QHRP: What Are Your Thoughts, Questions, and Suggestions?

Here at 1QBit, we have just put out a white paper on some exciting research that we have been working on for the past few months. The white paper discusses an algorithm that we have named Quantum Hierarchical Risk Parity, or QHRP, which is an extension of the work done by Marcos Lopez de Prado on Hierarchical Risk Parity in his paper Building Diversified Portfolios that Outperform Out-of-Sample. QHRP tackles the problem of minimizing the risk of a portfolio of assets. Although the ideas surrounding this go back to Markowitz’s mean-variance portfolio optimization of 1952’s Portfolio Selection, we have applied recent quantum-ready machine learning tools to the problem to arrive at innovative solutions. We look forward to your thoughts, questions, and suggestions in response to the paper.

Would you mind, Max, reminding kind authors, to review the text?

In the initially published paper, there are yet left many questionmarks un-filled ( used as a placeholder during  drafting the text ) instead of the visibly intended references to cited sources.

Would be great to have it fixed, won’t it?

Best regards, MS

on February 2, 2017.
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The link doesnt go to the paper.

Answered on July 30, 2017.
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Fixed, thank you

Answered on July 31, 2017.
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