Equity Premium

How should global investors assess country sovereign bond and equity risks? In his July 2026 paper entitled “Country Risk: Determinants, Measures and Implications – The 2026 Edition”, Aswath Damodaran examines country risk from multiple perspectives. To estimate a country risk premium, he considers direct and indirect measures of country government bond risk and country equity risk. Based on a variety of sources and methods, he concludes that: Keep Reading

Do exchange-traded funds selecting stocks based on environmental, social, and governance characteristics (ESG ETF) typically offer attractive performance? To investigate, we compare performance statistics of eight ESG ETFs, all currently available, to those of simple and liquid benchmark ETFs, as follows:

1. iShares MSCI USA ESG Select ETF (SUSA), with SPDR S&P 500 ETF Trust (SPY) as a benchmark.
2. iShares MSCI KLD 400 Social ETF (DSI), with SPY as a benchmark.
3. iShares ESG MSCI EM ETF (ESGE), with iShares MSCI Emerging Markets ETF (EEM) as a benchmark.
4. iShares ESG Aware MSCI EAFE ETF (ESGD), with iShares MSCI EAFE ETF (EFA) as a benchmark
5. iShares ESG MSCI USA ETF (ESGU), with SPY as a benchmark.
6. Nuveen ESG Small-Cap ETF (NUSC), with iShares Russell 2000 ETF (IWM) as a benchmark.
7. Vanguard ESG U.S. Stock ETF (ESGV), with SPY as a benchmark.
8. Vanguard ESG International Stock ETF (VSGX), with Vanguard FTSE All-World ex-US Index Fund ETF (VEU) as a benchmark.

We focus on average return, standard deviation of returns, reward/risk (average return divided by standard deviation of returns), compound annual growth rate (CAGR) and maximum drawdown (MaxDD), all based on monthly data. Using monthly dividend-adjusted returns for all specified ETFs since inceptions and for all benchmarks over matched sample periods through June 2026, we find that: Keep Reading

What happens when investors largely ignore their stock holdings over intervals of one, five and ten years? In his June 2026 paper entitled “Returns to ‘Do-Nothing’ Portfolios”, Hendrik Bessembinder studies returns to “do-nothing” portfolios constructed from the stocks that comprise the S&P 500 index as of the end of each year since 1970. Do-nothing means buying the stocks at the end of a year and holding them for 1, 5 or 10 years. The only trades during the holding interval are reinvestment of dividends in the same stock and rolling of proceeds from delisted stocks into U.S. Treasury bills. He performs four tests, comparing performances of:

1. Initial equal-weighted to initial market capitalization-weighted (value-weighted) do-nothing portfolios of all S&P 500 stocks.
2. These do-nothing portfolios to that of the S&P 500 Index.
3. Do-nothing value-weighted portfolios of the 100, 50, 10 or 1 largest S&P 500 stock to that comprised of all index stocks.
4. Do-nothing value-weighted portfolios of 100, 50, 25, 5 or 1 randomly selected stock to that of the S&P 500 Index.

He ignores trading frictions and taxes. Using total returns and delisting events for the contemporaneous S&P 500 stocks from the end of 1970 through the end of 2025 (55 years), he finds that: Keep Reading

Under what conditions does equity market time series momentum (TSMOM) work and not work? In their June 2026 paper entitled “Boundaries of Time Series Momentum”, Matti Suominen and Erik Hjalmarsson examine performance of equity market TSMOM across ranges of three valuation metrics: cyclically adjusted price-to-earnings ratio (CAPE), dividend yield and term spread (difference between long-maturity and short-maturity Treasury instrument yields). They specify TSMOM as long (short) the market when past market return in excess of the risk-free rate over a specified lookback interval is positive (negative). They specify a Boundaries variable for predicting TSMOM performance as follows:

1. Scale each of the CAPE, dividend yield and term spread to values between -1 and 1 as follows:
   1. Subtract from its 12-month average the past 10-year or 20-year minimum observation and divide the difference by the past 10-year or 20-year range (maximum minus minimum).
   2. Multiply results by two and subtract one.
2. Compute a Boundaries variable as the square of the scaled term spread plus the square of scaled CAPE or scaled dividend yield.

For a given equity market, they construct a TSMOM index as an equal-weighted average of 25 time series momentum strategies, with lookback and investment intervals of 1, 3, 6, 9 or 12 months. They then explore how index returns interact with the Boundaries variable. Using the specified inputs and stock index returns during July 1927 through December 2024 for the U.S. and during January 1989 through December 2024 for a 20-country international sample, they find that: Keep Reading

Has the strong shift in investor flows from active funds to passive funds unexpectedly contributed to a decline in performance of the former? In her June 2026 paper entitled “Passive Flows, Active Woes: Passive Investing and the Decline of Active Mutual Fund Alpha”, Hannah Unterberg studies whether the secular shift from active to passive investing (see the chart below) has depressed active fund performance because outflows force liquidation of active fund holdings. Using holdings and returns for a broad sample of active and passive U.S. equity mutual funds and exchange-traded funds during January 1984 through December 2024, she finds that:

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Do U.S. industry portfolios perform predictably after similar U.S. equity market returns? In his May 2026 paper entitled “Industry Rotation Using Market-State Similarity”,  Valeriy Zakamulin studies whether current market returns exploitably predict subsequent industry returns. Specifically, he:

1. Examines general market-to-industry monthly return predictability.
2. Assesses monthly market-to-industry predictability across ranges of market returns by:
   • Standardizing market excess return using a 120-month rolling window of past returns.
   • Identifying the 20% of market states most similar to the current state over a 480-month rolling window of past returns, excluding the latest 36 months.
   • Computing subsequent average industry returns for these similar market states.
3. Backtests a strategy that each month buys (sells) those of 30 industry portfolios with positive (negative) expected returns based on current market state. For robustness, he considers alternative industry classifications with 10, 12, 17, 38 and 48 portfolios and different market similarity parameters.

