# Since 2010 Stock Signals Lose Most of Their Return Before Publication > Evidence on how published equity signals lose returns before and after publication, with the weakest retention in 2010-2024 calendar returns, when unpublished signals kept about a quarter of their backtest. Twelve pages compare long-run returns, publication cohorts, data types and capacity, with selected primary sources. Published: 2026-09-29 Publisher: BlackRidge (https://blckridge.com/) Canonical: https://blckridge.com/research/signal-commoditization-20260929/ PDF: https://blckridge.com/research/signal-commoditization-20260929/signal-commoditization-20260929-en.pdf --- # Since 2010 stock signals lose most of their return before publication We tracked 208 US stock signals from the original backtest, through the unpublished years, to the period after publication. We asked whether cheaper computing makes the edge decay faster today. The numbers reveal the timeline and where an edge survives. ## After 2010 an unpublished signal kept a quarter of its backtest and published ones keep under half Since 2010 much of the edge has leaked before publication (unpublished signals kept 24.7% in 2010-2024 against 76.2% in 2000-2009) [01] . Price and trading-volume signals published since 2000 and value-weighted (big-stock) versions keep the least [01] . The timeline has shifted. McLean and Pontiff found returns 58% lower after publication, of which they attribute 32 points to publication itself [04] . ## Signal returns fell from 0.67% to 0.23% a month as published predictors grew from 14 to 208 The drop is a sudden step. It arrived in the 2000s alongside decimalization and cheaper trading [05] . The recent recovery to 0.44% a month shows the trend is not a one-way street [01] . Rolling five-year average monthly long-short return of published predictors and cumulative count, equal-weighted, before costs, 1974-2024 [01] [02] . Cards show decade or period averages. ## In 2010-2024 an unpublished signal kept 24.7% of its backtest, down from 76.2% in the 2000s A strategy used to work between the end of the backtest and publication. That grace period closed. The return fell before the paper appeared, consistent with the 26% out-of-sample decline McLean and Pontiff report [04] . The data cannot say how [01] . - Era - Unpublished, % of backtest - Signals - Published, % of backtest - Signals - Before 1990 - 36.8% - 41.0% - 1990-1999 - 90.2% - 67.1% - 2000-2009 - 76.2% - 66.3% - 2010-2024 - 24.7% - 36.4% ## Papers from 1995 or earlier kept 66.6% in their first three years, later ones about a third Recent academic signals appear to degrade faster than older discoveries. The direction of the data points toward a harsher environment for newly published research [01] . Yet a definitive conclusion eludes us: given the dispersion across predictors, the difference has a t-statistic of only about −1.2. - Published - Signals - First three years, % of backtest - Whole period, % of backtest - Backtest length, median years - 1995 or earlier - 66.6% - 57.3% - 1996-2005 - 29.5% - 44.8% - 2006-2015 - 36.1% - 35.0% ## The most-cited papers kept 49.4% after publication, the least-cited 42.3%, and signals move together no more than in 1975 If consensus formed instantly through wide reading and chatbots, the most-read signals should die first and returns should start moving together. Neither shows up. The data instead shows a flat landscape [01] . Average absolute pairwise correlation of monthly long-short returns, five-year windows, 158-207 signals [01] . - Citation group - Google Scholar citations - After publication, % of backtest - Least cited - 2–444 - 42.3% - Second - 444–948 - 49.7% - Third - 1,020–1,859 - 37.5% - Most cited - 1,859–29,625 - 49.4% ## Price and volume signals published since 2000 kept 29.7% of their backtest, accounting signals 49.9% Price and volume are the inputs every screen, script and chatbot already has. Accounting and specialized data demand cleaning, precise timing and matching. This gap is where the proprietary processing argument finds support. But the advantage stops there: pooled, specialized data did no better than standard accounting, though single groups range from 30.5% to 87.5% [01] . - Data used - Signals - After publication, % of backtest - Price - 28.7% - Trading volume - 33.2% - Options - 30.5% - Analyst forecasts - 37.5% - Accounting statements - 49.9% - Institutional holdings (13F) - 87.5% ## Weighted by size, published signals keep a quarter of their backtest and a third earn nothing Equal-weighted portfolios flatter the data by treating micro-caps exactly like large