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>
Content Blocked



Content Blocked

!

We have detected that you may be using an automated script or search engine our site does not support. Please
retry using
an alternate way of accessing our site.

Contact
Content_Protection_Services@Elsevier.com

for more information.

Reference Number: a3be980e3f972244

IP Address: 208.110.231.2

Timestamp:

Wed, 16 Sep 2026 08:45:16 GMT

::CLOUDFLARE_ERROR_1000S_BOX::
</reference>

<statements>
1. DeMiguel, Garlappi, and Uppal show that, across 14 models and seven datasets, none consistently beats the naive 1/N portfolio out of sample in Sharpe ratio, certainty-equivalent return, or turnover, because the gain from optimal diversification is more than offset by estimation error.
2. For sample-based mean-variance and its extensions, the estimation window needed to beat 1/N is around 3,000 months for 25 assets and about 6,000 months for 50 assets.
3. DeMiguel et al. show that even well-designed optimizers can lose to 1/N once estimation error is priced in, with required estimation windows of thousands of months for realistic portfolios
4. Mean-variance optimization measures risk as variance, requires estimates of expected returns and covariances, and produces a transparent but often unstable weight vector; its original formulation does not assume normality, but it ignores higher moments and is highly sensitive to estimation error.
5. The published evidence supports hybrid frameworks that are more general and potentially more effective than any single family in isolation, because they address different failure modes: estimation error in mean-variance, subjectivity in Black-Litterman, opacity and overfitting in deep learning, and tail insensitivity in variance.
6. What would change the judgment is not another backtest with a higher Sharpe ratio, but long-sample out-of-sample results after realistic costs, robustness across market regimes and holding horizons, transparent attribution that survives auditability, and deployment evidence showing that learned or hybrid allocators displace regularized mean-variance and Black-Litterman workflows in live robo-advisor or institutional asset-allocation settings.
</statements>

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