MosaicQuant is built on the premise that most financial analysis fails not because of insufficient data, but because of unstructured reasoning, implicit assumptions and overconfidence in single models.
Our methodology treats valuation and analysis as explicit, model-driven processes. Assumptions are surfaced, uncertainty is acknowledged, and disagreement between models is treated as information rather than noise.
No single valuation or analytical model is sufficient on its own. Each framework embeds assumptions about growth, risk, capital structure and market behaviour.
MosaicQuant applies several complementary models in parallel, allowing areas of agreement and disagreement to become visible.
Financial outputs are inherently sensitive to assumptions. Presenting a single number implies a level of precision that does not exist.
MosaicQuant expresses results as valuation or outcome ranges, reflecting both model dispersion and parameter sensitivity.
Growth rates, discount rates, margins and terminal conditions materially influence analytical outcomes.
All MosaicQuant products are designed to make key assumptions visible and inspectable, rather than embedding them implicitly in opaque calculations.
While individual products differ in scope, the underlying process follows the same disciplined structure.
Inputs are cleaned and standardised to ensure consistency across companies, sectors and markets. Where data quality is insufficient, models are excluded rather than forced.
Each applicable model is run independently using the same underlying data set. This avoids selectively tailoring assumptions to fit preferred outcomes.
Model outputs that fail basic plausibility or stability checks are excluded. This prevents fragile or mechanically extreme results from dominating.
Remaining model outputs are combined into a coherent range. The focus shifts from prediction to interpretation: understanding what must be true for a given outcome to hold.
MosaicQuant does not attempt to forecast prices or time markets. Outputs are analytical reference points, not targets.
The platform provides structured analysis, not recommendations. Judgement, risk tolerance and decision-making remain with the user.
Qualitative stories are not ignored, but they are not allowed to override disciplined, model-based reasoning.
MosaicQuant’s methodology is intentionally conservative. It prioritises transparency over persuasion, structure over storytelling, and repeatability over novelty.
This foundation allows individual products — from equity valuation to ETF analysis and beyond — to evolve independently while remaining analytically consistent.