corporations. The surviving edge is concentrated where capital cannot go in size, which is a different kind of moat from better models [01] [08] . Small-stock signals may persist partly because institutional money cannot trade them at scale. ## An equal mix of five textbook factors earned 5.4% a year in 1990-2006 and 0.4% since 2007 These are the most public signals of all. They appear in every textbook and every free dataset. Profitability was published in 2006 and is the only one that kept earning [03] . Rolling ten-year annualised return, %, June and December points, 1973-August 2026 [03] . - Factor - 1963-1989, % a year - 1990-2006, % a year - 2007-2026, % a year - Value (HML) - 5.5% - 5.2% - −1.9% - Size (SMB) - 3.5% - 1.9% - −0.9% - Momentum - 9.3% - 8.9% - 0.0% - Profitability (RMW) - 1.8% - 4.1% - 2.8% - Investment (CMA) - 4.1% - 4.0% - −0.2% ## The average signal earned 0.16% a month in 2023-2024 against 0.33% in 2015-2022, too short a run to blame chatbots Peer-reviewed evidence now shows that GPT-4 news scores predict the subsequent price drift, and that strategy returns decline as LLM adoption rises [06] . Our two post-chatbot years look weak, yet they match the near-zero returns of 2017-2020 [01] . Equal-weighted average long-short return, % a month, by year, 1990-2024, before costs [01] ## In 2010-2024 unpublished signals kept a quarter of their backtest; after publication price, volume and big-stock signals kept least The data trace precisely when and where equity predictors lose their edge, without isolating a singular cause. In 2010-2024 calendar returns, signals not yet published kept only a quarter of their backtest. Returns drop across the board. The fall is steepest in the largest stocks where the heaviest capital trades. - Measure - What it shows - Value - Post-publication, all signals - Portion of the original backtest return retained - 44.7% - Pre-publication, 2010-2024 - Retained return before the paper appeared - 24.7% - Pre-publication, 2000-2009 - Historical edge retained prior to publication - 76.2% - Price and volume signals since 2000 - Return kept by price and volume predictors - 29.7% - Accounting signals since 2000 - Return kept by fundamentals-based predictors - 49.9% - Value-weighted, post-publication - Retained backtest return in the largest stocks - 24.8% Before paying for a systematic signal, demand to see where the data comes from and how managers process it. Verify its performance in big stocks and its track record in the years before anyone published. ## Selected sources We provide independent research, not investment advice. All returns are gross of costs and taxes. Past decay does not bound how fast signals might fade in the future. ## Keep up with the research. Choose optional research updates, or learn how the account structure works. ## Independent research, introductions clearly defined. BlackRidge is an independent research bureau introducing private investors to quantitative traders through multiple strategy providers. We publish quantitative research. Investors access strategies through PAMM accounts at the broker. We are not a fund or broker and never hold client money. Your funds stay at the broker. The account is opened in your own name. BlackRidge never receives, holds, or has withdrawal rights over client capital. No fee on your profits. A one-time access payment of 6.7% of the agreed trading level, paid to the selected strategy provider. No management fee, no performance fee, no profit share, in any year. You fund the risk, not the exposure. Allocations are notionally funded: you agree a trading level and fund the margin and drawdown allowance behind it. Trading losses can exceed the deposit without applicable negative balance protection; the separate 6.7% access payment is nonrefundable. blckridge.com/research : published research. Notional funding and PAMM accounts explained in full, and answers to the questions this raises. We use AI models to gather and aggregate source material and to help prepare each report. Read it with its cited sources, sample, methods and limitations, and send corrections through research methodology and corrections . The client pays the selected strategy provider a one-time access payment of 6.7% of the agreed trading level. BlackRidge receives an introduction fee from that provider and does not receive broker compensation. About BlackRidge . Minimum funded capital $25,000. The strategy, its live records, the broker, the selected strategy provider and the full commercial terms are presented on an introductory call and confirmed in writing before any payment or deposit. This