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An extended ASLD trading system to enhance portfolio management | IEEE Journals & Magazine | IEEE Xplore

An extended ASLD trading system to enhance portfolio management


Abstract:

An adaptive supervised learning decision (ASLD) trading system has been presented by Xu and Cheung (1997) to optimize the expected returns of investment without consideri...Show More

Abstract:

An adaptive supervised learning decision (ASLD) trading system has been presented by Xu and Cheung (1997) 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.
Published in: IEEE Transactions on Neural Networks ( Volume: 14, Issue: 2, March 2003)
Page(s): 413 - 425
Date of Publication: 31 March 2003

ISSN Information:

PubMed ID: 18238023
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I. Introduction

In the literature, various theories and methods have been developed to help investors to pursue great profits in the investment. One is a computer-based trading system that produces appropriate investment decision signals (also called trading signals hereafter) on the basis of the available information to assist an investor make a sensible investment. In the past, one kind of widely used trading systems consists of two modules: prediction module followed by trading module. However, this type of trading system is optimized to some prediction criterion (e.g., mean square error), which is not the ultimate goal of a financial investment. Therefore, it often leads to suboptimal performance in the profit-achieved sense. To solve this problem, some efforts have been made along different directions. One is to use a prediction criterion more correlated with common trading strategies such as that proposed in [2]. Another direction is the return-based systems as proposed in the papers [1], [10], where the prediction module and the trading module are merged into one single system that optimizes the returns instead of the prediction criterion.

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