首页 | 本学科首页   官方微博 | 高级检索  
     检索      


A descent algorithm for constrained LAD-Lasso estimation with applications in portfolio selection
Authors:Yue Shi  Zhiguo Feng  Ka-Fai Cedric Yiu
Institution:1. School of Mathematics and Systems Science, Beihang University, Beijing, People's Republic of China;2. Department of Mathematics and Computer Science, Guangdong Ocean University, Guangdong, People's Republic of China;3. Department of Applied Mathematics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Abstract:To improve the out-of-sample performance of the portfolio, Lasso regularization is incorporated to the Mean Absolute Deviance (MAD)-based portfolio selection method. It is shown that such a portfolio selection problem can be reformulated as a constrained Least Absolute Deviance problem with linear equality constraints. Moreover, we propose a new descent algorithm based on the ideas of ‘nonsmooth optimality conditions’ and ‘basis descent direction set’. The resulting MAD-Lasso method enjoys at least two advantages. First, it does not involve the estimation of covariance matrix that is difficult particularly in the high-dimensional settings. Second, sparsity is encouraged. This means that assets with weights close to zero in the Markovwitz's portfolio are driven to zero automatically. This reduces the management cost of the portfolio. Extensive simulation and real data examples indicate that if the Lasso regularization is incorporated, MAD portfolio selection method is consistently improved in terms of out-of-sample performance, measured by Sharpe ratio and sparsity. Moreover, simulation results suggest that the proposed descent algorithm is more time-efficient than interior point method and ADMM algorithm.
Keywords:MAD-Lasso  portfolio selection  constrained LAD Lasso  nonsmooth optimality conditions  Sharpe ratio  sparsity
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号