report is published for information only. It is not investment advice and does not take account of your circumstances. Trading leveraged instruments including CFDs carries substantial risk and is not suitable for all investors; you may lose the capital you fund and, depending on your broker's terms, may owe more than your deposit. Findings in this report may combine third-party evidence, historical calculations and illustrative scenarios; they are not verified live trading results of any strategy provider. Past performance does not indicate future results. ## Sources - [01] [Open Source Asset Pricing, data, October 2025 release](https://www.openassetpricing.com/data/). We extract monthly long-short returns for 212 stock predictors. The file logs sample and publication years alongside data categories, citations and value-weighted versions. - [02] [Chen and Zimmermann, Open Source Cross-Sectional Asset Pricing](https://www.federalreserve.gov/econres/feds/files/2021-037pap.pdf). This paper provides the exact replication method used to build the underlying portfolios. - [03] [Kenneth French Data Library](https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html). We pull returns for five core factors and momentum through August 2026. - [04] [McLean and Pontiff, Does Academic Research Destroy Stock Return Predictability?, Journal of Finance 2016](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2156623). The authors define the pooling method we use. They found returns drop 58% after publication, and they attribute 32 points of that decline directly to the paper going public. - [05] [Chordia, Subrahmanyam and Tong, Have capital market anomalies attenuated in the recent era of high liquidity and trading activity?, Journal of Accounting and Economics 2014](https://econpapers.repec.org/RePEc:eee:jaecon:v:58:y:2014:i:1:p:41-58). Strategy returns fell by roughly half after the 2001 switch to decimal pricing. - [06] [Lopez-Lira and Tang, Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models, Journal of Financial Economics 2026](https://arxiv.org/abs/2304.07619). Language models hit about a 90% success rate predicting the initial reaction to news. Strategy returns fall as adoption spreads. - [07] [Jacobs and Mueller, Anomalies across the globe: Once public, no longer existent?, Journal of Financial Economics 2020](https://www.sciencedirect.com/science/article/pii/S0304405X19301618). The researchers test 241 anomalies across 39 distinct markets. The United States is the only market with a reliable post-publication decline. - [08] [Novy-Marx and Velikov, A Taxonomy of Anomalies and Their Trading Costs, NBER working paper 20721, Review of Financial Studies 2016](https://www.nber.org/papers/w20721). Strategies keep their returns net of costs mostly when one-sided monthly turnover stays below 50%. - [09] [Jensen, Kelly and Pedersen, Is There a Replication Crisis in Finance?, Journal of Finance 2023, and the Global Factor Data site](https://jkpfactors.com/). The authors evaluate 153 factors across 93 countries. Most of them replicate. - [10] [Brogaard, Nguyen, Putnins and Zhang, What Drives Anomaly Decay?, working paper 2024](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4934042). Decay clusters around structural jumps in market liquidity. --- ## Citation context Since 2010 much of the edge has leaked before publication: unpublished signals kept 24.7% of their return in 2010-2024, against 76.2% in 2000-2009. Price and trading-volume signals published since 2000, and value-weighted (big-stock) versions, keep the least. Sample and method: published equity-signal returns. In 2010-2024 calendar returns, unpublished signals kept only about a quarter of their backtest. The report compares long-run returns, publication cohorts, data types and capacity. Limits: the pre-publication window may include circulation of working papers, so it does not mark when the signal became tradeable. Returns are before costs. The cause is not identified. Jacobs and Mueller find a reliable post-publication decline only in the US. Primary input: [Open Source Asset Pricing data, October 2025 release](https://www.openassetpricing.com/data/). Stable permalink: [https://blckridge.com/research/signal-commoditization-20260929/#citation-context](https://blckridge.com/research/signal-commoditization-20260929/#citation-context). ## Read next - [Backtests Are the Most Optimistic Estimates a Strategy Will Get](/research/forward-validation-20260928/). Compare pre-publication signal erosion with a separate study of reconstructed backtest, forward-window and post-publication returns.