TY - JOUR
T1 - An extended ASLD trading system to enhance portfolio management
AU - Hung, Kei Keung
AU - CHEUNG, Yiu Ming
AU - Xu, Lei
N1 - Funding Information:
Manuscript received January 22, 2000; revised October 12, 2001. This work was supported by the Research Grant Council of the Hong Kong SAR under Project RGC Earmarked CUHK 4297/98E. K. Hung and L. Xu are with the Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, P. R. China (e-mail: [email protected]). Y. Cheung is with the Department of Computer Science, Hong Kong Baptist University, Hong Kong, P. R. China (e-mail: [email protected]). Digital Object Identifier 10.1109/TNN.2003.809423
PY - 2003/3
Y1 - 2003/3
N2 - An adaptive supervised learning decision (ASLD) trading system has been presented by Xu and Cheung to optimize the expected returns of investment without considering risks. In this paper, we propose an extension of the ASLD system (EASLD), which combines the ASLD with a portfolio optimization scheme to take a balance between the expected returns and risks. This new system not only keeps the learning adaptability of the ASLD, but also dynamically controls the risk in pursuit of great profits by diversifying the capital to a time-varying portfolio of N assets. Consequently, it is shown that 1) the EASLD system gives the investment risk much smaller than the ASLD one and 2) more returns are gained through the EASLD system in comparison with the two individual portfolio optimization schemes that statically determine the portfolio weights without adaptive learning. We have justified these two issues by the experiments.
AB - An adaptive supervised learning decision (ASLD) trading system has been presented by Xu and Cheung to optimize the expected returns of investment without considering risks. In this paper, we propose an extension of the ASLD system (EASLD), which combines the ASLD with a portfolio optimization scheme to take a balance between the expected returns and risks. This new system not only keeps the learning adaptability of the ASLD, but also dynamically controls the risk in pursuit of great profits by diversifying the capital to a time-varying portfolio of N assets. Consequently, it is shown that 1) the EASLD system gives the investment risk much smaller than the ASLD one and 2) more returns are gained through the EASLD system in comparison with the two individual portfolio optimization schemes that statically determine the portfolio weights without adaptive learning. We have justified these two issues by the experiments.
KW - Expected returns
KW - Extended adaptive supervised learning decision system (EASLD)
KW - Portfolio optimization schemes
KW - Risk
UR - http://www.scopus.com/inward/record.url?scp=0344951095&partnerID=8YFLogxK
U2 - 10.1109/TNN.2003.809423
DO - 10.1109/TNN.2003.809423
M3 - Journal article
AN - SCOPUS:0344951095
SN - 1045-9227
VL - 14
SP - 413
EP - 425
JO - IEEE Transactions on Neural Networks
JF - IEEE Transactions on Neural Networks
IS - 2
ER -