Portfolio optimization when expected stock returns are determined by exposure to risk
Carl Lindberg
Source: Bernoulli
Volume 15, Number 2
(2009), 464-474.
Abstract
It is widely recognized that when classical optimal strategies are applied with parameters estimated from data, the resulting portfolio weights are remarkably volatile and unstable over time. The predominant explanation for this is the difficulty of estimating expected returns accurately. In this paper, we modify the n stock Black–Scholes model by introducing a new parametrization of the drift rates. We solve Markowitz’ continuous time portfolio problem in this framework. The optimal portfolio weights correspond to keeping 1/n of the wealth invested in stocks in each of the n Brownian motions. The strategy is applied out-of-sample to a large data set. The portfolio weights are stable over time and obtain a significantly higher Sharpe ratio than the classical 1/n strategy.
Keywords: 1/n strategy; Black–Scholes model; expected stock returns; Markowitz’ problem; portfolio optimization; ranks
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Links and Identifiers
Permanent link to this document: http://projecteuclid.org/euclid.bj/1241444898
Digital Object Identifier: doi:10.3150/08-BEJ163
Mathematical Reviews number (MathSciNet):
MR2543870
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