When Is Fresh AI Stock Analysis Worth It? Allocating a Limited Research Budget Across Price Updates, Deep Forecast Refreshes, and Editorial Reviews of Top Stock Picks

Must long-horizon AI investment research be rewritten every time stock prices move? For multi-agent platforms covering hundreds of equities, rerunning deep reasoning workflows after every market close is costly and often redundant. Solving this resource-allocation problem is the foundational reason we built iPulse AI. Using an archive of 8,120 production reports on a balanced panel of 203 stocks across four production batches (retaining ten matched agent configurations across seven investment frameworks), we measure how much decision value can be retained by updating current prices against existing forecasts before paying for fresh multi-agent inference. Across three refresh transitions, we compare (1) old price targets repriced at current market quotes (with and without a volatility-scaled shock safeguard), (2) a zero-inference mechanical reset that re-anchors old predicted growth rates to current prices, and (3) fresh deep reports. In our primary economic evaluation—selecting equal-weight Top 10 USD portfolios from one-year forecasts net of 10 bps transaction costs and reference compute charges—shock-guarded old targets beat SPY across all three short (3-month) holding windows without incurring new inference costs, averaging 2.25% versus 1.66% for SPY and 4.77% for fresh reports in unchanged-model runs. The thesis half-life question becomes visible in the longer window: extending the first holding window to nearly six months without a refresh causes the guarded reuse portfolio to fall to -11.26%, whereas fresh reports return +9.12% (SPY: 3.46%). Supporting 90-day forecast diagnostics reveal why inexpensive baselines compete so effectively over shorter windows: mechanically resetting old forecast paths to current prices eliminates 98% of the Mean Absolute Error (MAE) gap between stale targets (15.26 pp) and fresh LLM analysis (13.51 pp vs. 13.55 pp for reset), yet still turns over 60%–90% of the Top 10 shortlist. Expanding the shortlist to Top 30 reverses the unchanged-model comparison in favor of guarded reuse (4.17% vs. 1.88% for fresh), and leave-one-out stress tests highlight strong dependence on individual outlier winners (MRNA). A separate audit of nine production editions shows substantial changes in published Top 10 membership between editions, already present in their incoming candidate lists. Within each edition, the final editorial screen retained its incoming list without replacements. Finally, we measure historical token workloads and show how the capital pool sharing a research bill changes the realized break-even cost of a deep refresh. Release contents: the 26-page paper; clean LaTeX source with nine vector figures and thirteen tables; forecast-only data for 6,090 paired reports on 203 stocks; aggregate results; sanitized selection and editorial records; reproduction code and methods. Market outcome cutoff: 30 October 2026. Paper and original research data: CC BY 4.0. Focused reproduction code: MIT. Private historical market inputs and proprietary production code are excluded. See the package README and license files. Repository: https://github.com/TheFutureEdge/research-when-fresh-ai-stock-analysis-is-worth-it