Fundamental Valuation

Are prediction markets better at forecasting firm earnings than professional analysts? In their April 2026 paper entitled “Beating the Earnings Game: Why Do Prediction Markets Outperform Professional Analysts?”, Daniel Rabetti, Jiaqi Shao and Che Zhang investigate whether and, if so, why a blockchain-based prediction market such as Polymarket outperforms professional analysts in forecasting U.S. stock earnings. The earnings predictions of this market are public and unchangeable contracts, taking the form:

“Will [Company] beat earnings for [Quarter] [Fiscal Year]?”

relative to analyst consensus as of contract creation date. Using data for 469 Polymarket firm-quarter earnings beat contracts, corresponding analyst earnings forecast data and associated daily stock prices during September 2025 through February 2026, they find that:

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The home page, Simple Asset Class ETF Momentum Strategy (SACEMS) and Simple Asset Class ETF Value Strategy (SACEVS) now show preliminary positions for May 2026. SACEMS rankings probably will not change by the close. SACEVS allocations are unlikely to change by the close.

Will the relatively high U.S. stock valuation ratios observed over the past few decades revert, or are they persistent artifacts of fundamental shifts in the U.S. economy? In their January 2026 paper entitled “A Macroeconomic Perspective on Stock Market Valuation Ratios”, Andrew Atkeson, Jonathan Heathcote and Fabrizio Perri examine the interplay between economic data (share of labor in corporate output and corporate investment/capital base) and stock market valuation ratios. They derive aggregate U.S. corporate value from the Integrated Macroeconomic Accounts (IMA). Their measure of enterprise value differs from stock market capitalization in two ways:

1. It is insensitive to the mix of debt and equity used for firm financing.
2. It includes estimated value U.S. subsidiaries of foreign multinationals and excludes estimated value of the foreign subsidiaries of U.S. multinationals. Thus, it measures the value of entities filing U.S. corporate tax returns.

Using U.S. economic and corporate valuation data during January 1952 through September 2025, they find that: Keep Reading

We are making two changes to CXOadvisory.com methodology:

1. The Social Science Research Network (SSRN) is our principal source of research relevant to individual investors. We monitor relevant parts of SSRN to discover such research. However, with widespread use of Large Language Models (LLM), inputs to SSRN seem to have multiplied and become, on the whole, much less relevant to our use. To address this lower signal-to-noise ratio, we are implementing a separate “Investing Research Sweep” via Claude to discover important ideas across the web.
2. S&P 500 12-month trailing operating earnings yield is a key input to the Simple Asset Class ETF Value Strategy (SACEVS) model. Until the end of January 2026, S&P Global for many years made up-to-date S&P 500 earnings publicly available. Starting with the fourth quarter of 2025, we are experimenting with LLMs to estimate S&P 500 operating earnings (Grok corroborated by Claude). We dovetail these estimates with prior data from S&P Global. With this change, we will update the earnings estimates in SACEVS more frequently than quarterly. Though SACEVS is usually a slow-changing model, this change could increase allocation volatility.

We will audit results of these changes for plausibility and usefulness.

Are there cryptocurrencies that are so alike that they generally track each other and reliably revert whenever they diverge? In their December 2025 paper entitled “Pairs Trading in Crypto”, Sasha Stoikov, Dora Xu, Shijie Shao, Yourui Wang, Tongshu Zhang and Jinxuan Hu show how to identify cryptocurrency pairs with stable relationships and execute mean reversion strategies in real time. Specifically, they:

• Identify pairs to trade by combining correlation behaviors, structural metadata and stability diagnostics.
• Generate entry thresholds, exit rules and risk controls (stop-loss, pair suspension and pair abandonment) for long-short trades that exploit overvaluation and undervaluation of pairs based on rolling window divergences.

Starting with hourly data for a sample of 543 cryptocurrency perpetual futures contract series (147,153 potential pairs), with 800 days through February 2025 as a training set and March through September 2025 as a test set (plus some short live tests during late November and early December 2025), they find that:

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Do firms that spend big on research and development (R&D) relative to revenue outperform in the future? In his January 2026 paper entitled “R&D Alpha: Investment Intensity and Long-Term Stock Returns”, flagged by a subscriber, Abhishek Sehgal investigates the relationship between firm R&D intensity and future returns for large-capitalization U.S. stocks (S&P 500, as historically added). Specifically, he each year:

• Sorts S&P 500 stocks into fifths (quintiles) by end-of-fiscal year (usually December) R&D intensity.
• Computes equal-weighted average annual (end of the following June) returns by quintile, and returns for a hedge portfolio that is long the highest and short the lowest quintile.
• Computes annual (end of June) returns for a simple long-only portfolio that holds the equal-weighted top 20 stocks by R&D intensity, including 0.027% baseline trading frictions.

