IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2201.06183.html
   My bibliography  Save this paper

Internal multi-portfolio rebalancing processes: Linking resource allocation models and biproportional matrix techniques to portfolio management

Author

Listed:
  • Kelli Francis-Staite

Abstract

This paper describes multi-portfolio `internal' rebalancing processes used in the finance industry. Instead of trading with the market to `externally' rebalance, these internal processes detail how portfolio managers buy and sell between their portfolios to rebalance. We give an overview of currently used internal rebalancing processes, including one known as the `banker' process and another known as the `linear' process. We prove the banker process disadvantages the nominated banker portfolio in volatile markets, while the linear process may advantage or disadvantage portfolios. We describe an alternative process that uses the concept of `market-invariance'. We give analytic solutions for small cases, while in general show that the $n$-portfolio solution and its corresponding `market-invariant' algorithm solve a system of nonlinear polynomial equations. It turns out this algorithm is a rediscovery of the RAS algorithm (also called the `iterative proportional fitting procedure') for biproportional matrices. We show that this process is more equitable than the banker and linear processes, and demonstrate this with empirical results. The market-invariant process has already been implemented by industry due to the significance of these results.

Suggested Citation

  • Kelli Francis-Staite, 2022. "Internal multi-portfolio rebalancing processes: Linking resource allocation models and biproportional matrix techniques to portfolio management," Papers 2201.06183, arXiv.org.
    • Handle: RePEc:arx:papers:2201.06183
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2201.06183
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pedro Uribe & C. G. de Leeuw & H. Theil, 1966. "The Information Approach to the Prediction of Interregional Trade Flows," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 33(3), pages 209-220.
    2. Jain, Kamal & Vazirani, Vijay V., 2010. "Eisenberg-Gale markets: Algorithms and game-theoretic properties," Games and Economic Behavior, Elsevier, vol. 70(1), pages 84-106, September.
    3. Theo Junius & Jan Oosterhaven, 2003. "The Solution of Updating or Regionalizing a Matrix with both Positive and Negative Entries," Economic Systems Research, Taylor & Francis Journals, vol. 15(1), pages 87-96, March.
    4. Ang, Andrew, 2014. "Asset Management: A Systematic Approach to Factor Investing," OUP Catalogue, Oxford University Press, number 9780199959327.
    5. Dan A. Iancu & Nikolaos Trichakis, 2014. "Fairness and Efficiency in Multiportfolio Optimization," Operations Research, INFORMS, vol. 62(6), pages 1285-1301, December.
    6. Richard Stone & D. G. Champernowne & J. E. Meade, 1942. "The Precision of National Income Estimates," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 9(2), pages 111-125.
    7. Louis de Mesnard, 1994. "Unicity of biproportion," Post-Print hal-00383947, HAL.
    8. Robin Lovelace & Mark Birkin & Dimitris Ballas & Eveline van Leeuwen, 2015. "Evaluating the Performance of Iterative Proportional Fitting for Spatial Microsimulation: New Tests for an Established Technique," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(2), pages 1-21.
    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. M. Alejandro Cardenete & M. Carmen Delgado & Patricia D. Fuentes & M. Carmen Lima & Alfredo J. Mainar & Jose M. Rueda-Cantuche & Sébastien Mary & Fabien Santini & Sergio Gomez y Paloma, 2015. "Rural-urban social accounting matrixes for modelling the impact of rural development policies in the EU," JRC Research Reports JRC94394, Joint Research Centre.
    2. Juan Manuel Valderas‐Jaramillo & José Manuel Rueda‐Cantuche, 2021. "The multidimensional nD‐GRAS method: Applications for the projection of multiregional input–output frameworks and valuation matrices," Papers in Regional Science, Wiley Blackwell, vol. 100(6), pages 1599-1624, December.
