【摘要】：為得到魯棒、稀疏的投資組合,提出稀疏魯棒M-投資選擇模型,并且基于L1/2正則化理論和Half閾值算法,構(gòu)建魯棒Half閾值算法求解稀疏魯棒M-投資選擇問題.數(shù)值實驗表明,該算法不僅比Lasso算法收斂速度更快,而且在期望值固定的情況下得到的風(fēng)險更小、更平穩(wěn).
[Abstract]:In order to obtain a robust and sparse portfolio, a sparse robust M- investment selection model is proposed. Based on L1 / 2 regularization theory and Half threshold algorithm, a robust Half threshold algorithm is constructed to solve the sparse robust M- investment selection problem. Numerical experiments show that the proposed algorithm not only converges faster than the Lasso algorithm, but also has a lower and more stable risk when the expected value is fixed.
【作者單位】： 西安工程大學(xué)理學(xué)院;
【基金】：國家自然科學(xué)基金資助項目(11201362) 陜西省教育廳自然科學(xué)專項基金資助項目(14JK1305)
【分類號】：F224;F830.59
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本文編號：2399861