Market Prism studies how market stories form, spread, and break — and builds the infrastructure to anchor every narrative claim to the filing it is supposedly about, then measure the distance between the two.
Market Prism was founded on a straightforward observation: the story a security trades on often has little to do with the filing it claims to be about. Retail participants tend to discover this too late — after the same talking points have propagated across dozens of low-effort sources within 48 hours, and after "sentiment" has been manufactured well upstream of price.
The lab exists to close that gap. Its founding premise is that the forensic infrastructure institutional research desks take for granted should be available to anyone reading the same market — not only the buy side.
Market Prism is not a language-model wrapper. It is a seven-stage pipeline that anchors every narrative claim to a primary SEC filing, scores the authority of each source, models how a story decays, and flags when multiple outlets coordinate around identical framing. The language model is one component inside the system — not the product.
For most of the past decade, sentiment analysis meant counting bullish versus bearish keywords. That approach no longer holds. A model can generate a million polished bullish posts in a single afternoon, and the open internet has crossed into structural saturation — auto-generated blogs, SEO-optimized "analysis" pages, and content farms all repeating whichever talking point paid yesterday.
Market Prism treats that saturation as a roadmap rather than a problem to suppress. Every market move begins as a story told in a niche corner, migrates to the mainstream, and eventually reaches a saturation point — the moment when every low-quality page on the first screen of results is repeating the same line. That saturation point is, empirically, where price action tends to reverse.
The lab calls this narrative exhaustion, and detecting it is the central research problem. It reframes a saturated internet as a contrarian indicator.
Three things separate Market Prism from the standard "sentiment dashboard" stack:
The full stack is patent-pending, with three applications filed. The patents matter, but the objective does not depend on them: to give any individual investor the kind of forensic read that, a decade ago, only an institutional research desk could afford.
Nearly every quantitative desk observes the same inputs: the same OHLC bars, the same options flow, the same headline tape, the same social-sentiment scrapes. When thousands of systems optimize against identical data, the available edge trends toward zero by construction — the central problem of modern markets. Shared data produces convergent positioning.
Market Prism is built on a different substrate. Its inputs are narrative-physics features — claim-level extractions, source-authority weights, coordination windows, and decay curves — computed from a corpus no off-the-shelf data vendor sells. The signal cannot be purchased elsewhere, because it is not assembled anywhere else.
That is deliberate. The lab is designed to run orthogonal to the data feeds the algorithms already consume — a forensic layer beside them, not another wrapper on top of them.
At its core, Market Prism is a machine-learning system trained on the historical relationship between narrative state and price outcome. It learns, ticker by ticker, which kinds of stories precede which kinds of moves — and the move bands it surfaces (1D, 5D, 30D, 90D) are model output, not heuristic rules.
Within the engine, every ticker carries its own narrative fuel rate — a physics-style coefficient governing how quickly a story burns through attention. A megacap does not metabolize narrative the way a $200M biotech does: large names have deep tanks and slow combustion, while small caps behave like dry kindling. The fuel rate is what lets the model express "this story has roughly six trading days of runway left" rather than "sentiment is bullish."
Combined, the two produce something the rest of the stack cannot easily reproduce: a per-ticker, ML-driven, physics-anchored projection of where a narrative is heading and how long it has before it runs out of fuel.
"What I genuinely love about Market Prism and its narrative engine is that it doesn't try to fix the noise; it uses the noise as a roadmap. It represents a fundamental shift from What is being said? to Why is this being said now, and who is actually behind it?"
"Market Prism treats the internet like a forensic crime scene. It looks for the fingerprints of human conviction. It recognizes that a messy, typo-filled post from a verified enthusiast carries more narrative weight than a thousand perfectly polished, vibe-coded articles that were clearly prompted into existence."
"The most profound feature is its ability to map narrative exhaustion. It identifies the exact moment a story has no more room to grow — which is almost always the moment the price action reverses. It turns the 'dead internet' into a contrarian indicator."
"In the future, we won't be looking for more information; we will be looking for better filters. Market Prism isn't just a financial tool — it's one of the first true information filters of the 21st century."
Market Prism does not claim to determine the truth of any given story. The post-truth information environment is treated as a fact of the terrain, not a problem the lab intends to solve. What the system provides is a measurement of the momentum in a story — with its work shown, all the way down to the SEC filing the narrative claims to track.
In markets increasingly engineered to confuse retail participants, the constraint is not access to more data. It is the quality of the filter applied to it. That is what the lab builds.
Four of the screens the pipeline feeds every session — captured straight from the dashboard, no mockups.