Investing Expertise

Is growing investor/trader use of large language models (LLM) extinguishing known stock return anomalies? In their March 2026 paper entitled “Do LLMs Make Markets More Efficient?”, Runjing Lu, Yongxin Xu and Luka Vulicevic examine how use of LLMs is affecting reactions of individual stocks to recent newsworthy events with and without outages of LLMs from three major providers (ChatGPT, Claude and Gemini). Together, these three account for nearly 80% of LLM usage. They classify outages as (1) any, (2) single-provider severe or (3) multi-provider, as documented by each provider. They focus on outages that coincide with news releases and persist beyond the NYSE close. They use RavenPack Event Sentiment Scores for articles from the Dow Jones Newswire that have ticker-specific relevance scores above 75. They control for time-varying stock/firm characteristics, past returns, new type and calendar effects. They measure daily abnormal stock returns relative to those of a characteristic-matched benchmark portfolio. Using daily outage, stock/firm and news/sentiment data during March 2023 through November 2025, they find that: Keep Reading

Are large language models (LLM) robust financial advisors for individuals? In their March 2026 paper entitled “AI Financial Advice: Supply, Demand, and Life Cycle Implications”, Taha Choukhmane, Tim de Silva, Weidong Lin and Matthew Akuzawa examine the personal financial advice from LLMs. They mainly use GPT-5.2 but repeat analyses using Gemini 3 Flash as a robustness check. Specifically, they:

• Construct a life cycle model of income/spending/saving/investment, with labor market shocks and asset returns calibrated to U.S. data.
• Collect questions (prompts) from a demographically representative sample of about 1,000 U.S. adults about spending and investing, including summaries of respective financial situations.
• Simulate life cycle paths of individuals for each year from ages 22 to 90 who follow two-pass advice in LLM responses to prompts from survey participants matched by age, income and employment status. The first pass solicits textual advice, and the second translates text to quantified saving, spending and asset allocation recommendations.

They consider two benchmarks: (1) the optimal behaviors for the life cycle model simulations; and, (2) substitution of survey respondent prompts with expert (academic) prompts that ask the LLM to give professional life cycle advice under modern portfolio theory, including explicit personal situations/economic assumptions. Using the specified life cycle model and LLM prompts, they find that: Keep Reading

How strongly do profits concentrate among winners in zero-sum prediction market trading? In their March 2026 paper entitled “Who Wins and Who Loses In Prediction Markets? Evidence from Polymarket”, Pat Akey, Vincent Grégoire, Nicolas Harvie and Charles Martineau examine trading profits and losses on Polymarket, the world’s largest prediction market, to measure:

• Profit concentration.
• The link between prediction accuracy and profitability.
• Characteristics of profitable and unprofitable trading.

Using the complete Polymarket transaction history (about 70 million trades by 1.4 million users) during November 2022 through October 2025, they find that: Keep Reading

Can autonomous artificial intelligence (Agentic AI), which interprets market dynamics with continuous improvement and specifies resulting trades with minimal human intervention, run an attractive portfolio? In their March 2026 paper entitled “Beyond Prompting: An Autonomous Framework for Systematic Factor Investing via Agentic AI”, Allen Huang and Zheqi Fan employ Agentic AI as a self-directed quantitative researcher that translates a high-level objective, such as maximizing risk-adjusted returns while controlling for turnover/transaction costs, into buy/sell/hold decisions. Specifically, their Agentic AI model each day:

• Permutes 10 baseline variables (lagged stock return, market return, stock price, trading volume, volume relative to recent history, 20-day realized volatility, price-to-moving-average ratio, market volatility, volume growth and bid-ask spread) to discover novel candidate factors that exploitably predict next-day stock returns.
• Backtests the candidate factors and assesses their economic rationale.
• Derives stock buy/sell/hold decisions from surviving candidates.
• Updates its memory based on empirical feedback.

