MEAN-ABSOLUTE DEVIATION PORTFOLIO OPTIMIZATION MODEL AND ITS APPLICATIONS TO TOKYO STOCK-MARKET
MEAN-ABSOLUTE DEVIATION PORTFOLIO OPTIMIZATION MODEL AND ITS APPLICATIONS TO TOKYO STOCK-MARKET
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DOI：
10.1287/mnsc.37.5.519
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发表时间：
1991-05-01
影响因子：
5.4
通讯作者：
YAMAZAKI, H
中科院分区：
文献类型：
--
作者：
KONNO, H;YAMAZAKI, H
本文的目的是证明使用L1风险(平均绝对偏差风险)函数的投资组合优化模型可以消除经典Markowitz模型的大部分困难，同时保持其相对于均衡模型的优势。特别是，L1风险模型导致线性规划而不是二次规划，从而可以实时地解决由1000多只股票组成的大规模优化问题。利用日经225只股票的历史数据进行的数值实验表明，L1风险模型产生的投资组合与Markowitz模型非常相似，求解后者所需的时间很短。
The purpose of this paper is to demonstrate that a portfolio optimization model using the L1 risk (mean absolute deviation risk) function can remove most of the difficulties associated with the classical Markowitz's model while maintaining its advantages over equilibrium models. In particular, the L1 risk model leads to a linear program instead of a quadratic program, so that a large-scale optimization problem consisting of more than 1,000 stocks may be solved on a real time basis. Numerical experiments using the historical data of NIKKEI 225 stocks show that the L1 risk model generates a portfolio quite similar to that of the Markowitz's model within a fraction of time required to solve the latter.