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Integrating Different Informations for Portfolio Selection

Author

Listed:
  • Yi Huang
  • Wei Zhu
  • Duan Li
  • Shushang Zhu
  • Shikun Wang

Abstract

Following the idea of Bayesian learning via Gaussian mixture model, we organically combine the backward-looking information contained in the historical data and the forward-looking information implied by the market portfolio, which is affected by heterogeneous expectations and noisy trading behavior. The proposed combined estimation adaptively harmonizes these two types of information based on the degree of market efficiency and responds quickly at turning points of the market. Both simulation experiments and a global empirical test confirm that the approach is a flexible and robust forecasting tool and is applicable to various capital markets with different degrees of efficiency.

Suggested Citation

  • Yi Huang & Wei Zhu & Duan Li & Shushang Zhu & Shikun Wang, 2023. "Integrating Different Informations for Portfolio Selection," Papers 2305.17881, arXiv.org.
    • Handle: RePEc:arx:papers:2305.17881
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    References listed on IDEAS

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