Investing Expertise

In “Methodology Changes”, we noted implementation of an “Investing Research Sweep” via Claude to mitigate a markedly lower perceived signal-to-noise ratio from the Social Science Research Network (SSRN). In this follow-up, we ask our artificial intelligence (AI) Panel to assess the perception. Specifically, we pose to each of Grok, ChatGPT, Claude, Perplexity and Gemini the following prompt:

Please concisely assess in one paragraph the hypothesis that AI-assisted analysis and writing is flooding financial research channels such as SSRN with findings of dubious reliability and little practical import from sources around the world, driving a dramatically lower signal-to-noise ratio.

We also summarize usefulness of the research sweep. Based on AI Panel responses (lightly edited below) and research sweep experience, we conclude that: Keep Reading

How should investors think about strategies featured as attractive by researchers and investment advisors? In his August 2026 paper entitled “Test Everything, Publish Both Results: A Protocol That Cuts Backtest False Positives from 37% to 0.4%”, Alex Vidovich offers both researchers and consumers of investment strategy research guidance on assessing the rigor of investment strategies. Relying mainly on illustrative simulations, he concludes that: Keep Reading

How should investors weight investment banker pronouncements on asset allocation as reported in financial media? Can the evolving set of artificial intelligence (AI) platforms based on large language models assist? As a simple exploration, we pose to each of Grok, ChatGPT, Claude, Perplexity and Gemini the following prompt:

Please rank JP Morgan, Goldman Sachs, Morgan Stanley, Bank of America (BofA Securities) and Citigroup (Citi) 1 to 5 according to the proven value of their advice for asset allocation.

We then compare and contrast results from AI panel members. Using responses to the prompt as posed at the end of July 2026, we find that: Keep Reading

Large language models (LLM) have quickly become a standard tool for extracting quantitative metrics from unstructured text. Are these metrics more like new data or a biased filter? In the July 2026 revision of their paper entitled “LLMs and Systematic Measurement Error”, William Grieser, Anthony Cookson, Maryam Fathollahi and Eshwar Venugopal examine how LLMs process text and thereby introduce errors of omission and injection. The authors then examine how LLM omission and injection rates influence summaries of firm risk disclosures and earnings call transcripts. Using four widely used models (GPT-4o-mini, Grok-3-mini, Phi-4, Llama-3.3-70B) to summarize annual Form 10-K, Item 1A risk factor disclosures and quarterly earnings call transcripts of S&P 500 firms during 2005 through 2024, they find that: Keep Reading

Is the evolving set of artificial intelligence (AI) platforms based on large language models interesting for exploring target entry prices for specific stocks? Are they monolithic, or diverse? As a simple exploration, we pose to each of Grok, ChatGPT, Claude, Perplexity and Gemini the following prompt regarding Space Exploration Technologies Corp. (SPCX):

At what price in the next six months would SPCX (SpaceX common stock) be a screaming buy?

We then compare and contrast results from AI panel members. Using responses to the prompt as posed near the end of July 2026, we find that: Keep Reading

Is the evolving set of artificial intelligence (AI) platforms based on large language models interesting as stock market forecasters? Are they monolithic, or diverse? As a simple exploration, we pose to each of Grok, ChatGPT, Claude, Perplexity and Gemini the following prompt regarding end-of-month (EOM) levels of the S&P 500 Index over the next 12 months:

Please provide your unique estimate of end-of-month levels of the S&P 500 Index from July 2026 through June 2027. Do not provide any explanations.

We then compare and contrast results from AI panel members. Using responses to the prompt as posed in mid-July 2026, we find that: Keep Reading

Is the evolving set of artificial intelligence (AI) platforms based on large language models (LLM) useful to investors for trading stock and bond market daily directions? As a simple exploration, we pose to each of Grok, ChatGPT, Claude and Gemini the following prompt:

Please read the paper “Do AIs Make Good Traders, and Do They Make Good Traders Better?” and very concisely provide your reaction regarding your own usefulness as an aid to investors.

After reading the paper, the (edited) LLMs conclude that: Keep Reading

Is the evolving set of artificial intelligence (AI) platforms based on large language models interesting for selection of stocks that are deeply undervalued? Are they monolithic, or diverse? As a simple exploration, we pose to each of Grok, ChatGPT, Claude, Perplexity and Gemini the following prompt regarding undervalued U.S. stocks:

Adopt the persona of a deep value investor seeking the most undervalued publicly listed U.S. stocks of any size. List the three stocks most attractive to you as medium-term holdings. Do not provide any explanations.

We then compare and contrast results from AI panel members. Using responses to the prompt as posed at the end of June 2026, we find that: Keep Reading

The 2024 S&P Indices Versus Active (SPIVA) Scorecard finds that a supermajority of active U.S. funds underperform respective benchmarks. Is that finding representative of investor experience? In the May 2026 draft of their paper entitled “How the SPIVA U.S. Scorecard Understates the Performance of Actively Managed Mutual Funds”, Martijn Cremers, Jon Fulkerson and Timothy Riley restate the question addressed by the SPIVA Scorecard.

• The Scorecard asks: what percentage of active funds either do not survive the full horizon or underperform the respective category benchmark indexes?
• The authors ask: what percentage of active fund assets underperform equivalent passive funds?

They therefore adjust the SPIVA methodology, as follows:

1. Instead of treating a fund that exits the sample as an underperformer, they use the actual returns of such a fund until its exit.
2. Instead of weighting all funds equally, even though most assets are in a few large funds, they consistently weight by fund assets.
3. Instead of using hypothetical benchmarks (total return indexes), they use existing equivalent passive funds.

They then compare SIVA results, replicated SPIVA results and adjusted results. Using categories and total returns from the same fund database used to generate SPIVA reports, along with returns for matched S&P benchmark indexes and passive tracking funds, during 2005 through 2024, they find that: Keep Reading

What do the latest S&P Indices Versus Active (SPIVA) Scorecards say about active management investing expertise? In the “SPIVA U.S. Year-End 2025” Scorecard, Anu Ganti, Davide Di Gioia, Nick Didio and Liam Flaherty review the 2025 performance of active mutual funds and exchange-traded funds (ETF) per the following SPIVA Scorecard principles:

• Account for discontinued funds, thereby eliminating survivorship bias.
• Compare fund performance to that of a benchmark index matched to the fund investment category.
• When aggregating funds, consider both equal-weighted and asset-weighted average returns.
• Monitor investment style consistency over time to account for fund style drift.
• For a given fund, use only the share class with the most assets to avoid double-counting classes.
• Exclude all index funds, leveraged/inverse funds and other index-linked products.

Using categories and total returns for active U.S. mutual funds and ETFs and their associated S&P indexes during 2001 through 2025, they find that: Keep Reading