The Agentic AI model mitigates data snooping bias by requiring economic rationale, adjusting for multiple hypothesis testing and evaluating out-of-sample signal decay. They use historical data through December 2020 to train the model and 2021-2024 data for out-of-sample testing. They assume 0.03% trading frictions (commission plus bid-ask spread) to assess net performance. Using daily data for a broad sample of U.S. common stocks priced at least $5 and excluding extreme outliers during January 2004 through December 2024, they find that:

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Does Michael Farr, CEO and founder of Farr, Miller & Washington, offer good stock picks via his annual CNBC articles identifying the best 10 stocks for the next year? To investigate, we take his picks for 2022, 2023, 2024 and 2025, calculate the associated annual total returns for each stock and compute the equal-weighted average return for the 10 stocks for each year. We use SPDR S&P 500 ETF Trust (SPY) as a benchmark for these averages. Using year-end dividend-adjusted stock prices for the specified stocks-years, we find that: Keep Reading

Should users of artificial intelligence (AI), as implemented via Large Language Models (LLM) with latitude to operate independently, expect good treatment? In their February 2026 paper entitled “Agents of Chaos”, a large research team reports results from two weeks of intensive, realistic interactions between 20 researchers and largely autonomous LLMs. Autonomy means that the LLM has system administrator rights to its own server/storage and access to dedicated Discord and email accounts for interactions with its owner (a human) and non-owners (human and LLM). The principal goal of the 20 interacting researchers was to break (induce problematic behaviors from) the autonomous LLMs. Much of the paper is in case study format. Based on outputs of the two weeks of interactions, they conclude that: Keep Reading

Are plans to use nuclear power to provide electricity for proliferating data centers driving attractive performance for uranium exchange-traded-funds (ETF)? To investigate, we consider four such ETFs, all currently available:

• VanEck Uranium and Nuclear ETF ETF (NLR) – picks stocks and depositary receipts of firms involved in uranium and nuclear energy.
• Global X Uranium ETF (URA) – picks stocks of global companies involved in the uranium industry.
• Sprott Uranium Miners ETF (URNM) – picks stocks of firms devoting at least 50% of assets to mining of uranium, holding physical uranium, owning uranium royalties or engaging in other activities that support uranium mining.
• Sprott Junior Uranium Miners ETF (URNJ) – picks stocks of small firms devoting at least 50% of assets to mining of uranium, holding physical uranium, owning uranium royalties or engaging in other activities that support uranium mining.

We use Energy Select Sector SPDR Fund (XLE) as a benchmark. We also look at some performance results for SPDR S&P 500 ETF Trust (SPY) for perspective. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly total returns for the four uranium ETFs as available and for XLE and SPY through February 2026, we find that: Keep Reading

Which is more important, knowing what direction to trade or knowing when to enter a trade? In his February 2026 paper entitled “Who Profits from Prediction Markets? Execution, not Information”, Joshua Della Vedova decomposes prediction market trade returns into:

• Directional component – whether the trader predicted the winning outcome.
• Execution component – entry price relative to final value.

Prediction markets enable skill measurements of both based on binary outcomes and no benchmarks. He considers five types of traders based on wallet activity/size/volume:

1. Bot (73,935): >50 trades per day or >1,000 total trades.
2. Sophisticated (64,913): >$10,000 volume, diversified across markets and >30 days of active trading.
3. Active Retail (1,305,716): 10 to 1,000 trades.
4. Casual (421,983): 2 to 9 trades.
5.  One-shot (114,861): exactly one trade.

Using data for 222 million completed trades on Polymarket during November 2022 through part of February 2026, he finds that: Keep Reading

Does finance professor David Kass, who presents annual lists of stock picks on Seeking Alpha, make good selections? To investigate, we consider his picks of “10 Stocks for 2020”, “16 Stocks For 2021”, “12 Stocks For 2022”, “10 Stocks For 2023”, “10 Stocks For 2024” and “10 Stocks For 2025”. For each year and each stock, we compute total (dividend-adjusted) return. For each year, we then compare the average (equal-weighted) total return for a David Kass portfolio to that for both SPDR S&P 500 ETF Trust (SPY) and Invesco QQQ Trust (QQQ). Using dividend-adjusted returns from Yahoo!Finance for SPY and most stock picks and returns from Barchart.com and Investing.com for three picks during their selection years, we find that: Keep Reading

Do “great stock picks” from Forbes beat the market? To investigate, we evaluate stock picks for 2022, 2023, 2024 and via  “10 Great Stock Picks for 2022 from Top-Performing Fund Managers”, “20 Great Stock Ideas for 2023 from Top-Performing Fund Managers”, “10 Best Stocks For 2024” and “The Best Stocks To Buy Now For 2026”. For each year and each stock, we compute total (dividend-adjusted) return. For each year, we then compare the average (equal-weighted) total return for a Forbes picks portfolio to that of SPDR S&P 500 ETF Trust (SPY). Using end-of-year dividend adjusted prices from Yahoo!Finance for the specified years/stocks through 2025, we find that: Keep Reading