We consider the problem of constructing a perturbed portfolio by utilizing a benchmark portfolio. We propose two computationally efficient portfolio optimization models, the mean-absolute deviation risk and the Dantzig-type, which can be solved using linear programing. These portfolio models push the existing benchmark toward the efficient frontier through sparse and stable asset selection. We implement these models on two benchmarks, a market index and the equally-weighted portfolio. We carry out an extensive out-of-sample analysis with 11 empirical datasets and simulated data. The proposed portfolios outperform the benchmark portfolio in various performance measures, including the mean return and Sharpe ratio.
Bibliographical noteFunding Information:
This work was supported by National Institutes of Health (NIH) under Grants GM59507, CA154295, and CA196530; National Research Foundation of Korea under Grant NRF-2017R1A2B2005661.
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All Science Journal Classification (ASJC) codes
- Economics, Econometrics and Finance (miscellaneous)