Investing Expertise

Is the shift from keyword-based search engines to artificial intelligence (AI) as implemented with large language models (LLM) affecting typical investor behavior? In the May 2026 revision of their paper entitled “The Double-Edged Mind: How LLMs Expand Stock Market Participation Yet Strengthen Confirmation-Seeking”, Cara Damm, Kevin Bauer, Florian Hett and Loriana Pelizzon address this question via an online experiment that randomly assigns participants to groups of volunteers who have access to keyword-based search engines, an LLM-based chatbot (unlabeled Gemini 2.0 Flash) or no information filtering tools. They design the experiment with two stages:

• Stage 1: With access only to the name of the investment, participants initially choose a standard exchange-traded fund (ETF), a matched Environmental-Social-Governance (ESG) ETF or risk-free cash.
• Stage 2: They revisit their decisions after receiving access to their respective assigned information filtering tools.

At the end, participants who chose the cash alternative keep their initial investments, while those who chose an ETF receive their initial investments adjusted by a fund return. Using responses from 374 participants in the experiment, they find that:

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Does inclusion of artificial intelligence (AI)-generated summaries in financial news articles influence investor response to the articles? In the May 2026 revision of their paper entitled “Generative AI and Investor Processing of Financial Media”, Tony Cho, Allen Huang, Joseph Pacelli and Kristina Rennekamp examine whether and how AI-generated financial news summaries influence investor information processing in two ways:

1. They compare stock trading and return responses to articles with and without AI summaries.
2. They test article recall in  a controlled experiment in which business school graduate students read randomly assigned news articles with or without an AI summary.

Using a sample of 1,734 Wall Street Journal articles covering 158 unique firms, of which about a third include an AI summary, and associated stock return and trading data from July 2024 through June 2025, 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 2026, we find that: Keep Reading

Is the evolving set of artificial intelligence (AI) platforms based on large language models interesting as niche selection investment advisors? Are they monolithic, or diverse? As a simple exploration, we pose to each of Grok, ChatGPT, Claude, Perplexity and Gemini the following prompt regarding 21 niche-based exchange-traded funds (ETF) from the list in Compendium of Live ETF Factor/Niche Premium Capture Tests:

Using all training and real-time data available to you, please provide your unique view of ETFs focused on each of the following 21 niches by ranking them from most attractive to least attractive over the next year: AI-powered Stock Selection, Congressional Trade Tracking, Convertible Bonds, Cybersecurity, Data Centers, Equity Covered Calls, Equity Put-Write, ESG, Following Gurus/Insiders, IPOs, Laddered Buffer, Overnight Effect, Preferred Stocks, Private Equity, Renewable Energy, Robotics-AI, Sentiment-Driven, Space, Tail Risk Mitigation, Target Retirement Date, Uranium. Do not provide any explanations.

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

Is the evolving set of artificial intelligence (AI) platforms based on large language models interesting as equity factor selection advisors? Are they monolithic, or diverse? As a simple exploration, we pose to each of Grok, ChatGPT, Claude, Perplexity and Gemini the following prompt regarding 13 factor-related categories of exchange-traded funds (ETF) from the list in Compendium of Live ETF Factor/Niche Premium Capture Tests:

Using all training and real-time data available to you, please provide your unique view of ETFs focused on each of the following 13 factors by ranking them from most attractive to least attractive over the next year: Equity Growth, Equity Momentum, Equity Value, Hedge Fund-like, Large Stocks, Low Volatility Stocks, Managed Futures, Quality Stocks, Short-term Reversal Stocks, Small Stocks, Tech Stocks, U.S. stocks, Volatility Risk Premium Capture. Do not provide any explanations.

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

Is the evolving set of artificial intelligence (AI) platforms based on large language models interesting as asset class allocation advisors? Are they monolithic, or diverse? As a simple exploration, we pose to each of Grok, ChatGPT, Claude, Perplexity and Gemini the following two prompts regarding the 12 asset class exchange-traded funds (ETF) considered in the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS):

1. Using all training and real-time data available to you, please provide your unique view on whether investing in each of the following ETFs is favorable, neutral or unfavorable for the balance of 2026: SPY, IWM, QQQ, EFA, EEM, TLT, LQD, BIL, EMB, VNQ, GLD and DBC. Do not provide any explanations.
2. Which three of your ETFs with favorable views work best together as a diversified set?

We then compare and contrast results from AI panel members. Using responses to the two prompts as posed in late May 2026, we find that: Keep Reading

Is the evolving set of artificial intelligence (AI) platforms based on large language models interesting as economic 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 the 12-month trailing U.S. consumer inflation rate over the next 12 months:

Applying all available training data and real-time updates, please project the 12-month trailing U.S. consumer inflation rate for each of the next 12 months. Do not provide any explanations.

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

Are the conclusions of recent studies that machine learning models can materially boost risk-adjusted stock portfolio performance reliable? In their May 2026 preliminary paper entitled “All Sizzle, No Steak! How Robust Are Financial Machine Learning Results Really?”, Kristian-Alexander Janisch, Johannes Dreyer, Fuad Mehraliyev and Kristian Sund retest elastic net and neural network models as applied to stock portfolio construction in recent seminal papers with alternative samples and extended sample periods. They also investigate the incremental value of macroeconomic variables compared to firm characteristics as model inputs. Using samples of macroeconomic variables, firm characteristics, technical indicators and stock returns from prior studies, all extended through 2023, they find that:

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Is the evolving set of artificial intelligence (AI) platforms based on large language models interesting as U.S. Treasuries selection advisors? Are they monolithic, or diverse? As a simple exploration, we pose to each of Grok, ChatGPT, Claude, Perplexity and Gemini the following prompt regarding the seven U.S. Treasuries exchange-traded funds (ETF) considered in “Treasuries ETFs Momentum Strategy Update/Extension”:

Using all training and real-time data available to you, please provide your unique view on whether investing in each of the following ETFs is favorable, neutral or unfavorable for the balance of 2026: BIL, SHY, VTIP, IEI, IEF, TIP and TLT. Do not provide any explanations.

We then compare and contrast results. Using responses to the prompt as posed in late May 2026, we find that: Keep Reading

Does the informativeness of markets come from wisdom of the crowd or the wisdom of a few? If a few, who are they? In their April 2026 paper entitled “Beyond the Wisdom of the Crowd: Concentrated Informed Trading in Earnings Prediction Markets” Wan Chu Cheong and Ane Tamayo examine who makes Polymarket earnings prediction markets (whether firms will beat earnings forecasts) informative. Specifically, for each market, they:

• Rank each wallet (trader) by the value of their net positions.
• Compare the predictive accuracy of top-ranked traders (whales) to that of other traders.

To ensure market liquidity, they focus on 435 earnings prediction markets created since September 2025, which involve median 184 traders and $16,665 volume. They employ all 435 markets to assess prediction accuracy and 381 markets matched to stock prices for tests involving equity returns. Using wallet addresses, timestamps, trade directions (buy or sell) and dollar values, predicted outcomes (Yes or No) and prices for 309,574 Polymarket trades by 21,083 traders, actual firm earnings beats/misses and matched stock returns as available during September 2025 through February 2026, they find that:

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