Portfolio analysis with a large universe of assets.

Covariance matrix optimization algorithms are applied to a large number of assets. A previous paper by Burgess and Bey (1988) suggests that attempting to optimize a large number of securities with the traditional covariance matrix model is not practical. An alternative approach ranks the securities with the reward to volatility (reward to beta) ratio and then optimizes a smaller subset of securities with the covariance matrix model. This study proposes additional screening methods such as stochastic dominance, reward to variability (R/V) ratios, reward to lower partial moment (R/LPM) ratios, and the optimization of subgroups, and provides an empirical test of the various screening methodologies. The results indicate that the full covariance critical line optimization algorithm is surprisingly robust compared to the other techniques.

Main Author: Nawrocki, David.
Language: English
Published: 1996
Online Access: http://ezproxy.villanova.edu/login?url=https://digital.library.villanova.edu/Item/vudl:178249
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dc_source_str_mv Applied Economics, 1996, 28, 1191-1198.
author Nawrocki, David.
author_s Nawrocki, David.
spellingShingle Nawrocki, David.
Portfolio analysis with a large universe of assets.
author-letter Nawrocki, David.
author_sort_str Nawrocki, David.
dc_title_str Portfolio analysis with a large universe of assets.
title Portfolio analysis with a large universe of assets.
title_short Portfolio analysis with a large universe of assets.
title_full Portfolio analysis with a large universe of assets.
title_fullStr Portfolio analysis with a large universe of assets.
title_full_unstemmed Portfolio analysis with a large universe of assets.
collection_title_sort_str portfolio analysis with a large universe of assets.
title_sort portfolio analysis with a large universe of assets.
description Covariance matrix optimization algorithms are applied to a large number of assets. A previous paper by Burgess and Bey (1988) suggests that attempting to optimize a large number of securities with the traditional covariance matrix model is not practical. An alternative approach ranks the securities with the reward to volatility (reward to beta) ratio and then optimizes a smaller subset of securities with the covariance matrix model. This study proposes additional screening methods such as stochastic dominance, reward to variability (R/V) ratios, reward to lower partial moment (R/LPM) ratios, and the optimization of subgroups, and provides an empirical test of the various screening methodologies. The results indicate that the full covariance critical line optimization algorithm is surprisingly robust compared to the other techniques.
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dc.title Portfolio analysis with a large universe of assets.
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dc.description Covariance matrix optimization algorithms are applied to a large number of assets. A previous paper by Burgess and Bey (1988) suggests that attempting to optimize a large number of securities with the traditional covariance matrix model is not practical. An alternative approach ranks the securities with the reward to volatility (reward to beta) ratio and then optimizes a smaller subset of securities with the covariance matrix model. This study proposes additional screening methods such as stochastic dominance, reward to variability (R/V) ratios, reward to lower partial moment (R/LPM) ratios, and the optimization of subgroups, and provides an empirical test of the various screening methodologies. The results indicate that the full covariance critical line optimization algorithm is surprisingly robust compared to the other techniques.
dc.date 1996
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dc.source Applied Economics, 1996, 28, 1191-1198.
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