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Are the “Simple Asset Class ETF Value Strategy” (SACEVS) and the “Simple Asset Class ETF Momentum Strategy” (SACEMS) mutually diversifying. To check, based on feedback from subscribers about combinations of interest, we look at three equal-weighted (50-50) combinations of the two strategies, rebalanced monthly:

1. 50-50 Best Value – EW Top 2: SACEVS Best Value paired with SACEMS Equally Weighted (EW) Top 2 (aggressive value and somewhat aggressive momentum).
2. 50-50 Best Value – EW Top 3: SACEVS Best Value paired with SACEMS EW Top 3 (aggressive value and diversified momentum).
3. 50-50 Weighted – EW Top 3: SACEVS Weighted paired with SACEMS EW Top 3 (diversified value and diversified momentum).

We consider as a benchmark a simple technical strategy (SPY:SMA10) that holds SPDR S&P 500 ETF Trust (SPY) when the S&P 500 Index is above its 10-month simple moving average and 3-month U.S. Treasury bills (Cash, or T-bills) when below. We also test sensitivity of results to deviating from equal SACEVS-SACEMS weights. Using monthly gross returns for SACEVS, SACEMS, SPY and T-bills during July 2006 through August 2025, we find that: Keep Reading

“Compendium of Live ETF Factor/Niche Premium Capture Tests” summarizes results for its eponymous title. Here we add a live test of the short-term reversal effect among U.S. stocks. Specifically, we examine the performance of the now dead Vesper U.S. Large Cap Short-Term Reversal Strategy ETF (UTRN), designed to track the performance of a portfolio of 25 of the 500 largest U.S.-listed stocks most likely benefit from the short-term reversal effect. We use SPDR S&P 500 ETF Trust (SPY) as the benchmark. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly total returns for UTRN and SPY during September 2018 (UTRN inception) through March 2025 (UTRN death), we find that:

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Is relevant news sentiment from other countries additive to local news in predicting country stock market returns? In their August 2025 paper entitled “Global News Networks and Return Predictability”, Gustavo Freire, Ali Moin, Alberto Quaini and Amar Soebhag compare the effects of local versus global news sentiment, organized into 260 major themes, on country stock market returns. They apply random forest machine learning to news sentiment variables to construct out-of-sample market timing strategies based on local versus global sentiment measures. Specifically, they:

• Fit random forests on the initial 18 months of local or global data to select base models.
• Use the next six months to select optimal hyperparameters.
• Retrain the model every 20 trading days with an expanding window, so that each new fit uses all data up to the end of the previous month.
• Each day for each country market, go long (short) that market for that day if its projected return is positive (negative).

Using Global Knowledge Graph Database news sentiment data for 520 million articles from 14 developed countries and associated country market returns computed from futures contract prices for 14 equity indexes during February 2015 through December 2023, they find that:

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Does alignment of return-based factors with informed traders and against noise traders produce a superior model of stock returns? In his August 2025 paper entitled “An Auto-Residual Factor Model”, Malek Alkshaik introduces and tests a 5-factor Auto-Residual Factor Model of stock returns comprised of: market excess return; market capitalization (size); residual short-term reversal (last month); residual momentum (measured from 12 months ago to one month ago): and, residual long-term reversion (measured from 24 months ago to 13 months ago). This model uses no firm fundamental data. He postulates that the latter three factors occur due to interactions between noise traders and informed traders. He calculates (purifies) residuals via regressions against five principal components derived from the last 24 months of returns for all stocks, thereby aligning residuals with informed traders and against noise traders (purifying). He emphasizes maximum squared Sharpe ratio (based on mean-variance optimal factor allocations) to compare the new model to seven widely used alternatives. Using a main sample of U.S. listed common stocks during 1972 through 2022, plus a 1932 through 1971 U.S. sample and a 1992 through 2022 global sample for robustness tests, he finds that:

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Should investors relish opportunities to invest in private equity? In his July 2025 paper entitled “The Private Equity Illusion: Revisiting Risks, Returns, and Realities”, Simon Nocera analyzes the narrative that private equity is a superior asset class that delivers higher returns at lower risk than, and diversifies, public equity markets. Using investment industry data, academic research, consulting reports and empirical data focused on the last decade, he finds that:

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Does the ICE BofAML MOVE Index, the implied volatility of U.S. Treasuries as derived from options on U.S. Treasuries with maturities 2, 5, 10 and 30 years, usefully predict U.S. stock market and U.S. Treasury bond returns? To investigate, we perform two sets of calculations using SPDR S&P 500 ETF (SPY) as a proxy for the U.S. stock market and iShares 20+ Year Treasury Bond ETF (TLT) as a proxy for U.S. Treasury bonds:

1. Lead-lag analyses using correlations between end-of-month MOVE Index or change in MOVE Index and monthly SPY or TLT returns.
2. Average next-month SPY or TLT returns by ranked fifth (quintile) of end-of-month MOVE Index or change in MOVE Index.

Using end-month MOVE Index levels and monthly dividend-adjusted SPY and TLT data during November 2002 (limited by MOVE Index data) through August 2025, we find that: Keep Reading

Are equity multifactor strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider eight multifactor ETFs, all currently available:

• iShares Edge MSCI Multifactor USA (LRGF) – holds large and mid-cap U.S. stocks with focus on quality, value, size and momentum, while maintaining a level of risk similar to that of the market. The benchmark is iShares Russell 1000 (IWB).
• iShares Edge MSCI Multifactor International (INTF) – holds global developed market ex U.S. large and mid-cap stocks based on quality, value, size and momentum, while maintaining a level of risk similar to that of the market. The benchmark is iShares MSCI ACWI ex US (ACWX).
• Goldman Sachs ActiveBeta U.S. Large Cap Equity (GSLC) – holds large U.S. stocks based on good value, strong momentum, high quality and low volatility. The benchmark is SPDR S&P 500 (SPY).
• John Hancock Multifactor Large Cap (JHML) – holds large U.S. stocks based on smaller capitalization, lower relative price and higher profitability, which academic research links to higher expected returns. The benchmark is SPY.
• John Hancock Multifactor Mid Cap (JHMM) – holds mid-cap U.S. stocks based on smaller capitalization, lower relative price and higher profitability, which academic research links to higher expected returns. The benchmark is SPDR S&P MidCap 400 (MDY).
• JPMorgan Diversified Return U.S. Equity (JPUS) – holds U.S. stocks based on value, quality and momentum via a risk-weighting process that lowers exposure to historically volatile sectors and stocks. The benchmark is SPY.
• Xtrackers Russell 1000 Comprehensive Factor (DEUS) – seeks to track, before fees and expenses, the Russell 1000 Comprehensive Factor Index, which seeks exposure to quality, value, momentum, low volatility and size factors. The benchmark is IWB.
• Vanguard U.S. Multifactor (VFMF) – uses a rules-based quantitative model to evaluate U.S. common stocks and construct a U.S. equity portfolio that seeks to achieve exposure to multiple factors across market capitalizations (large, mid and small). The benchmark is iShares Russell 3000 (IWV).

We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the seven equity multifactor ETFs and benchmarks as available through August 2025, we find that: Keep Reading

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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