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A Random Forests Based Performance Ratio for Regulatory Asset Portfolio Management and Optimization


Wälchli, Boris (2015). A Random Forests Based Performance Ratio for Regulatory Asset Portfolio Management and Optimization. SSRN Electronic Journal 2550072, University of Zurich.

Abstract

The following paper proposes a portfolio performance measure to optimize, mostly bond asset portfolios usually held for regulatory purposes from a risk focused perspective. The measure is based on variations of the proximity measure introduced by the Random Forests framework, leading to a proximity based performance ratio. The proximities are modeled using a recursive conditional partitioning type of Random Forests, which allows for a ranking as well as an analysis of the risk drivers of the portfolio performance. The proximity based performance ratio is shown to, on average, outperform nine different and commonly known risk and performance ratios as well as the 1/N-balanced portfolio in three different tests, in- and out of the sample. The proximity based performance ratio can consider a large amount of risk rivers and is suitable for big data analysis for big and small financial institutions.

Abstract

The following paper proposes a portfolio performance measure to optimize, mostly bond asset portfolios usually held for regulatory purposes from a risk focused perspective. The measure is based on variations of the proximity measure introduced by the Random Forests framework, leading to a proximity based performance ratio. The proximities are modeled using a recursive conditional partitioning type of Random Forests, which allows for a ranking as well as an analysis of the risk drivers of the portfolio performance. The proximity based performance ratio is shown to, on average, outperform nine different and commonly known risk and performance ratios as well as the 1/N-balanced portfolio in three different tests, in- and out of the sample. The proximity based performance ratio can consider a large amount of risk rivers and is suitable for big data analysis for big and small financial institutions.

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Additional indexing

Item Type:Working Paper
Communities & Collections:03 Faculty of Economics > Department of Banking and Finance
Dewey Decimal Classification:330 Economics
Language:English
Date:4 November 2015
Deposited On:01 Mar 2016 07:45
Last Modified:08 Dec 2017 19:11
Series Name:SSRN Electronic Journal
Number of Pages:35
ISSN:1556-5068
Free access at:Official URL. An embargo period may apply.
Official URL:http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2550072
Other Identification Number:merlin-id:13080

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