Equity Premium

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 2025, we find that: Keep Reading

Do AIs that incorporate audio and video (multimodal) aspects of firm/stock analysis (e.g. from YouTube), including tone, delivery style and facial expressions, distill better buy and sell recommendations from financial influencers (finfluencers) than text-only large language models (LLM)? In their May 2025 paper entitled “VideoConviction: A Multimodal Benchmark for Human Conviction and Stock Market Recommendations”, Michael Galarnyk, Veer Kejriwal, Agam Shah, Yash Bhardwaj, Nicholas Meyer, Anand Krishnan and Sudheer Chava test abilities of 16 text-only LLMs and six multimodal LLMs (MLLM) and to extract stock recommendations and associated levels of conviction from a benchmark dataset. This dataset consists of 288 finfluencer videos of 12 minutes or less from 22 YouTube channels during January 2018 through December 2024, each transcribed and annotated by five human experts. These experts identify 687 unique stock recommendation segments in these videos and assign a level of conviction to each. The authors then perform two sets of tests on this benchmark dataset:

• The ability of each LLM/MLLM to mimic human expert analyses with respect to identifying recommended stock tickers, recommended actions (buy or sell) and the level of finfluencer conviction for each recommendation.
• The inherent value of finfluencer recommendations by comparing performances of the following three active portfolios to those of Invesco QQQ Trust (QQQ) and SPDR S&P 500 ETF (SPY):
  1. Buy & Hold: hold each Buy recommendation for six months with equal weights.
  2. Buy & Hold (Weighted by Conviction): hold each Buy recommendation for six months, with positions weighted by relative conviction (position conviction divided by the sum of all position convictions).
  3. Inverse YouTuber: do the opposite of recommendations by selling Buys and buying Sells with equal weights and holding each position for six months.

Using the specified benchmark dataset, they find that:

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Is the performance of listed private equity funds (LPE) on the London Stock Exchange attractive? In their August 2025 paper entitled “What the London Stock Exchange can Teach Us About Private Equity”, Richard Ennis and Daniel Rasmussen examine LPE volatility, valuation and performance and compare findings with those based on private equity net asset values (NAV). LPE holdings are typical of the private equity investments made by pension funds and endowments. Several LPEs are funds of funds that invest in large numbers of separate private equity funds. The principal benchmark for LPEs is the MSCI ACWI. Using annual June-to-June returns for a capitalization-weighted sample 13 LPEs with capitalizations of at least $500 million (three dead and 10 live), associated NAVs and ACWI during June 2008 through June 2025, 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 July 2025, we find that: Keep Reading

Do exchange-traded funds (ETF) focused on cybersecurity stocks offer attractive performance? To investigate, we compare performance statistics of five cybersecurity ETFs, all currently available, to those of Invesco QQQ Trust (QQQ), as follows:

1. Amplify Cybersecurity ETF (HACK)
2. First Trust NASDAQ Cybersecurity ETF (CIBR)
3. iShares Cybersecurity and Tech ETF (IHAK)
4. Global X Cybersecurity ETF (BUG)
5. WisdomTree Cybersecurity Fund (WCBR)

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 QQQ over matched sample periods, all through July 2025, we find that: Keep Reading

Do exchange-traded funds (ETF) that hold convertible corporate bonds offer attractive performance? To investigate, we compare performance statistics for the following four convertible bond ETFs, all currently available, to those for a monthly rebalanced 60%-40% combination of SPDR S&P 500 ETF Trust (SPY) and iShares iBoxx $ Investment Grade Corporate Bond ETF (LQD):

1. SPDR Bloomberg Convertible Securities ETF (CWB)
2. iShares Convertible Bond ETF (ICVT)
3. First Trust SSI Strategic Convertible Securities ETF (FCVT)
4. American Century Quality Convertible Securities ETF (QCON)

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 SPY and LQD over matched sample periods through July 2025, we find that: Keep Reading

How should global investors assess country sovereign bond and equity risks? In his July 2025 paper entitled “Country Risk: Determinants, Measures and Implications – The 2025 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 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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