Investing Expertise

A Securities Information Processor (SIP) aggregates quotes and trades from all U.S. stock exchanges to feed the NYSE Trade and Quote (TAQ) database, used in much finance research to (for example) estimate effective bid-ask spreads and associated trading frictions. Is this database trustworthy? In their December 2025 paper entitled “Latency and the Look-Ahead Bias in Trade and Quote Data”, Robert Battalio, Craig Holden, Matthew Pierson, John  Shim and Jun Wu investigate the reliability of TAQ data, with focus on the arrival times of data with different latencies (delays) as compared to the assuredly ordered NYSE Arca Direct Feed Data. Using timestamped NYSE Daily TAQ data and NYSE Arca Direct Feed Data for the month of June 2019, they find that: Keep Reading

Most researchers use classical statistical testing, with a t-statistic of 2.0 as the significance threshold for accepting an hypothesis. However, this threshold is valid only if the associated p-value derives from a single test. There are hundreds of published factor tests and an unknown number of unpublished tests. How far should researchers raise the significance threshold to account for multiple hypothesis testing? In their December 2025 paper entitled “What Threshold Should be Applied to Tests of Factor Models?”, Campbell Harvey, Alessio Sancetta and Yuqian Zhao address this issue by:

1. Clarifying applicable statistical methods, including how to measure the probability that the null hypothesis is true and insight on the False Discovery Rate (FDR), without knowing the number of tests.
2. Reconciling existing results in the literature.
3. Providing guidance on the threshold for deciding statistical significance.

They also discuss the plausibility of the assumptions embedded in their approach. Based on mathematical analysis in the context of financial research, they find that: Keep Reading

Prior research suggests that machine learning factor models of the cross section of stock returns greatly enhance portfolio performance by: (1) expanding the dataset to include more variables; and, (2) allowing more complex (non-linear) variable interactions. Does this finding hold up in a realistic portfolio management scenario? In their November 2025 paper entitled “What Drives the Performance of Machine Learning Factor Strategies?”, Mikheil Esakia and Felix Goltz decompose performance contributions from these two enhancements in scenarios ranging from ideal to realistic. The ideal scenario, found in much machine learning research, ignores portfolio management constraints. The realistic scenario excludes microcaps, removes look-ahead bias for yet-to-be-published factors and accounts for trading frictions. They further look at exclusion of shorting. They estimate trading frictions as half the monthly effective bid-ask spread (daily average of closing quoted spreads). Using daily and monthly data for publicly listed U.S. common stocks and monthly data for 94 firm-level characteristics as available during June 1963 and through December 2021, they find that: Keep Reading

How should researchers apply and restrict artificial intelligence (AI) in research? In the December 2025 revision of their editorial entitled “The Use of AI in Academic Research”, Gordon Graham and Jennifer Tucker share experiences as accounting journal editors in dealing with this question. They review the meaning and capabilities of AI. They address the extent to which AI can perform the tasks involved in production of academic research, including pros, cons and unintended consequences. Based on their experiences, they conclude that: Keep Reading

Can Grok extract a useful weekly U.S. stock market sentiment metric from posts on X? To investigate, we ask Grok to each week for two years aggregate weekly U.S. stock market sentiment looking for at least 50 posts per week (ending Saturdays) and weighting each post sentiment according to its audience engagement (influence). For example, the Grok Sentiment for 2025-11-29 encompasses posts from 2025-11-23 through 2025-11-29. We then relate the resulting aggregate sentiment values and change in these values to S&P 500 Index (SP500) returns from the first open after measurement (usually the Monday open) to the close before the next measurement (usually the Friday close). Using the specified weekly inputs we find that: Keep Reading

Can a large language model (LLM) applied to social media data catalog the strategy choices, sentiment and trading behavior of retail investors? In the November 2025 revision of their paper entitled “Wisdom or Whims? Decoding Retail Strategies with Social Media and AI”, Shuaiyu Chen, Lin Peng and Dexin Zhou apply GPT-4 Turbo and BERT to StockTwits messages to classify retail investor strategies as: (1) technical analysis (TA); (2) fundamental analysis (FA); (3) other strategies (such as options trading); or, (4) no strategy. They then relate strategy classes to future stock returns and trading activity. Using StockTwits messages posted by 840,846 investors on 7,834 common stocks and associated accounting, price, trade order and financial news during January 2010 through June 2023, they find that: Keep Reading

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 October 2025, we find that: Keep Reading

A subscriber asked about the relationship between Berkshire Hathaway cash position and S&P 500 Index returns. To investigate, we:

1. Located annual “Berkshire Hathaway (BRK-B) – Cash on Hand” data as a baseline cash pile.
2. Asked Grok to construct a table showing annual Berkshire Hathaway cash plus U.S. Treasury bills (T-bills), annual total assets and annual cash plus T-bills as a percentage of total assets. Grok stated that these data come “directly from Berkshire Hathaway’s official SEC filings — no third-party estimates, no aggregators.” We also asked Grok to explain any differences between its cash plus T-bills series and the baseline series.
3. Performed lead-lag analyses between each cash series as percentage of total assets versus annual S&P 500 Index (SP500) annual returns.

Scaling cash position to total assets measures the importance of cash to Berkshire Hathaway management. Using the specified annual data for 1996 through 2024, we find that: Keep Reading

Can insights inferred from real-time financial news by large language models (LLM) such as ChatGPT 4.0 mini enhance a conventional stock momentum strategy? In their October 2025 paper entitled “ChatGPT in Systematic Investing – Enhancing Risk-Adjusted Returns with LLMs”, Nikolas Anic, Andrea Barbon, Ralf Seiz and Carlo Zarattini investigate whether ChatGPT can improve a conventional momentum strategy applied to S&P 500 stocks by extracting predictive signals from minute-level Stock News API news articles. Specifically, they each month:

1. Rank stocks based on returns from 12 months ago to one month ago.
2. Construct an equal-weighted or value-weighted long-only momentum portfolio by buying stocks in the top 20% of rankings (top two deciles).
3. Ask ChatGPT to quantify the potential of each stock in the momentum portfolio to increase Sharpe ratio and suppress maximum drawdown (MaxDD), and weight each stock according to this signal.

They apply 0.02% trading frictions to portfolio changes to test net performance. Using daily total returns for S&P 500 stocks, relevant high-frequency Stock News API articles and the daily U.S. risk-free rate to perform model validation during October 2019 through December 2023 and out-of-sample testing during January 2024 through March 2025, they find that: Keep Reading

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