Writings
Research notes, method memos, and analytical essays from topicspace. Each piece explores how narrative-price dynamics work and how to reason about them.
Not stock picks. Not recommendations. Five forward tests of what the current topicspace field model implies should happen next, each tied to a specific pattern in the data and stated in a way that can be falsified.
A model that never risks being wrong isn't doing much. The most useful research isn't the kind that sounds confident — it's the kind that can be checked.
Across 5+ months of backtest data, topicspace measures a +4.92pp gap in 10-day forward excess returns between AI hardware names and AI software/platform names — with three counterintuitive sub-findings.
Hardware +3.29pp, software −1.63pp, gap +4.92pp (Welch t=13.5). Software CONFIRMED is the worst software setup. CRWV behaves like hardware. The AI-celebrity hardware names are the cluster's laggards.
A new layer that turns a claim, basket, or ticker question into a grounded brief built from topicspace fields. Not a chatbot, not a recommendation engine — a layer for interrogating the field.
A dashboard shows what's happening. intel helps answer a harder question: what does that actually mean here?
Strong narrative-price gap is not the same as a credible setup. Meta is the cleanest example of why the validity layer matters — and why hesitation can be the insight.
A strong raw signal gets your attention. Validity tells you whether to trust it. Transitions tell you what happened next.
We tested whether narrative-price state could improve stock selection in the AI ecosystem. Raw dislocation was the ranking core. State and sector context made the signal investable.
S4 Sharpe 2.10. Cap S4+B4 Sharpe 2.44. ML ranking failed at this sample size. Pure NDS ranking inside the eligibility layer remains the rule.
After backtesting the model, topicspace is becoming more specific: where narrative pressure is strongest, where that signal is actually valid, and how to turn it into a disciplined workflow.
How topicspace measures the gap between what analysts are saying and what the market is pricing — and what that gap has historically meant.
Narrative-leading states outperformed price-led states. Transitions carried more signal than static presence.
Why narrative intelligence requires field-level state modeling rather than document retrieval — and how topicspace is built around that distinction.
Show where the story is stronger than the stock move — or where the stock has already moved ahead of the story. The bigger the gap, the more it’s worth a closer look.
Not every kind of gap matters the same way. topicspace surfaces the situations that have shown more reliable follow-through in similar contexts.
Alerts, state transitions, and the daily field read focus attention on what actually moved — not just what’s loud.
intel lets you check a claim, compare a basket, or explain a ticker using the same structured context that powers the rest of topicspace.