IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2306.08105.html
   My bibliography  Save this paper

Model-Free Market Risk Hedging Using Crowding Networks

Author

Listed:
  • Vadim Zlotnikov
  • Jiayu Liu
  • Igor Halperin
  • Fei He
  • Lisa Huang

Abstract

Crowding is widely regarded as one of the most important risk factors in designing portfolio strategies. In this paper, we analyze stock crowding using network analysis of fund holdings, which is used to compute crowding scores for stocks. These scores are used to construct costless long-short portfolios, computed in a distribution-free (model-free) way and without using any numerical optimization, with desirable properties of hedge portfolios. More specifically, these long-short portfolios provide protection for both small and large market price fluctuations, due to their negative correlation with the market and positive convexity as a function of market returns. By adding our long-short portfolio to a baseline portfolio such as a traditional 60/40 portfolio, our method provides an alternative way to hedge portfolio risk including tail risk, which does not require costly option-based strategies or complex numerical optimization. The total cost of such hedging amounts to the total cost of rebalancing the hedge portfolio.

Suggested Citation

  • Vadim Zlotnikov & Jiayu Liu & Igor Halperin & Fei He & Lisa Huang, 2023. "Model-Free Market Risk Hedging Using Crowding Networks," Papers 2306.08105, arXiv.org.
  • Handle: RePEc:arx:papers:2306.08105
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2306.08105
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vipul Satone & Dhruv Desai & Dhagash Mehta, 2021. "Fund2Vec: Mutual Funds Similarity using Graph Learning," Papers 2106.12987, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jerinsh Jeyapaulraj & Dhruv Desai & Peter Chu & Dhagash Mehta & Stefano Pasquali & Philip Sommer, 2022. "Supervised similarity learning for corporate bonds using Random Forest proximities," Papers 2207.04368, arXiv.org, revised Oct 2022.
    2. Rian Dolphin & Barry Smyth & Ruihai Dong, 2022. "A Multimodal Embedding-Based Approach to Industry Classification in Financial Markets," Papers 2211.06378, arXiv.org.
    3. Bhaskarjit Sarmah & Nayana Nair & Dhagash Mehta & Stefano Pasquali, 2022. "Learning Embedded Representation of the Stock Correlation Matrix using Graph Machine Learning," Papers 2207.07183, arXiv.org.
    4. Dhruv Desai & Ashmita Dhiman & Tushar Sharma & Deepika Sharma & Dhagash Mehta & Stefano Pasquali, 2023. "Quantifying Outlierness of Funds from their Categories using Supervised Similarity," Papers 2308.06882, arXiv.org.
    5. Dimitrios Vamvourellis & Mate Attila Toth & Dhruv Desai & Dhagash Mehta & Stefano Pasquali, 2022. "Learning Mutual Fund Categorization using Natural Language Processing," Papers 2207.04959, arXiv.org.
    6. Guilherme Thomaz & Denis Maua, 2024. "A Case Study of Next Portfolio Prediction for Mutual Funds," Papers 2410.18098, arXiv.org.
    7. Alejandro Rodriguez Dominguez, 2022. "Portfolio Optimization based on Neural Networks Sensitivities from Assets Dynamics respect Common Drivers," Papers 2202.08921, arXiv.org, revised Dec 2022.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2306.08105. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.