← 研踪 PaperTrailarXiv:2611.04348 · v1
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
首次提交 2026-11-03 13:30
版本更新 2026-11-03 13:30
雷达观测 2026-11-04 23:40
摘要
Must long-horizon AI investment research be rewritten every time stock prices move? Using 8,120 production reports on the same 203 stocks across four iPulse AI batches, we compare repricing existing targets, guarding them against price shocks, mechanically resetting old forecast growth, and generating fresh multi-agent reports. Ten matched configurations represent seven investment frameworks. Our primary economic evaluation selects Top 10 USD portfolios using one-year forecasts. Across two unchanged-model windows, guarded reuse averages 2.25%, versus 1.66% for SPY and 4.77% for fresh reports, after modeled trading costs and reference compute charges on a USD 100,000 account. Guarded reuse beats SPY in all three short windows, including the model-change transition. Extending the first holding window to nearly six months reverses the result: guarded reuse returns -11.26%, fresh reports +9.12%, and SPY +3.46%, motivating the thesis half-life question. Supporting quarter-forecast diagnostics show that mechanically re-anchoring old growth paths closes 98% of the MAE improvement over fixed targets, yet Fresh and Reset share only 1-4 of their Top 10 stocks. At Top 30, the unchanged-model portfolio comparison instead favors guarded reuse (4.17% versus 1.88%). Endpoint and concentration tests reveal the influence of horizon choices and exceptional winners. Nine production editions show substantial between-edition turnover, while every final editorial screen retains its own incoming list. Measured token workloads connect refresh costs to shared capital and realized break-even budgets. The resulting architecture separates continuous price monitoring from episodic thesis rebuilding and targeted editorial evidence checks.
作者备注：26 pages, 9 figures, 13 tables. Paper and research artifacts: https://doi.org/10.5281/zenodo.23907031 . Code: https://github.com/TheFutureEdge/research-when-fresh-ai-stock-analysis-is-worth-it
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v2 · 2026-11-04 17:08 · 最新观测版本
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
When is another AI research run worth more than keeping that money invested? Using 8,120 iPulse AI reports that consumed nearly 100 million output and reasoning tokens on the same 203 stocks across four production batches, we compare repricing old targets (with and without shock safeguards), mechanical growth-path resets and fresh multi-agent reports. Ten matched configurations represent seven investment frameworks. The primary economic test selects equal-weight Top 10 USD portfolios from one-year forecasts, net of 10 bps trading costs and a USD 3,000 all-in charge for fresh research on a USD 300,000 account. In two unchanged-model short windows, guarded reuse averages 2.25%, versus 1.66% for SPY and 4.24% for fresh reports. Guarded reuse beats SPY in all three short windows. Extending the first holding window to 176 days reverses the comparison: guarded reuse returns -11.26%, fresh reports +8.55%, and SPY +3.46%, motivating the thesis half-life question. Supporting quarter-forecast diagnostics show that mechanically resetting old growth paths closes 98% of the MAE gap to fresh reports, yet Fresh and Reset share only 1-4 Top 10 stocks. Top 30 reverses the unchanged-model comparison in favor of guarded reuse (4.17% versus 1.37%). Concentration tests expose dependence on exceptional winners. Nine editorial editions show substantial between-edition turnover, but each final screen retains its own incoming list. Recurring budget scenarios retain initial holdings: monthly costs approach realized short-window refresh value, while weekly budgets require larger additional gains. A matched ranking-continuity extension extracts further value from retained forecasts without eliminating thesis ageing. The architecture separates continuous price monitoring, episodic thesis rebuilding and evidence-based editorial checks.
查看此版本解读 →查看此版本原文 ↗v1 · 2026-11-03 13:30
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? Using 8,120 production reports on the same 203 stocks across four iPulse AI batches, we compare repricing existing targets, guarding them against price shocks, mechanically resetting old forecast growth, and generating fresh multi-agent reports. Ten matched configurations represent seven investment frameworks. Our primary economic evaluation selects Top 10 USD portfolios using one-year forecasts. Across two unchanged-model windows, guarded reuse averages 2.25%, versus 1.66% for SPY and 4.77% for fresh reports, after modeled trading costs and reference compute charges on a USD 100,000 account. Guarded reuse beats SPY in all three short windows, including the model-change transition. Extending the first holding window to nearly six months reverses the result: guarded reuse returns -11.26%, fresh reports +9.12%, and SPY +3.46%, motivating the thesis half-life question. Supporting quarter-forecast diagnostics show that mechanically re-anchoring old growth paths closes 98% of the MAE improvement over fixed targets, yet Fresh and Reset share only 1-4 of their Top 10 stocks. At Top 30, the unchanged-model portfolio comparison instead favors guarded reuse (4.17% versus 1.88%). Endpoint and concentration tests reveal the influence of horizon choices and exceptional winners. Nine production editions show substantial between-edition turnover, while every final editorial screen retains its own incoming list. Measured token workloads connect refresh costs to shared capital and realized break-even budgets. The resulting architecture separates continuous price monitoring from episodic thesis rebuilding and targeted editorial evidence checks.
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