Using monthly data for July 1995 through June 2025, he finds that:

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The conventional Cyclically Adjusted Price-Earnings ratio (CAPE), or P/E10, divides current real S&P 500 Index level by average annual aggregate real index earnings as reported over the prior 10 years. This approach ignores stocks added to or deleted from the index during those 10 years. Is there a more timely and useful way to aggregate earnings? In their January 2026 paper entitled “CAPE Ratios and Long-Term Returns”, Rui Ma, Ben Marshall, Nhut Nguyen and Nuttawat Visaltanachoti introduce Component CAPE, which each year divides current real S&P 500 Index level by historical annual real reported earnings for current S&P 500 Index stocks weighted by market capitalization.  They consider six ways to aggregate earnings:

1. Average over the last 10 years.
2. Average over the last five years.
3. Exponentially weighted moving average (EWMA) over 10 years.
4. Total Return CAPE, accounting for share buybacks/dividends over 10 years.
5. Payout-adjusted CAPE, adjusting for level of retained earnings over 10 years.
6. Operating earnings (excluding one-time items within as-reported earnings) over 10 years.

They measure CAPE performance via predictive regressions with a constant predictive slope coefficient to calculate out-of-sample (OOS) R-squared, which relates the mean squared error of 10-year return predicted by CAPE to that predicted by historical average return. They start CAPE calculations in 1964, with OOS predictions commencing in 1974. They account for data snooping bias from multiple tests (two CAPEs and six ways to aggregate earnings) using two separate corrections. To assess economic value of predictions, they calculate the certainty equivalence return (CER) for quadratic risk aversion of an allocation strategy based on CAPE signals. Using S&P 500 Index level and aggregate earnings data and annual price and earnings data for individual S&P 500 components during 1955 through 2024, they find that: Keep Reading

Is U.S. equity market valuation outrunning its productive value? For perspective, we compare the trajectories of S&P 500 (SP500) index, earnings and dividends over recent decades and look at some potential explanations for divergences. Using quarterly SP500 data and 10-year U.S. Treasury note (T-note) yield during March 1988 through September 2025 and Shiller data as available through November 2025, we find that: Keep Reading

A subscriber asked about the performance of a 50-50 combination of a basket of momentum stock exchange-traded funds (ETF) and a basket of quality stock ETFs, specifically with comparison to a 50-50 combination of the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS). To investigate, we employ results from:

• The EW portfolio of momentum ETFs in “Are Equity Momentum ETFs Working?” as the basket of momentum ETFs.
• The equal-weighted (EW) portfolio of quality ETFs in “Are Stock Quality ETFs Working?” as the basket of quality ETFs.
• The 50-50 SACEVS Best Value-SACEMS EW Top 2 portfolio.

We assume monthly rebalancing of the 50-50 momentum-quality portfolio. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). We also use SPDR S&P 500 ETF (SPY) to assess effectiveness of the factor portfolios. Using monthly total returns from the above three sources and SPY during April 2007 (limited by momentum ETF data) through October 2025, we find that: Keep Reading

Are stock quality strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider six ETFs, all currently available (from oldest to youngest):

• Invesco S&P 500 Quality ETF (SPHQ) – seeks to track performance of S&P 500 stocks with the highest quality scores based on firm return on equity, accruals ratio and financial leverage ratio, reformed semi-annually. The benchmark is SPDR S&P 500 (SPY).
• iShares Edge MSCI USA Quality Factor ETF (QUAL) – seeks to track performance of U.S. large-capitalization and mid-capitalization stocks selected based return on firm equity, earnings variability and debt-to-equity. The benchmark is SPY.
• iShares Edge MSCI Intl Quality Factor ETF (IQLT) – seeks to track performance of large-capitalization and mid-capitalization developed international stocks screened for attractive return-on-equity, earnings variability and debt-to-equity. The benchmark is iShares MSCI ACWI ex U.S. ETF (ACWX).
• Fidelity Quality Factor ETF (FQAL) – seeks to track performance of U.S. large-capitalization and mid-capitalization stocks with a higher firm quality profile than the broader market. The benchmark is Vanguard Russell 1000 Index Fund ETF (VONE).
• JPMorgan U.S. Quality Factor ETF (JQUA) – designed to provide domestic equity exposure with a focus on companies with strong quality and profitability characteristics and the potential to enhance returns. The benchmark is VONE.
• Vanguard U.S. Quality Factor ETF (VFQY) – applies a rules-based quantitative model to select U.S. common stocks with strong fundamentals (strong profitability and healthy balance sheets) across market capitalizations, sectors and industry groups. The benchmark is iShares Russell 3000 ETF (IWV).

We calculate monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the stock quality ETFs and benchmarks as available through October 2025, we find that:

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