IDEAS home Printed from https://ideas.repec.org/p/zur/econwp/463.html
   My bibliography  Save this paper

Low risk, high variability: practical guide for portfolio construction

Author

Listed:
  • Antonello Cirulli
  • Gianluca De Nard
  • Joshua Traut
  • Patrick Walker

Abstract

The low-risk anomaly challenges traditional financial theory by stating that less volatile stocks generate higher risk-adjusted returns. This paper explores how various portfolio construction choices influence the performance of low-risk portfolios. We show that methodological decisions critically influence portfolio outcomes, causing substantial dispersion in performance metrics across weighting schemes and risk estimators. This can only be marginally mitigated by incorporating constraints such as short-sale restrictions and size or price filters. Our analysis reveals that volatility-based estimators yield the most favorable performance distribution, outperforming beta-based approaches. Transaction costs are found to significantly affect performance and are vitally important in identifying the most attractive portfolios, highlighting the importance of realistic implementation constraints. Through rigorous empirical analysis, this study bridges the gap between theoretical insights and practical applications, offering actionable guidance to investors. The findings advocate for a cautious approach to nonstandard errors in portfolio modeling and emphasize the necessity of robust strategies in low-risk investing.

Suggested Citation

  • Antonello Cirulli & Gianluca De Nard & Joshua Traut & Patrick Walker, 2025. "Low risk, high variability: practical guide for portfolio construction," ECON - Working Papers 463, Department of Economics - University of Zurich.
    • Handle: RePEc:zur:econwp:463
    as

    Download full text from publisher

    File URL: https://www.zora.uzh.ch/id/eprint/268927/1/econwp463.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Joshua Traut, 2023. "What we know about the low-risk anomaly: a literature review," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(3), pages 297-324, September.
    2. Farshid Abdi & Angelo Ranaldo, 2017. "A Simple Estimation of Bid-Ask Spreads from Daily Close, High, and Low Prices," The Review of Financial Studies, Society for Financial Studies, vol. 30(12), pages 4437-4480.
    3. Chung, Kee H. & Zhang, Hao, 2014. "A simple approximation of intraday spreads using daily data," Journal of Financial Markets, Elsevier, vol. 17(C), pages 94-120.
    4. Robert Novy-Marx & Mihail Velikov, 2016. "A Taxonomy of Anomalies and Their Trading Costs," The Review of Financial Studies, Society for Financial Studies, vol. 29(1), pages 104-147.
    5. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    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. Park, Sung Jun & Park, Ki Young, 2019. "Can investors profit from security analyst recommendations?: New evidence on the value of consensus recommendations," Finance Research Letters, Elsevier, vol. 30(C), pages 403-413.
    2. Pätäri, Eero & Ahmed, Sheraz & Luukka, Pasi & Yeomans, Julian Scott, 2023. "Can monthly-return rank order reveal a hidden dimension of momentum? The post-cost evidence from the U.S. stock markets," The North American Journal of Economics and Finance, Elsevier, vol. 65(C).
    3. Köstlmeier, Siegfried, 2024. "Pricing and mispricing of accounting fundamentals: Global evidence," The Quarterly Review of Economics and Finance, Elsevier, vol. 94(C), pages 71-87.
    4. Jia, Yuecheng & Wu, Yangru & Yan, Shu & Liu, Yuzheng, 2023. "A seesaw effect in the cryptocurrency market: Understanding the return cross predictability of cryptocurrencies," Journal of Empirical Finance, Elsevier, vol. 74(C).
    5. Bagnara, Matteo & Jappelli, Ruggero, 2022. "Liquidity derivatives," SAFE Working Paper Series 358, Leibniz Institute for Financial Research SAFE.
    6. Cakici, Nusret & Zaremba, Adam, 2022. "Salience theory and the cross-section of stock returns: International and further evidence," Journal of Financial Economics, Elsevier, vol. 146(2), pages 689-725.
    7. Mohrschladt, Hannes, 2021. "The ordering of historical returns and the cross-section of subsequent returns," Journal of Banking & Finance, Elsevier, vol. 125(C).
    8. Geertsema, Paul & Lu, Helen, 2020. "The correlation structure of anomaly strategies," Journal of Banking & Finance, Elsevier, vol. 119(C).
    9. Lin, Zih-Ying & Chang, Chuang-Chang & Wang, Yaw-Huei, 2018. "The impacts of asymmetric information and short sales on the illiquidity risk premium in the stock option market," Journal of Banking & Finance, Elsevier, vol. 94(C), pages 152-165.
    10. Kanne, Stefan & Korn, Olaf & Uhrig-Homburg, Marliese, 2023. "Stock illiquidity and option returns," Journal of Financial Markets, Elsevier, vol. 63(C).
    11. Kan, Raymond & Wang, Xiaolu & Zheng, Xinghua, 2024. "In-sample and out-of-sample Sharpe ratios of multi-factor asset pricing models," Journal of Financial Economics, Elsevier, vol. 155(C).
    12. Doron Avramov & Si Cheng & Lior Metzker, 2023. "Machine Learning vs. Economic Restrictions: Evidence from Stock Return Predictability," Management Science, INFORMS, vol. 69(5), pages 2587-2619, May.
    13. Michaely, Roni & Rubin, Amir & Vedrashko, Alexander, 2016. "Further evidence on the strategic timing of earnings news: Joint analysis of weekdays and times of day," Journal of Accounting and Economics, Elsevier, vol. 62(1), pages 24-45.
    14. Langlois, Hugues, 2023. "What matters in a characteristic?," Journal of Financial Economics, Elsevier, vol. 149(1), pages 52-72.
    15. Kozak, Serhiy & Nagel, Stefan & Santosh, Shrihari, 2020. "Shrinking the cross-section," Journal of Financial Economics, Elsevier, vol. 135(2), pages 271-292.
    16. Carlo Rosa, 2022. "Understanding intraday momentum strategies," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(12), pages 2218-2234, December.
    17. Yezhou Sha & Ziwen Bu & Zilong Wang, 2023. "What drives the distress risk–return puzzle? A perspective on limits of arbitrage," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 3574-3592, October.
    18. Marie Brière & Ariane Szafarz, 2021. "When it rains, it pours: Multifactor asset management in good and bad times," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 44(3), pages 641-669, September.
    19. Hanauer, Matthias X. & Jansen, Maarten & Swinkels, Laurens & Zhou, Weili, 2024. "Factor models for Chinese A-shares," International Review of Financial Analysis, Elsevier, vol. 91(C).
    20. Tian Ma & Cunfei Liao & Fuwei Jiang, 2023. "Timing the factor zoo via deep learning: Evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(1), pages 485-505, March.

    More about this item

    Keywords

    Low-risk investing; methodology; nonstandard errors; portfolio construction;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    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:zur:econwp:463. 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: Severin Oswald (email available below). General contact details of provider: https://edirc.repec.org/data/seizhch.html .

    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.