You will be provided with a reference and some statements. Please determine whether each statement is 'supported', 'unsupported', or 'unknown' with respect to the reference. Please note:
First, assess whether the reference contains any valid content. If the reference contains no valid information, such as a 'page not found' message, then all statements should be considered 'unknown'.
If the reference is valid, for a given statement: if the facts or data it contains can be found entirely or partially within the reference, it is considered 'supported' (data accepts rounding); if all facts and data in the statement cannot be found in the reference, it is considered 'unsupported'.

You should return the result in a JSON list format, where each item in the list contains the statement's index and the judgment result, for example:
[
    {
        "idx": 1,
        "result": "supported"
    },
    {
        "idx": 2,
        "result": "unsupported"
    }
]

Below are the reference and statements:
<reference>
Access forbidden!



Access forbidden!

You don't have permission to access the requested object.
It is either read-protected or not readable by the server.

If you think this is a server error, please contact
the
webmaster
.

Error 403

cims.nyu.edu

Apache/2.4.62
</reference>

<statements>
1. BL is a Bayesian procedure that starts from a CAPM‑style equilibrium prior on expected excess returns (implied by market‑cap weights and covariance) and blends investor “views” with that prior to produce posterior expected returns, which are then fed into mean–variance optimization.
2. BL mostly keeps the same covariance matrix as mean–variance and therefore uses variance as risk, though Bayesian updating can also yield a posterior covariance used in optimization.
3. BL’s innovation is on the return side, not the risk measure: it reduces parameter‑uncertainty risk by treating expected returns as random with a prior centered on CAPM equilibrium and updating that prior using noisy views, thus stabilizing portfolios and avoiding “corner solutions” driven by noisy mean estimates.
4. Investor views are encoded as linear constraints \(P\mu = Q + \varepsilon\) with view uncertainty \(\Omega\), and a Bayesian mixed‑estimation formula combines the equilibrium prior and views into a posterior mean; deviations from equilibrium are proportional to view confidence.
5. BL yields intuitive, stable expected returns and portfolios that look like the market portfolio plus modest tilts, addressing the mean–variance sensitivity and extreme allocations problem.
6. Same optimizer, better inputs: BL feeds its posterior expected returns (and possibly posterior covariance) into the standard mean-variance optimizer.
7. Portfolio shape: With no views, the optimal unconstrained portfolio is the global CAPM market portfolio; with views, portfolios are tilts around the market portfolio, scaled by confidence, yielding more diversified and intuitive allocations than naïve mean-variance.
8. Black–Litterman: Return prediction / prior: CAPM equilibrium risk premia as prior; investor views encoded and blended via Bayesian update.
9. Black–Litterman: Allocation behavior: Mean–variance optimization on posterior returns; portfolios are market‑cap plus confidence‑weighted tilts.
10. Black–Litterman: Main strengths: Stabilizes allocations; avoids corner solutions; accommodates relative views and partial information.
11. Black–Litterman: Main limitations: Requires subjective view specification and confidence; still variance‑based risk; prior choice is model‑dependent.
12. Apply BL’s mixed-estimation formula to blend equilibrium prior and ML views into posterior expected returns that are statistically regularized and economically anchored.
13. This preserves the geometry and tractability of mean-variance while using ML for better forecasts and BL for regularization.
14. BL improves stability and realism of portfolios compared to naïve mean–variance.
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.