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Modeling asset allocation strategies and a new portfolio performance score

Author

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  • Apostolos Chalkis
  • Emmanouil Christoforou
  • Ioannis Z. Emiris
  • Theodore Dalamagas

Abstract

We discuss and extend a powerful, geometric framework to represent the set of portfolios, which identifies the space of asset allocations with the points lying in a convex polytope. Based on this viewpoint, we survey certain state-of-the-art tools from geometric and statistical computing in order to handle important and difficult problems in digital finance. Although our tools are quite general, in this paper we focus on two specific questions. The first concerns crisis detection, which is of prime interest for the public in general and for policy makers in particular because of the significant impact that crises have on the economy. Certain features in stock markets lead to this type of anomaly detection: Given the assets' returns, we describe the relationship between portfolios' return and volatility by means of a copula, without making any assumption on investor strategies. We examine a recent method relying on copulae to construct an appropriate indicator that allows us to automate crisis detection. On real data, the indicator detects all past crashes in the cryptocurrency market, whereas from the DJ600-Europe index, from 1990 to 2008, the indicator identifies correctly 4 crises and issues one false positive for which we offer an explanation. Our second contribution is to introduce an original computational framework to model asset allocation strategies, which is of independent interest for digital finance and its applications. Our approach addresses the crucial question of evaluating portfolio management, and is relevant to individual managers as well as financial institutions. To evaluate portfolio performance, we provide a new portfolio score, based on the aforementioned framework and concepts. In particular, our score relies on the statistical properties of portfolios, and we show how they can be computed efficiently.

Suggested Citation

  • Apostolos Chalkis & Emmanouil Christoforou & Ioannis Z. Emiris & Theodore Dalamagas, 2020. "Modeling asset allocation strategies and a new portfolio performance score," Papers 2012.05088, arXiv.org, revised Sep 2021.
  • Handle: RePEc:arx:papers:2012.05088
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    References listed on IDEAS

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    1. Hallerbach, W.G.P.M. & Hundack, C. & Pouchkarev, I. & Spronk, J., 2002. "A Broadband Vision of the DAX over Time," ERIM Report Series Research in Management ERS-2002-87-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    2. J. Bradford De Long & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1989. "The Size and Incidence of the Losses from Noise Trading," Journal of Finance, American Finance Association, vol. 44(3), pages 681-696, July.
    3. Emmanouil Christoforou & Ioannis Z. Emiris & Apostolos Florakis, 2020. "Neural Networks for Cryptocurrency Evaluation and Price Fluctuation Forecasting," Springer Proceedings in Business and Economics, in: Panos Pardalos & Ilias Kotsireas & Yike Guo & William Knottenbelt (ed.), Mathematical Research for Blockchain Economy, pages 133-149, Springer.
    4. Banerjee, Anurag & Hung, Chi-Hsiou, 2011. "Informed momentum trading versus uninformed "naive" investors strategies," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 3077-3089, November.
    5. Dominique Guegan & Ludovic Calès & Monica Billio, 2011. "A Cross-Sectional Score for the Relative Performance of an Allocation," Post-Print halshs-00646070, HAL.
    6. Ludovic Cales & Apostolos Chalkis & Ioannis Z. Emiris & Vissarion Fisikopoulos, 2018. "Practical volume computation of structured convex bodies, and an application to modeling portfolio dependencies and financial crises," Papers 1803.05861, arXiv.org.
    7. Stivers, Chris & Sun, Licheng, 2010. "Cross-Sectional Return Dispersion and Time Variation in Value and Momentum Premiums," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 45(4), pages 987-1014, August.
    8. Grinblatt, Mark & Titman, Sheridan, 1994. "A Study of Monthly Mutual Fund Returns and Performance Evaluation Techniques," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 29(3), pages 419-444, September.
    9. Calès, Ludovic & Chalkis, Apostolos & Emiris, Ioannis Z., 2019. "On the cross-sectional distribution of portfolio returns," JRC Working Papers in Economics and Finance 2019-11, Joint Research Centre, European Commission.
    10. Harry Markowitz, 1956. "The optimization of a quadratic function subject to linear constraints," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 3(1‐2), pages 111-133, March.
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    Cited by:

    1. Cyril Bachelard & Apostolos Chalkis & Vissarion Fisikopoulos & Elias Tsigaridas, 2024. "Randomized Control in Performance Analysis and Empirical Asset Pricing," Papers 2403.00009, arXiv.org.

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