> For the complete documentation index, see [llms.txt](https://docs.orbitfin.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.orbitfin.ai/part-3-use-cases/3.5-portfolio-risk.md). # 3.5 Portfolio risk The portfolio risk use cases shift the unit of analysis from one company to the whole book. Rather than asking "what about this name," they ask "what about my portfolio" — where it has drifted, where it is concentrated, what it is exposed to, and where attention is most needed right now. These depend on the platform's reach across an entire holdings list at once, anchored through the entity master so every position is correctly identified and aggregated. On the value chain they sit in synthesis: each connects information across many companies to a team's own portfolio and its own view of risk. **09 · Portfolio thesis drift.** What it produces is a read across the book of where the original case for each holding is weakening — aggregating the thesis-level signals from individual names into a portfolio-wide view of which positions are drifting away from the reasons they were taken. The outcome is that thesis erosion is caught at the portfolio level, systematically, rather than name by name as individual analysts happen to notice it. It runs naturally on a schedule, refreshing as new evidence arrives. On the value chain it is synthesis across the whole portfolio — the single-name thesis monitoring of 3.4, lifted to the level of the book. Run it against a general thesis frame, or shape it to the specific theses your team has set for its positions. **10 · Concentration and factor drift.** What it produces is a view of how the portfolio's concentrations and factor exposures are shifting over time — where risk is quietly building through correlated positions or drift in underlying characteristics, even when no single position looks alarming. The outcome is that a team sees structural risk forming before it becomes a problem, rather than discovering it after a move. It runs on a schedule as a standing monitor. On the value chain it is synthesis — combining position-level data into a portfolio-level read on risk. Run it as shipped, or customise the factors and thresholds to the risk frame your firm actually manages to. **11 · Sector and theme exposure.** What it produces is a map of where the portfolio is actually exposed — by sector and by theme — including the exposures that are not obvious from a position's primary classification, drawing on the deeper company understanding in the data rather than surface labels. The outcome is that a team knows its true exposure, including the indirect and second-order kind, rather than the exposure its holdings appear to have on paper. It runs on demand for a point-in-time read or on a schedule to track exposure as it moves. On the value chain it is synthesis, and it connects directly to the thematic work in 3.6. Run it on Orbit's sector and theme definitions, or supply your own. **12 · Top-3 attention list.** What it produces is the sharpest possible output: the small number of things across the entire portfolio that most warrant attention right now — the positions where something has changed enough, or risk has built enough, to deserve a human's time today. The outcome is that a team's attention is directed to where it matters most, rather than spread evenly across a book or pulled toward whatever happened to be loudest. It is an automate-mode use case by nature, recomputed continuously so the list is always current. On the value chain it reaches toward decision support — not making a decision, but surfacing what most needs one, with the evidence attached. As established earlier, the platform surfaces and shows its work; the judgment of what to do remains with the team. Run it with Orbit's prioritisation, or shape what "warrants attention" means to your desk. The progression across these four is worth noting. The first three describe the portfolio's state — its thesis health, its risk structure, its true exposure — each a synthesis across many positions. The fourth, the attention list, turns that state into a prioritised call on where to look, which is the closest any use case in the library comes to decision support. It is also the clearest illustration of the line the platform holds throughout: it can tell a team where attention is most warranted and show exactly why, but the decision about what to do with that attention stays human. That is by design, and it is what makes a portfolio-level tool something a team can rely on rather than something it has to second-guess. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. 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