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>
ICLR  Integrating LLM-generated views into the Black-Litterman model



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Poster
in
Workshop:
Advances in Financial AI: Opportunities, Innovations, and Responsible AI

Integrating LLM-generated views into the Black-Litterman model

Lee ⋅ yejin kim ⋅ Yongjae Lee

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OpenReview
]

Abstract

Portfolio optimization faces challenges due to sensitivity in traditional mean-variance models. The Black-Litterman model mitigates this by integrating investor views, but defining these views remains difficult. This study explored the integration of large language models (LLMs) generated views into the Black-Litterman framework for portfolio optimization. Our method leverages LLMs to estimate expected stock returns from historical prices and company metadata, incorporating uncertainty via variance in predictions. We backtest optimized portfolios using LLM against the S&P 500 for 2024, assessing its returns and risks. Empirical results suggest that different LLMs exhibit varying levels of predictive optimism and confidence stability, which impact portfolio performance. The source code and data are available at https://anonymous.4open.science/r/ICLR2025-4E6D.

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</reference>

<statements>
1. Lee et al. (2025) Integrating LLM-Generated Views into MV Optimization Using the BL Model: LLMs estimate expected returns and uncertainty for BL views; BLM-Llama achieved the highest cumulative return (CAGR 0.6731) versus S&P 500, equal-weight, and MVO baselines over the 2024 test window.
</statements>

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