Using monthly returns for the U.S. stock market/industry portfolios and the monthly risk-free rate from the Kenneth French data library during July 1926 through December 2025, he finds that: Keep Reading

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

• Invesco S&P 500 Low Volatility Portfolio (SPLV) – the 100 stocks from the S&P 500 Index with the lowest realized volatility over the past 12 months, reformed quarterly. The benchmark ETF for SPLV is SPDR S&P 500 (SPY).
• iShares Edge MSCI Min Vol USA (USMV) – seeks to track an index composed of U.S. equities that, in the aggregate, have lower volatility characteristics relative to the broader U.S. equity market. The benchmark ETF for USMV is iShares Russell 3000 (IWV).
• iShares Edge MSCI Min Vol EAFE (EFAV) – seeks to track an index composed of developed market equities that, in the aggregate, have lower volatility characteristics relative to the broader developed equity markets, excluding the U.S. and Canada. The benchmark ETF for EFAV is iShares MSCI EAFE Index (EFA).
• iShares Edge MSCI Min Vol Emerging Markets (EEMV) – seeks to track an index composed of emerging market equities that, in the aggregate, have lower volatility characteristics relative to the broader emerging equity markets. The benchmark ETF for EEMV is iShares MSCI Emerging Markets Index (EEM).
• iShares Edge MSCI Min Vol Global (ACWV) – seeks to track an index composed of developed and emerging market equities that, in the aggregate, have lower volatility characteristics relative to the broader developed and emerging equity markets. The benchmark ETF for ACWV is iShares MSCI ACWI (ACWI).
• Invesco S&P International Developed Low Volatility Portfolio (IDLV) – the 200 least volatile stocks of the S&P Developed excluding U.S. and South Korea LargeMid Cap BMI Index over the past 12 months, reformed quarterly. The Index is computed using net return, which withholds taxes applicable to non-resident investors. The benchmark ETF for IDLV is Vanguard FTSE All-Wld ex-US ETF (VEU).
• Invesco S&P MidCap Low Volatility Portfolio (XMLV) –  the 80 out of 400 medium-capitalization stocks from the S&P MidCap 400 Index with the lowest realized volatility over the past 12 months, reformed quarterly. The benchmark ETF for XMLV is SPDR S&P MidCap 400 (MDY).
• Invesco S&P SmallCap Low Volatility Portfolio (XSLV) – the 120 out of 600 small-capitalization stocks from the S&P SmallCap 600 Index with the lowest realized volatility over the past 12 months, reformed quarterly. The benchmark ETF for XSLV is iShares Core S&P Small-Cap (IJR).

We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the low volatility stock ETFs and their benchmark ETFs as available through May 2026, we find that: Keep Reading

Are exchange-traded funds (ETF) focused on Initial Public Offerings of stocks (IPO) attractive? To investigate, we consider three of the largest IPO ETFs and one recent Special Purpose Acquisition Company (SPAC) ETF, one of which is no longer available, in order of longest to shortest available histories:

• First Trust US Equity Opportunities ETF (FPX) – seeks to track price and yield of the IPOX-100 U.S. Index.
• Renaissance IPO ETF (IPO) – reflects approximately the top 80% of new public firms weighted by free float capitalization with a 10% cap for any one position. Large IPOs enter quickly and others enter during quarterly reviews. All exit two years after initial trade date.
• First Trust International Equity Opportunities ETF (FPXI) – seeks gross investment results that track price and yield of the IPOX International Index.
• Defiance Next Gen SPAC Derived ETF (SPAK) – 60% weight to IPOs derived from SPACs and 40% weight to common stock of newly listed SPACs, excluding warrants (dead as of the end of August 2022).

We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). For all these ETFs, we use SPDR S&P 500 (SPY) as the benchmark. Using monthly returns for the IPO ETFs and SPY as available through April 2026, we find that:

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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 April 2026, we find that: Keep Reading

What are current estimates of equity risk premiums (ERP) and risk-free rates around the world? In their April 2026 paper entitled “Survey: Market Risk Premium and Risk-Free Rate used for 97 countries in 2026”, Pablo Fernandez, Amir Habibian and Lucia Acin summarize results of a March-April 2026 email survey of international finance and economic professors, analysts and company managers about the risk-free rate and the Market Risk Premium (MRP) used to calculate the required return to equity in different countries. Results are in local currencies. Based on 3,637 specific and credible premium estimates spanning 97 countries for which there are at least eight estimates, they find that: Keep Reading