from six major rating sources. They consider ESG scores from individual sources and apply several statistical and voting-based methods to aggregate ESG ratings across sources, including: simple average, Mahalanobis distance, principal component analysis, average voting and singular transferable voting. They consider equal-weighted and ESG score-weighted portfolios. They consider different percentile thresholds for long and short holdings. They assess ESG portfolio alpha with respect to widely used 1-factor (market), 3-factor (plus size and value) and 5-factor (plus investment and profitability) models of stock returns. They further test long-short portfolios from aggregations of E, S and G scores separately across sources. Using proprietary ESG ratings, monthly returns of associated stocks and monthly factor model returns during 2014 through 2020, they find that:

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Defined outcome Exchange-Traded Funds (ETF) use complex options strategies that buffer against loss but cap gain to generate a defined outcome for investors over a predefined period. Are they attractive? In their February 2023 paper entitled “The Dynamics of Defined Outcome Exchange Traded Funds”, Luis García-Feijóo and Brian Silverstein analyze average performance of the Innovator Defined Outcome ETF Buffer Series from 2019 through 2021. They also model the performance of the underlying strategy and simulate average outcome during January 2013 through August 2022. They consider three benchmarks: SPDR S&P 500 ETF Trust (SPY); 50% allocation to SPY and 50% allocation to iShares Core US Aggregate Bond ETF (AGG); and, iShares MSCI USA Min Vol Factor ETF (USMV). Using actual and simulated returns for the selected defined outcome ETFs/benchmarks as described, they find that:

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Can machine learning perfect equity sector rotation? In the January 2023 version of their paper entitled “Deep Sector Rotation Swing Trading”, flagged by a subscriber, Joel Bock and Akhilesh Maewal present a sector rotation strategy guided by multiple-input, multiple output deep learning model. The strategy chooses weekly from among 11 U.S. sectors using exchange-traded fund (ETF) proxies. Specifically, each week during each year, they:

• Train the machine learning model on the last two years of weekly (Friday close) historical sector ETF prices and volumes and sometimes auxiliary economic data (10-year U.S. Treasury yield, USD currency index, crude oil proxy and stock market volatility) to predict next-week opening and closing prices for each ETF.
• Compare the predicted return estimate for each ETF to a dynamically updated threshold return to screen for potential buys.
• Apply additional filters to screen out potential buys with unusual past losses to accommodate investor loss aversion.
• At the next-week open, allocate available capital to surviving sector ETFs based on respective past win rate (profitable trade) and respective past sector trade momentum.
• Liquidate all positions just prior to the next-week close.

Their benchmark is buying and holding the S&P 500 Index with reinvested dividends. Using weekly inputs as described during January 2012 through December 2022, they find that:

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What happens if we extend the “Simple Asset Class ETF Value Strategy” (SACEVS) with a real estate risk premium, derived from the yield on equity Real Estate Investment Trusts (REIT), represented by the FTSE NAREIT Equity REITs Index? To investigate, we apply the SACEVS methodology to the following asset class exchange-traded funds (ETF), plus cash:

3-month Treasury bills (Cash)
iShares 20+ Year Treasury Bond (TLT)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR Dow Jones REIT (RWR) through September 2004 dovetailed with Vanguard REIT ETF (VNQ) thereafter
SPDR S&P 500 (SPY)

This set of ETFs relates to four risk premiums, as specified below: (1) term; (2) credit (default); (3) real estate; and, (4) equity. We focus on effects of adding the real estate risk premium on gross compound annual growth rates (CAGR), maximum drawdowns (MaxDD) and annual Sharpe ratios of the Best Value (picking the most undervalued premium) and Weighted (weighting all undervalued premiums according to degree of undervaluation) versions of SACEVS. Using lagged quarterly S&P 500 earnings, monthly S&P 500 Index levels and monthly yields for 3-month U.S. Treasury bill (T-bill), the 10-year Constant Maturity U.S. Treasury note (T-note), Moody’s Seasoned Baa Corporate Bonds and FTSE NAREIT Equity REITs Index since March 1989 (limited by availability of earnings data), and monthly dividend-adjusted closing prices for the above asset class ETFs since July 2002, all through February 2023, we find that: Keep Reading

Is there a way to restore/enhance the relevance to investors of the Fed model, which is based on a putative investor-driven positive relationship between stock market earnings yield (equity earnings-to-price ratio) and U.S. Treasury bond (10-year) yield? In his February 2023 paper entitled “The Fed Model: Is it Still With Us?”, David McMillan re-examines the predictive power of this relationship with the addition of regime shifts that may expose predictive power not persistent across the full sample. He considers three versions of the Fed model:

1. Fed1 – ratio of earnings yield to bond yield (yield ratio).
2. Fed2 – simple difference between earnings yield and bond yield (yield gap).
3. Fed3 – logarithmic version of Fed2 (log yield gap).

