Investing Expertise

Do exchange-traded funds (ETF) that seek to mimic holdings of top-ranked hedge funds, firm insiders or other investing gurus offer attractive performance? To investigate, we consider nine ETFs, five live and four dead, in order of introduction:

• • Invesco Insider Sentiment (NFO) – focuses on stocks attracting interest of insiders such as company executives, fund managers and sell side analysts. This fund is dead as of February 2020.
  • Invesco BuyBack Achievers (PKW) – tracks the Nasdaq US BuyBack Achievers Index, comprised of stocks of U.S. firms with a net decline in shares outstanding of 5% or more in the last 12 months.
  • Direxion All Cap Insider Sentiment (KNOW) –  tracks the S&P Composite 1500 Executive Activity & Analyst Estimate Index, comprised of U.S. stocks that have favorable analyst ratings and are being acquired by firm insiders (top management, directors and large institutions). This fund is dead as of October 2020.
  • AlphaClone Alternative Alpha – (ALFA) – tracks the proprietary AlphaClone Hedge Fund Masters Index, comprised of U.S. securities held by the highest ranked managers of  hedge funds and institutions. This fund is dead as of August 2022.
  • Global X Guru Index (GURU) – tracks the Solactive Guru Index, comprised of the highest conviction ideas from a select pool of hedge funds.
  • Direxion iBillionaire (IBLN) –  tracks the proprietary iBillionaire Index, comprised of 30 U.S. mid and large cap securities. This fund is dead as of April 2018.
  • Goldman Sachs Hedge Industry VIP (GVIP) – tracks the proprietary GS Hedge Fund VIP Index, comprised of stocks appearing most frequently among the top 10 equity holdings of fundamentally driven hedge fund managers.
  • Guru Favorite Stocks (GFGF) – tracks stock holdings of about 20 quality-oriented gurus who have publicly available records of at least 10 years.
  • Motley Fool Next Index (TMFX) – tracks the performance of mid- and small-capitalization U.S. companies recommended by The Motley Fool analysts and newsletters.

We use SPDR S&P 500 (SPY) as a simple benchmark for all these ETFs. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the above guru/insider-following ETFs and SPY as available through September 2025, we find that: Keep Reading

Are there any experts who can reliably predict stock market returns? In their September 2025 paper entitled “Beliefs and Stock Market Fluctuations: New Evidence from the Past Seven Decades”, David Thesmar and Emil Verner assemble and test a 69-year sample of expected stock earnings and returns from Value Line (about 1,500 firms per year). They first compute the expected return for each stock by combining long-term (three to five years) Value Line earnings expectations and price targets. They then aggregate these expectations to annual market-weighted time series. They then compare the predictive power of the Value Line series to those of survey-based expectations of finance professionals, professional forecasters and individuals from prior research. Using the specified Value Line inputs during 1956 through 2024 and expectations from various surveys, they find that:

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What are life lessons from one of the leading researchers in finance? In the August 2025 transcript of his interview entitled “My Life in Finance in 12 Questions”, Campbell Harvey offers the following notable points relevant investors regarding (1) most important findings and (2) interpretation of academic research:

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Do active investment managers as a group successfully time the stock market? The National Association of Active Investment Managers (NAAIM) is an association of registered investment advisors. “NAAIM member firms who are active money managers are asked each week to provide a number which represents their overall equity exposure at the market close on a specific day of the week (usually Wednesday). Responses can vary widely [200% Leveraged Short; 100% Fully Short; 0% (100% Cash or Hedged to Market Neutral); 100% Fully Invested; 200% Leveraged Long].” The association each week releases (usually on Thursday) the average position of survey respondents as the NAAIM Exposure Index (NEI).” Using historical weekly survey data and Thursday-to-Thursday weekly dividend-adjusted returns for SPDR S&P 500 (SPY) over the period July 2006 through late July 2025, we find that: Keep Reading

Can investors rely upon general-purpose Artificial Intelligence (AI) to tackle the complex data science problems of investment analysis?