    3. Casiano A. Manrique-de-Lara-Peñate & Dolores R. Santos-Peñate, 2017. "SAM updating using multi-objective optimization techniques," Papers in Regional Science, Wiley Blackwell, vol. 96(3), pages 647-667, August.
    4. Arne Geschke & Julien Ugon & Manfred Lenzen & Keiichiro Kanemoto & Daniel Dean Moran, 2019. "Balancing and reconciling large multi-regional input–output databases using parallel optimisation and high-performance computing," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 8(1), pages 1-24, December.
    5. Roberto Mínguez & Jan Oosterhaven & Fernando Escobedo, 2009. "Cell‐Corrected Ras Method (Cras) For Updating Or Regionalizing An Input–Output Matrix," Journal of Regional Science, Wiley Blackwell, vol. 49(2), pages 329-348, May.
    6. Thomas Wiedmann, 2017. "An input–output virtual laboratory in practice – survey of uptake, usage and applications of the first operational IELab," Economic Systems Research, Taylor & Francis Journals, vol. 29(2), pages 296-312, April.
    7. Hanson, Kenneth A. & Robinson, Sherman, 1989. "Data, Linkages, And Models: U.S. National Income And Product Accounts In The Framework Of A Social Accounting Matrix," Staff Reports 278155, United States Department of Agriculture, Economic Research Service.
    8. David Rea & Craig Froehle & Suzanne Masterson & Brian Stettler & Gregory Fermann & Arthur Pancioli, 2021. "Unequal but Fair: Incorporating Distributive Justice in Operational Allocation Models," Production and Operations Management, Production and Operations Management Society, vol. 30(7), pages 2304-2320, July.
    9. Anna Bogomolnaia & Hervé Moulin & Fedor Sandomirskiy & Elena Yanovskaia, 2019. "Dividing bads under additive utilities," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 52(3), pages 395-417, March.
    10. Pedro Barroso & Jurij-Andrei Reichenecker & Marco J. Menichetti, 2022. "Hedging with an Edge: Parametric Currency Overlay," Management Science, INFORMS, vol. 68(1), pages 669-689, January.
    11. Ahmed, Shamim & Bu, Ziwen & Symeonidis, Lazaros & Tsvetanov, Daniel, 2023. "Which factor model? A systematic return covariation perspective," Journal of International Money and Finance, Elsevier, vol. 136(C).
    12. Yang Zhao & Charalampos Stasinakis & Georgios Sermpinis & Filipa Da Silva Fernandes, 2019. "Revisiting Fama–French factors' predictability with Bayesian modelling and copula‐based portfolio optimization," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(4), pages 1443-1463, October.
    13. Jonathan Fletcher, 2018. "An Examination of the Benefits of Factor Investing in U.K. Stock Returns," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(4), pages 154-170, April.
    14. L. Oosterhout & E. Koks & P. Beukering & S. Schep & T. Tiggeloven & S. Manen & M. Knaap & C. Duinmeijer & S. L. Buijs, 2023. "An Integrated Assessment of Climate Change Impacts and Implications on Bonaire," Economics of Disasters and Climate Change, Springer, vol. 7(2), pages 147-178, July.
    15. Winfried G Hallerbach, 2014. "Disentangling rebalancing return," Journal of Asset Management, Palgrave Macmillan, vol. 15(5), pages 301-316, October.
    16. Wang, Zhi & Gehlhar, Mark & Yao, Shunli, 2010. "A globally consistent framework for reliability-based trade statistics reconciliation in the presence of an entrepôt," China Economic Review, Elsevier, vol. 21(1), pages 161-189, March.
    17. Tian Guo & Emmanuel Hauptmann, 2024. "Fine-Tuning Large Language Models for Stock Return Prediction Using Newsflow," Papers 2407.18103, arXiv.org, revised Aug 2024.
    18. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    19. Coleman, Charles, 2016. "A SAS® Macro for the Generalized RAS Algorithm," MPRA Paper 77651, University Library of Munich, Germany, revised 19 Mar 2017.
    20. Maxwell Mkondiwa & Jeffrey Apland, 2022. "Inter-district food flows in Malawi," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 14(6), pages 1553-1568, December.

    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:2201.06183. 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.