Equity Premium

Do a relatively few stocks drive the alphas of many anomalies? In the May 2025 revision of his paper entitled “The Intersection of Expected Returns”, Austin Sobotka explores stock overlap among the portfolios of 164 cross-sectional asset pricing anomalies. Specifically, he each month:

• Ranks stocks into tenths (deciles) by each anomaly characteristic, lagged by one month.
• Computes the number of times each stock falls into extreme deciles for each anomaly.
• Identifies stocks that appear in many extreme deciles (overlap) across anomalies.
• Forms for each anomaly three extreme-decile, long-short portfolios: (1) the conventional anomaly portfolio; (2) the anomaly excluding overlap stocks per some threshold (filtered); and, (3) the anomaly with only overlap stocks per some threshold (overlap). For example, 90th percentile filtered (overlap) portfolios exclude (include only) the 10% of stocks with the greatest long side overlap and the 10% of stocks with the greatest short side overlap.
• Holds these portfolios for one month.

He considers both equal-weighted and value-weighted versions of all portfolios. For robustness, he repeats the analysis ranking stocks into fifths (quintiles), applying various liquidity screens, rebalancing annually rather than monthly and using different sample periods. Using cleaned, winsorized monthly firm-level data for publicly traded non-financial stocks with non-zero market equity and priced over $1 as available during 1926 through 2023, he finds that: Keep Reading

How should retail investors view funds offering private equity opportunities? In his July 2025 paper entitled “Private Markets for the People? Or Just More People for Private Markets?”, Ludovic Phalippou assesses the push to expand private equity access to retail investors. He highlights risks embedded in current product design. Based on review of such offerings, he concludes that:

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The value of holdings in passive (capitalization-weighted, index tracking) U.S. equity funds now exceeds that in active U.S. equity funds. In their May 2025 paper entitled “Passive Aggressive: The Risks of Passive Investing Dominance”, Chris Brightman and Campbell Harvey explore implications of the dramatic growth in passive investment strategies, including loss of diversification and mispricing. Using recent market data and results from past research, they conclude that:

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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 2025, we find that: Keep Reading

Do private equity funds beat the public equity market, as implied by allocations of large investors such as university endowments, pension funds and sovereign wealth funds? In his June 2025 paper entitled “Apples and Oranges: Benchmarking Games and the Illusion of Private Equity Outperformance”, Ludovic Phalippou updates private equity fund performance, focusing on 2000-2019 vintage funds that are past the investment phase and have little selection bias. He examines the reputation of private equity as a market-beating alternative asset. Using MSCI Burgiss data through the end of 2024, 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 2025, we find that: Keep Reading

What are current estimates of equity risk premiums (ERP) and risk-free rates around the world? In their May 2025 paper entitled “Survey: Market Risk Premium and Risk-Free Rate Used for 54 countries in 2025”, Pablo Fernandez, Diego Garcia and Lucia Acin summarize results of an April 2025 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 2,749 specific and credible premium estimates spanning 54 countries for which there are at least six estimates, they find that: Keep Reading

How much should investors who hold a conventionally diversified portfolio (stocks and bonds) be willing to pay for and an additional equity or bond fund that outperforms its benchmark (provides alpha)? In their May 2025 paper entitled “How Much Should You Pay for Alpha? Measuring the Value of Active Management with Utility Calculations”, Andrew Ang and Debarshi Basu estimate the amount an investor is willing to pay for access to an active equity or bond mutual fund, starting from an optimal stocks-bonds portfolio. Specifically, they:

1. Empirically estimate investor risk aversion for a given stocks-bonds base portfolio, focusing on a 60-40 S&P 500 Total Return Index-Bloomberg US Aggregate Bond Index portfolio.
2. Add one of 1,203 active U.S. large-capitalization mutual funds in the Morningstar database or one of 47 fixed income mutual funds in the Morningstar Core Plus US bond category to the base portfolio.
3. For each added fund, compute the utility benefit (certainty equivalent or willingness-to-pay) of adding it.

For robustness, they repeat this analysis for different stocks-bonds base portfolios and different assumptions about equity-bond correlations. Using monthly returns for the selected indexes and mutual funds during January 2016 to December 2024, they find that: Keep Reading

Should investors expect a negative correlation between stock market and bond market returns? In his February 2025 paper entitled “Rethinking the Stock-Bond Correlation”, Thierry Roncalli examines the stocks-bond return correlation from theoretical and empirical perspectives, employing a 4-year rolling window of monthly returns for the latter. Using both long-term and recent returns, he finds that: Keep Reading

Can investors exploit analyst stock price targets by finding the best analysts and overweighting the most extreme target-implied returns? In their March 2025 paper entitled “Alpha in Analysts”, Álvaro Cartea and Qi Jin test the informativeness and exploitability of sell-side analyst stock price targets. To test informativeness of target prices, they each month for each analyst:

• Use price targets to deduce 12-month return forecasts.
• Form a hedge portfolio that is long (short) stocks with positive (negative) return forecasts, with weights proportional to magnitudes of forecasted returns and absolute value of the sum of weights equal to one.
• Compare analyst portfolio performance to that of an equal-weighted, long-only portfolio of the same stocks.

To test exploitability of results, they each month:

• Predict portfolio profitability for each analyst via an inception-to-date regression of six analyst performance metrics up to 12 months ago (capturing historical performance and breadth of stock coverage) versus next-month portfolio return.
• Construct a portfolio of analyst portfolios with higher (lower) allocations to those with higher (lower) predicted returns.

Using daily analyst price targets and associated stock returns/firm characteristics as available for common NYSE/AMEX/NASDAQ stocks during January 1999 through November 2024, they find that: Keep Reading