Developing a dynamic portfolio selection model with a self-adjusted rebalancing method

Developing a dynamic portfolio selection model with a self-adjusted rebalancing method

0.00 Avg rating0 Votes
Article ID: iaor20172627
Volume: 68
Issue: 7
Start Page Number: 766
End Page Number: 779
Publication Date: Jul 2017
Journal: J Oper Res Soc
Authors: ,
Keywords: investment, decision, combinatorial optimization, programming: dynamic, optimization: simulated annealing, heuristics, learning
Abstract:

In this paper, we propose a comprehensive investment strategy for not only selecting but also maintaining an investment portfolio that takes into account changing market conditions. First, we implement a dynamic portfolio selection model (DPSM) that uses a time‐varying investment target according to market forecasts. We then develop a self‐adjusted rebalancing (SAR) method to assess the portfolio’s relevance to current market conditions, and further identify the appropriate timing for rebalancing the portfolio. We then integrate the DPSM and SAR into a comprehensive investment strategy, and develop an adaptive learning heuristic for determining the parameter of the proposed investment strategy. We further evaluate the performance of the proposed investment strategy by simulating investments with historical stock return data from different markets around the world, across a period of 10 years. The SAR Portfolio, maintained according to the proposed investment strategy, showed superior performance compared with benchmarks in each of the target markets.

Reviews

Required fields are marked *. Your email address will not be published.