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Portfolio Optimization via a Surrogate Risk Measure: Conditional Desirability Value at Risk (CDVaR)

  • İ. İlkay BoduroğluEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11353)

Abstract

A risk measure that specifies minimum capital requirements is the amount of cash that must be added to a portfolio to make its risk acceptable to regulators. The 2008 financial crisis highlighted the demise of the most widely used risk measure, Value-at-Risk. Unlike the Conditional VaR model of Rockafellar & Uryasev, VaR ignores the possibility of abnormal returns and is not even a coherent risk measure as defined by Pflug. Both VaR and CVaR portfolio optimizers use asset-price return histories. Our novelty here is introducing an annual Desirability Value (DV) for a company and using the annual differences of DVs in CVaR optimization, instead of simply utilizing annual stock-price returns. The DV of a company is the perpendicular distance from the fundamental position of that company to the best separating hyperplane \(H_0\) that separates profitable companies from losers during training. Thus, we introduce both a novel coherent surrogate risk measure, Conditional-Desirability-Value-at-Risk (CDVaR) and a direction along which to reduce (downside) surrogate risk, the perpendicular to \(H_0\). Since it is a surrogate measure, CDVaR optimization does not produce a cash amount as the risk measure. However, the associated CVaR (or VaR) is trivially computable. Our machine-learning-fundamental-analysis-based CDVaR portfolio optimization results are comparable to those of mainstream price-returns-based CVaR optimizers.

Keywords

Portfolio optimization Machine learning Risk management Downside risk Conditional value at risk Linear programming Fundamental analysis International financial reporting standards 

Notes

Acknowledgements

Attila Odabaşı initialized the author’s thoughts on using machine learning techniques in fundamental analysis. Ahmet Boyalı did the initial calculations in the machine learning problem. Murat G. Aktaş, CEO of Finnet Corp., provided us with guidance along with balance sheet data. Selahaddin Yıldırım wrote the Python code that handled the portfolio bookkeeping. The author is also grateful to the organizers of the LION 12 Conference at Kalamata. He also thanks the three anonymous referees, as well as Wolfgang Hörmann and Sevda Akyüz for reading the paper and providing him with ideas for a better presentation.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Namık Kemal University, Çorlu Mühendislik FakültesiÇorlu, TekirdağTurkey

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