Dynamic portfolio rebalancing with lag-optimised trading indicators using SeroFAM and genetic algorithms

Yeo, L. L. X., Cao, Q. and Quek, C. (2023) Dynamic portfolio rebalancing with lag-optimised trading indicators using SeroFAM and genetic algorithms. Expert Systems with Applications, 216, 119440. (doi: 10.1016/j.eswa.2022.119440)

Abstract

Some common technical indicators, such as moving average convergence divergence (MACD), relative strength index (RSI), and MACD histogram (MACDH) are used in technical analyses and stock trading. However, some of them are lagging indicators, affecting the effectiveness in the stock trading and portfolio management. A forecasted MACDH (fMACDH) indicator for predicting next day price by a neuro-fuzzy network, Self-reorganizing Fuzzy Associative Machine (SeroFAM) which has been reported in the prior research work. In order to further reduce the lagging effect, two trading indicators are proposed in this paper: the optimised fMACDH indicator and the fMACDH-fRSI indicator. The optimised fMACDH indicator is derived to extend price forecasting to 1-5 days ahead as the prediction depth, using 1-5 days of historical price data as the input depth. The fMACDH-fRSI indicator is derived by combining the optimized fMACDH indicator and the forecasted RSI (fRSI) indicator. A genetic algorithm (GA) and the fitness functions are designed with the SeroFAM in this paper, which are utilised for optimising parameters of these two proposed indicators. Experiments have been conducted to evaluate and benchmark of the proposed trading indicators optimised by the GA. Two rule-based portfolio rebalancing algorithms are then proposed using the optimised fMACDH trading indicator tuned by the GA: the Tactical Buy and Hold (TBH) and the Rule-Based Business Cycle (RBBC) portfolio rebalancing algorithms. The TBH algorithm takes advantage of relative differences in risk levels to perform rebalancing during trend reversals. The RBBC portfolio rebalancing algorithm takes advantage of the offsets between the business cycles of different market sectors. Experiments have been conducted to evaluate the performance of both algorithms using two sets of portfolios consisting of different assets. The TBH portfolio rebalancing algorithm outperforms the equally weighted portfolio strategy by about 26% - 27%; as well outperforms the Buy and Hold strategy by 5% - 40%. The RBBC portfolio rebalancing algorithm outperforms the equally weighted portfolio strategy by 54% - 55%; it also outperforms 12 out of the 13 assets with the Buy and Hold strategy, by an average performance of about 166%. The results are highly encouraging with consistent performances achieved in dynamic portfolio rebalancing.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Cao, Dr Qi

Creator Roles

Cao, Q.: Conceptualization, Methodology, Formal analysis, Investigation, Writing – original draft, Writing – review and editing

• Authors: Yeo, L. L. X., Cao, Q., and Quek, C.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Expert Systems with Applications
• Publisher: Elsevier
• ISSN: 0957-4174
• ISSN (Online): 1873-6793
• Published Online: 14 December 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Expert Systems with Applications 216: 119440
• Publisher Policy: Reproduced under a Creative Commons License