TY - GEN
T1 - An Empirical Comparison of Cross-Validation Procedures for Portfolio Selection
AU - Paskaramoorthy, Andrew
AU - Van Zyl, Terence L.
AU - Gebbie, Tim
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - We present the constrained portfolio selection problem as a learning problem requiring hyper-parameter specification. In practice, hyper-parameters are typically selected using a validation procedure, of which there are several widely-used alternatives. However, the performance of different validation procedures is problem dependent and has not been investigated for the portfolio selection problem. This study examines the behaviour of common validation procedures, including holdout, k-fold cross-validation, Monte Carlo cross-validation, and repeated k-fold cross-validation for estimating performance and selecting hyper-parameters for constrained portfolio selection. The results demonstrate that repeated k-fold cross-validation is the best performing procedure and recommend using 5 repetitions with 3 ≤ k ≤ 10 in practice.
AB - We present the constrained portfolio selection problem as a learning problem requiring hyper-parameter specification. In practice, hyper-parameters are typically selected using a validation procedure, of which there are several widely-used alternatives. However, the performance of different validation procedures is problem dependent and has not been investigated for the portfolio selection problem. This study examines the behaviour of common validation procedures, including holdout, k-fold cross-validation, Monte Carlo cross-validation, and repeated k-fold cross-validation for estimating performance and selecting hyper-parameters for constrained portfolio selection. The results demonstrate that repeated k-fold cross-validation is the best performing procedure and recommend using 5 repetitions with 3 ≤ k ≤ 10 in practice.
KW - constrained portfolio optimization
KW - cross-validation
KW - hyper-parameter optimization
KW - machine learning
KW - portfolio optimization
KW - portfolio selection
UR - https://www.scopus.com/pages/publications/85130934935
U2 - 10.1109/CIFEr52523.2022.9776132
DO - 10.1109/CIFEr52523.2022.9776132
M3 - Conference contribution
AN - SCOPUS:85130934935
T3 - 2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics, CIFEr 2022 - Proceedings
BT - 2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics, CIFEr 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics, CIFEr 2022
Y2 - 4 May 2022 through 5 May 2022
ER -