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Asset Allocation Model for a Robo-Advisor Using the Financial Market Instability Index and Genetic Algorithms

Author

Listed:
  • Wonbin Ahn

    (Center of Bionics, Korea Institute of Science and Technology, Seoul 02792, Korea)

  • Hee Soo Lee

    (Department of Business Administration, Sejong University, Seoul 05006, Korea)

  • Hosun Ryou

    (Department of Industrial Engineering, Yonsei University, Seoul 03722, Korea)

  • Kyong Joo Oh

    (Department of Industrial Engineering, Yonsei University, Seoul 03722, Korea)

Abstract

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.

Suggested Citation

  • Wonbin Ahn & Hee Soo Lee & Hosun Ryou & Kyong Joo Oh, 2020. "Asset Allocation Model for a Robo-Advisor Using the Financial Market Instability Index and Genetic Algorithms," Sustainability, MDPI, vol. 12(3), pages 1-15, January.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:3:p:849-:d:312362
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    References listed on IDEAS

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    Cited by:

    1. Yongjae Lee & Woo Chang Kim & Jang Ho Kim, 2020. "Achieving Portfolio Diversification for Individuals with Low Financial Sustainability," Sustainability, MDPI, vol. 12(17), pages 1-16, August.
    2. Ko, Hyungjin & Byun, Junyoung & Lee, Jaewook, 2023. "A privacy-preserving robo-advisory system with the Black-Litterman portfolio model: A new framework and insights into investor behavior," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 89(C).
    3. Laurentiu-Mihai Ionescu & Nicu Bizon & Alin-Gheorghita Mazare & Nadia Belu, 2020. "Reducing the Cost of Electricity by Optimizing Real-Time Consumer Planning Using a New Genetic Algorithm-Based Strategy," Mathematics, MDPI, vol. 8(7), pages 1-26, July.
    4. Mike K. P. So, 2021. "Robo-Advising Risk Profiling through Content Analysis for Sustainable Development in the Hong Kong Financial Market," Sustainability, MDPI, vol. 13(3), pages 1-15, January.

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