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

Abstract

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.