Foundry Healthcare Book Leverage Factor ETF

7 independent checks plus 1 arithmetically implied by another check (RIC / IRC 851(b)(3)(A) 50% bucket), which is reported separately so the tally cannot overstate scrutiny.

1 of 7 checks could not be tested. Those are neither passes nor failures — they are gaps in what the data can answer, and they are excluded from the passed count above rather than folded into it.

This assessment describes the long-only book this fund holds — the top bucket only.

This fund is excepted from the VaR tests, so the ratio below is context rather than the operative limit. It is shown because it is still a description of the book’s risk, not because it decides anything.

historical method · 331 monthly observations · about 3.3 in the 99% tail · paired percentile bootstrap over months

Engineering approximation of SEC Rule 18f-4 for research display. Not a compliance opinion and not a determination that any fund is compliant. Hypothetical model portfolio, not a registered fund.

There is no “before” to compare against — the predictor was already public when our data begins, so every month shown is out-of-sample relative to the original study.

Split at January of the year after the factor was published. The post-publication stretch IS genuinely out-of-sample relative to the original study -- the predictor was public by then -- so it speaks to whether the effect survived being known. Nothing is fitted here, so this is performance SINCE PUBLICATION, not validation of a model. Note the 'pre' side is our data before publication (our panel starts ~1999), NOT the study's original in-sample period, which usually ran decades earlier; a decay figure compares before-vs-after within our sample and is not a comparison against the published result.

3-year rolling windows, stepping 1 year (26 windows). Dispersion is the spread of window Sharpes — higher means the record depends more on which era you look at.

Rules-based factors fit no parameters, so these windows are not out-of-sample tests and do not validate a fitted model. They show whether the factor's record is consistent across eras or driven by one regime.