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.
Scopus Subject Areas
- Computer Science Applications
- Computer Networks and Communications
- Artificial Intelligence
- Expected returns
- Extended adaptive supervised learning decision system (EASLD)
- Portfolio optimization schemes