Strategic portfolio rebalancing: Integrating predictive models and adaptive optimization objectives in a dynamic market

Abstract

Adjusting investment strategy is one of the ways to handle dynamic market conditions. This study proposes a novel portfolio management strategy using appropriate optimization objectives for different stock market trends while also incorporating market trends and stock return predictions The optimization objectives that will be evaluated for different market trends are maximizing the Sharpe ratio, minimizing risk, and minimizing expected shortfall. This study utilizes simulation modelling with various predictive models on building the portfolios. The results show that, in an upward market trend, the strategy is to choose stocks with positive returns, and the objective is to maximize the Sharpe ratio. The portfolio that follows this strategy during upward market trends has greater returns than both the Indonesian Composite Index and LQ45, which serve as stock market benchmarks, with 90% certainty. Meanwhile, during the downward market trend, the strategy is to choose stocks with a negative correlation with the Indonesian Composite Index, and the proper optimization objective is to minimize risk. A portfolio that follows this strategy during downward market trends has greater returns than stock market benchmarks with 95% certainty. Across the evaluation period from 2018 to 2023, the portfolio using the proposed strategy outperforms both stock market benchmarks, with a higher quarterly Sharpe ratio of 0.3047 and cumulative return of 107.90%. The proposed portfolio has a higher quarterly return than the stock market benchmark with 99% certainty. Therefore, the proposed strategy shows a promising result in a dynamic market.