How does the evolving set of artificial intelligence (AI) platforms based on large language models view the likely winners of the AI platform competition. To investigate, we pose to each of Grok, ChatGPT, Claude, Perplexity and Gemini the following prompt:

Based on up-to-date market data, please concisely estimate which one or two AI platforms are most likely to achieve and sustain dominant usage over the next three years among: (1) consumers; and, (2) enterprises.

Compiling and comparing responses from the AI panel to the prompt as posed at the end of October 2026, we find that:

Do artificial intelligence (AI) platforms based on large language models offer a quick way to identify potentially exploitable technical trading rules in financial series? As a simple exploration, we pose to Claude the following prompt:

Please examine the attached daily GLD dataset and concisely identify any technical signals, including indicators involving trend/momentum, volatility, volume or support/resistance, that investors can reliably exploit on a net basis.

Using Claude’s response to the prompt as posed with daily open, high, low and close prices and trading volume for SPDR Gold Shares (GLD) during November 2004 through October 2026, we find that:

The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for October 2026. The actual total (core) inflation rate is slightly higher than (slightly lower than) forecasted.

What lessons are emerging from individual investor use of artificial intelligence (AI) platforms based on large language models (LLM) as an aid? As a simple exploration, we ask each of Grok, ChatGPT, Claude, Perplexity and Gemini to search for examples of individuals using LLMs to guide investments and provide a very concise set of lessons learned from their experiences. Using responses from these AIs to this request as posed in mid-October 2026, we find that:

How should investors view financial advice from artificial intelligence (AI) models based on large language models, which have neither finance credentials nor investing track records? In their October 2026 paper entitled “Judge for Yourself: Confidence Beats Accuracy in AI Financial Advice”, Golnaz Bahrami, Anthony Cookson and Austin Moss study advice preferences on Chatbot Arena, where users vote on answers to same question from two randomly assigned and anonymous AI models. Model anonymity guarantees that users do not vote based on AI reputation. Votes may indicate a decisive preference between the two answers, or they may result in a tie (both answers good or bad). For each AI answer, they task Claude Sonnet 5 to: (1) grade its accuracy based on Certified Financial Planner Board practice standards and life cycle prescriptions; and, (2) assess AI confidence in and actionability of the answer. Using votes on 3,060 financial questions (2,212 of which are decisive) from two public releases of Chatbot Arena full conversations during June to August 2024 and April to July 2025 and Claude answer assessments, they find that:

Below is a weekly summary of our research findings for 11/2/26 through 11/6/26. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list.

Does the evolving set of artificial intelligence (AI) platforms based on large language models offer a quick way to evaluate an active exchange-traded fund (ETF) that “seeks to offer a distinct source of monthly income by selling a 30-barrier put option?” As a simple exploration, we pose to each of Grok, ChatGPT, Claude, Perplexity and Gemini the following prompt:

Please review the attached documents and recommend whether an investor seeking income with typical risk aversion should allocate to Simplify Barrier Income ETF (SBAR) rather than some other ETF.

The attached documents are: (1) the SBAR Summary Prospectus of November 2025; (2) the SBAR Fact Sheet 3Q26; and, (3) the full SBAR price history. Compiling and comparing responses from the AI panel to the prompt as posed in late October 2026, we find that:

In their October 2026 paper entitled “When Price Charts Rhyme: Historical Visual Retrieval for Stock Return Prediction”, Hui Chen, Haoyu Liu, Ke Wu and Dexin Zhou report gross annualized hedge portfolio returns as high as 85% and gross annualized Sharpe ratios as high as 6.37 from strategies that predict returns by matching current price patterns to past patterns/subsequent returns. Their sample runs from 1993 through 2025. Their hedge portfolio is long (short) the tenth, or decile, of stocks with the highest (lowest) predicted returns, reformed weekly. Can an artificial intelligence (AI) model quickly summarize the practical import of these findings. To investigate, we provide the paper to Claude and simply ask: “Are the returns and Sharpe ratios reported in this paper possible?” Using Claude’s response to this open-ended question and a follow-up specific question, we find that:

Is aggregate net U.S. public company stock buyback activity a useful predictor of U.S. stock market return? To investigate, we:

• Retrieve Nonfinancial Corporate Business; Corporate Equities; Liability, Transactions and multiply values by -1 as a quarterly aggregate net stock buyback proxy (stock pulled from the market).
• Retrieve Nonfinancial Corporate Business; Corporate Equities; Liability, Level as a quarterly equity market size proxy.
• Estimate the market impact of net buybacks by dividing the buyback proxy by the market size proxy (net buyback ratio).
• Retrieve quarterly S&P 500 Index (SP500) levels to compute contemporaneous returns.
• Calculate quarterly lead-lag correlations between the net buyback ratio and SP500 returns.
• Construct average next-quarter and two-quarters-hence SP500 returns by ranked fifth (quintile) of quarterly net buyback ratio.

Using quarterly data for the specified series as available during December 1951 through September 2026, we find that:

Is there a material and reliable market capitalization (size) effect among U.S. stocks around the turn-of-the-year (TOTY)? To check, we track cumulative returns from 20 trading days before through 20 trading days after the end of the calendar year for the Russell 2000 Index and the S&P 500 Index since the inception of the Russell 2000 Index. We also look at full-month December and January returns for these indexes. Using daily and monthly levels of the two indexes during December 1987 through January 2026 (39 December and 39 January observations), we find that: