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Portfolio models with return forecasting and transaction costs

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  • Yu, Jing-Rung
  • Paul Chiou, Wan-Jiun
  • Lee, Wen-Yi
  • Lin, Shun-Ji

Abstract

In this paper, we advance portfolio models by incorporating return projection and further analyze their realized performance. To ensure practicality, the transaction costs and the optimization of short-selling weights are taken into account in portfolio rebalancing. Using the daily returns of international ETFs over a period of 14 years, the empirical results show that including return forecasting improves the realized performance due to more efficient asset allocation but not a reduction in trading costs. The models that are based on trade-off between return and volatility, such as the mean-variance and Omega models, show higher increases in performance than those mainly focus on controlling loss, such as the linearized value-at-risk, the conditional value-at-risk, and the downside risk. The superiority of forecasting risky portfolios over the equally-weighted diversification varies intertemporarily across various portfolio models. The benefit of inclusion of prediction is larger when the market is less volatile.

Suggested Citation

  • Yu, Jing-Rung & Paul Chiou, Wan-Jiun & Lee, Wen-Yi & Lin, Shun-Ji, 2020. "Portfolio models with return forecasting and transaction costs," International Review of Economics & Finance, Elsevier, vol. 66(C), pages 118-130.
  • Handle: RePEc:eee:reveco:v:66:y:2020:i:c:p:118-130
    DOI: 10.1016/j.iref.2019.11.002
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    Cited by:

    1. Hongxin Zhao & Yilun Jiang & Yizhou Yang, 2023. "Robust and Sparse Portfolio: Optimization Models and Algorithms," Mathematics, MDPI, vol. 11(24), pages 1-20, December.
    2. Spyridon D. Mourtas & Chrysostomos Kasimis, 2022. "Exploiting Mean-Variance Portfolio Optimization Problems through Zeroing Neural Networks," Mathematics, MDPI, vol. 10(17), pages 1-20, August.
    3. Ma, Yilin & Wang, Yudong & Wang, Weizhong & Zhang, Chong, 2023. "Portfolios with return and volatility prediction for the energy stock market," Energy, Elsevier, vol. 270(C).
    4. Chen, Wei & Zhang, Haoyu & Jia, Lifen, 2022. "A novel two-stage method for well-diversified portfolio construction based on stock return prediction using machine learning," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    5. Yong Zhang & Hong Lin & Lina Zheng & Xingyu Yang, 2022. "Adaptive online portfolio strategy based on exponential gradient updates," Journal of Combinatorial Optimization, Springer, vol. 43(3), pages 672-696, April.

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    More about this item

    Keywords

    Investment analysis; Portfolio rebalancing; Return forecasting; Multiple objectives; Transaction costs;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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