In his July 2025 presentation package entitled “AI Challenges in Mathematical Investing”, Marcos Lopez de Prado addresses why AI-driven methods fail in the context of investing and offers insights into how these challenges can be mitigated. Based on his investment analysis experience, he concludes that: Keep Reading

How is the growing use of large language models (LLM) in production of academic papers in finance affecting outputs? In their June 2025 paper entitled “Certainly! Generative AI and its Impact on Academic Writing (in Finance)”, Thomas Walther and Marie Dutordoir investigate how use of LLMs has affected academic writing in finance, with focus on readability. They quantify readability via the widely-used Flesch- Kincaid Index (FKI) and Gunning-Fog Index (GFI), both of which estimate the number of years of education required to understand a certain text in a first reading. They also look at author characteristics associated with LLM use and the impact of LLM use on author publication quantity, quality (journal rankings) and impact (citations). They treat the release of ChatGPT at the end of November 2022 as the LLM adoption date. To determine author use of LLMs, they: (1) identify specific words that disproportionally appear in LLM-generated text (AI Words); (2) count the AI Words in the abstract, introduction and full text of each article; and, (3) divide the number of AI Words by the total number of words in the article. Using 41,489 articles (minimum 200 words) from 34 finance journals published in English from January 1, 2000 to April 1, 2025 (32,993 from before December 1, 2022 and 8,496 after), they find that: Keep Reading

Do the trading activities of especially informed equity and equity option traders predict stock returns? In the June 2025 revision of their paper entitled “An Information Factor: What Are Skilled Investors Buying and Selling?”, Matthew Ma, Xiumin Martin, Matthew Ringgenberg and Guofu Zhou construct an information factor (INFO) using the trades of corporate insiders, short sellers and option traders. Specifically, they each month for each stock calculate:

• To inform the long side of the INFO factor portfolio, net insider purchases (purchases minus sales).
• To inform the short side of the INFO factor portfolio:
  • Short interest (number of shares shorted divided by shares outstanding).
  • Option trading (total option volume divided by total stock volume).
• For each of these three metrics, assign a rank from 1 to 100, with higher rank indicating higher level of positive private information.
• Average the three ranks to compute an information score.
• Reform 10 equal-weighted (decile) portfolios of stocks sorted by information score, with the INFO factor portfolio long the top decile and short the bottom.
• Hold the portfolios for one month.

They assess the impact of stock trading frictions by assuming costs equal to half the respective effective bid-ask spreads. Using insider trading, short interest and option/stock trading volumes during January 1996 through December 2019, they find that: Keep Reading

Could mutual fund managers achieve performance improvements by “hiring” artificial intelligence (AI) analysts? In their May 2025 working draft entitled “The Shadow Price of ‘Public’ Information”,  Ed deHaan, Chanseok Lee, Miao Liu and Suzie Noh estimate the value of an AI stock picking analyst by having it exploit public data to improve mutual fund holdings. Specifically, they:

• Each year train the AI (random forest model) on an expanding window of 170 stock market, accounting, analysts and macroeconomic inputs.
• For each mutual fund each quarter:
  • Have the AI make limited improvement/replacement picks similar to the type of stocks already in the fund, with the goal of maximizing benchmark-adjusted returns. The benchmark for each fund consists of a portfolio of stocks with similar characteristics.
  • Require the AI to mimic original fund and holdings sizes.
  • Quantify the profit difference between original and AI-improved portfolios.

They also consider matched portfolios selected completely (not partial replacement) by the AI analyst. Using data for 3,337 active/diversified U.S. equity mutual funds during 1990 through 2020, and for firm characteristics and trading data of all U.S. listed common stocks, macroeconomic variables, analyst forecasts, credit ratings and market/firm sentiment data starting 1980, they find that:

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Do funds based on holdings/trades of members of the U.S. Congress and their families beat the market? To investigate, we look at performances of two recently introduced exchange-traded funds (ETF):

1. Unusual Whales Subversive Democratic Trading ETF (NANC) – invests primarily in stocks held by Democratic members of Congress and/or their families per public disclosure filings.
2. Unusual Whales Subversive Republican Trading ETF (KRUZ) – invests primarily in stocks held by Republican members of Congress and/or their families per public disclosure filings.

We use SPDR S&P 500 ETF Trust (SPY) as a benchmark. Using monthly dividend-adjusted prices for NANC, KRUZ and SPY during February 2023 (NANC and KRUZ inception) through May 2025, we find that: Keep Reading

How well do the short-term stock picks of publicly available artificial intelligence (AI) platforms perform? To investigate, we asked Grok, ChatGPT, Perplexity, Gemini and Meta AI the following questions on April 20, 2025:

• Please succinctly provide your unique best long idea for the next 30 days.
• Please succinctly provide your unique best shorting idea for the next 30 days.

We then: (1) calculated total returns for the resulting stock picks from the close on April 21, 2025 to the close on May 21, 2025; (2) averaged the returns for long and short picks; and, (3) compared  average returns for long and short picks. We include total returns for SPDR S&P 500 ETF (SPY) and Invesco QQQ Trust (QQQ) over this same interval for context. Using dividend-adjusted prices for the specified picks, we find that:

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