He tests the power of each model variation to predict stock market returns at horizons of 1, 3 and 12 months, either including or excluding earnings yield and the interest rate term structure (U.S. Treasury 10-year yield minus 3-month yield) as control variables. He considers two ways to detect regime shifts in each model variation: (1) regressing each series on a constant term and looking for a break in its value; and, (2) a Markov-switching approach. Using monthly S&P Composite index level and earnings, and 10-year and 3-month U.S. Treasury yields during January 1959 through December 2021, he finds that:

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A subscriber requested measurement of a “premium” associated with U.S. stocks relative to those of other developed markets by looking at the difference in returns between the following two exchange-traded funds (ETF):

• SPDR S&P 500 (SPY)
• iShares MSCI EAFE Index Fund (EFA)

Using monthly dividend-adjusted closing prices for these ETFs during August 2001 (limited by EFA) through January 2023, we find that: Keep Reading

How do exchange-traded-funds (ETF) focused on development of robotics-artificial intelligence (AI), an arguably hot area of technology, perform? To investigate, we consider five of the largest such ETFs, all currently available, as follows:

• • Robo Global Robotics and Automation Index (ROBO)
  • ARK Autonomous Technology & Robotics (ARKQ)
  • Global X Robotics & Artificial Intelligence (BOTZ)
  • First Trust Nasdaq Artificial Intelligence and Robotics (ROBT)
  • iShares Robotics and Artificial Intelligence Multisector (IRBO)

We use Invesco QQQ Trust (QQQ) as a benchmark, assuming investors look at robotics-AI stocks as a way to beat other technology stocks. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly total returns for the five robotics-AI ETFs and QQQ as available through January 2023, we find that: Keep Reading

Subscribers asked whether substituting Invesco QQQ Trust (QQQ) for SPDR S&P 500 (SPY) in the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS) improves outcomes. To investigate, we substitute monthly QQQ dividend-adjusted returns for SPY dividend-adjusted returns in the two model strategies. We then compare the modified performance with the original baseline performance, including: gross compound annual growth rates (CAGR) at various horizons, average gross annual returns, standard deviations of gross annual returns, gross annual Sharpe ratios and maximum drawdowns (MaxDD) based only monthly measurements. 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 the specified methodology and data to generate SACEVS monthly returns starting August 2002 and SACEMS monthly returns starting July 2006, all through January 2023, we find that:

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How can researchers assess the true value and robustness of new stock return anomalies (predictors) in consideration for addition to the factor zoo? In their January 2023 paper entitled “Assaying Anomalies”, Robert Novy-Marx and Mihail Velikov present a protocol/tool set for dissecting and understanding newly proposed cross-sectional stock return predictors. The tools address the most important issues involved in testing asset pricing strategies, including some machine learning techniques. They pay particular attention to implementation costs that prevent exploitation of predictors with good gross returns (as with high turnover and/or overweighting small stocks). The tool set, including automated report generator, is available as a free web application and a public github repository. Key aspects of reports generated by this tool set are:

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Does aggregate insider stock buying and selling offer clues about future stock market returns? In their January 2023 paper entitled “Aggregate Insider Trading in the S&P 500 and the Predictability of International Equity Premia”, Andre Guettler, Patrick Hable, Patrick Launhardt and Felix Miebs investigate relationships between net aggregate insider trading and future stock market excess returns at horizons from one month to one year. They define net aggregate insider trading as unscheduled open market insider purchases minus sales, divided by purchases plus sales. They focus on S&P 500 firm insider trading and S&P 500 Index excess returns (relative to the U.S. Treasury bill yield). They also consider U.S. non-S&P 500 insider trading. They further look at insider trading and stock market excess returns within Canada, France, Germany, Great Britain and Italy. Using monthly aggregations of the specified insider trading data from 2iQ and monthly stock market index returns during January 2004 through December 2018, they find that:

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