التفاصيل البيبلوغرافية
| العنوان: |
Integrating prediction in mean-variance portfolio optimization. |
| المؤلفون: |
Butler, Andrew1 (AUTHOR), Kwon, Roy H.1 (AUTHOR) rkwon@mie.utoronto.ca |
| المصدر: |
Quantitative Finance. Mar2023, Vol. 23 Issue 3, p429-452. 24p. |
| مصطلحات موضوعية: |
Asset allocation, Prediction models, Forecasting, Analytical solutions, Regression analysis, Income inequality |
| مستخلص: |
In quantitative finance, prediction models are traditionally optimized independently from their use in the asset allocation decision-making process. We address this limitation and present a stochastic optimization framework for integrating regression prediction models in a mean-variance optimization (MVO) setting. Closed-form analytical solutions are provided for the unconstrained and equality constrained MVO case. For the general inequality constrained case, we make use of recent advances in neural-network architecture for efficient optimization of batch quadratic programs. To our knowledge, this is the first rigorous study of integrating prediction in a mean-variance portfolio optimization setting. We present several simulations, using both synthetic and global futures data, and demonstrate the benefits of the integrated approach in comparison to the decoupled alternative. [ABSTRACT FROM AUTHOR] |
| قاعدة البيانات: |
Finance Source |
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