Asset allocation model for a robo-advisor using the financial market instability index and genetic algorithms

Wonbin Ahn, Hee Soo Lee, Hosun Ryou, Kyong Joo Oh

Research output: Contribution to journalArticle


There has been a growing demand for portfolio management using robo-advisors, and hence, research on the automation of portfolio composition has been increasing. In this study, we propose a model that automates the portfolio structure by using the instability index of the financial time series and genetic algorithms (GAs). We use the instability index to filter the investment assets and optimize the threshold value used as a filtering criterion by applying a GA. For an empirical analysis, we use stocks, bonds, commodities exchange traded funds (ETFs), and exchange rate. We compare the performance of our model with that of risk parity and mean-variance models and find our model has better performance. Several additional experiments with our model using various internal parameters are conducted, and the proposed model with a one-month test period after one year of learning is found to provide the highest Sharpe ratio.

Original languageEnglish
Article number849
JournalSustainability (Switzerland)
Issue number3
Publication statusPublished - 2020 Feb 1


All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Management, Monitoring, Policy and Law

Cite this