Modeling the Momentum Effect in Stock Markets to Propose a New Portfolio Algorithm

Abstract

This research has two objectives: (1) to model and analyze the momentum effect and (2) to propose a portfolio-reconstruction algorithm that uses the momentum effect to obtain excess return. The momentum effect tends to be present in the stock market and describes the phenomenon whereby rising (declining) stocks tend to continue to rise (decline). However, because existing research does not separate momentum effects from stock price fluctuations, it is not always possible to obtain an excess return when working with an unknown dataset that contains a momentum effect. In this research, we define a new external-force momentum-effect (EFME) model based on bias in stock price rises (declines). We prepared an artificial stock dataset that contained this momentum effect and constructed a portfolio with the proposed algorithm. Then, we analyzed the relationship between the EFME model and excess return and verify that excess return is obtained. Additionally, we confirmed that the proposed method yields higher excess return than the existing method when applied to artificial and real stock datasets.