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<p>Portfolio selection is a critical issue in financial management under uncertainty. In this paper, we propose a complex approach for portfolio selection with quantile return approximation. In particular, we propose a practical workflow that combines principal component analysis with quantile regression into the Quantile Regression-Principal Component Analysis (QR-PCA) framework. The use of quantile regression allows to capture asymmetric and heterogeneous conditional behavior of return distributions. This strengthens the dimensionality reduction and robust regression techniques. For comparison, we consider parametric and nonparametric approximation techniques. We also design a new performance measure called quantile ratio (qR) based on approximate quantile expectations of returns incorporated in a portfolio optimization task. The proposed model is applied to a real-world dataset of financial assets, demonstrating its effectiveness in constructing portfolios that outperform traditional portfolio models. The empirical results reveal better risk-adjusted performance compared to those optimized using traditional models.</p>

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Quantile regression–PCA framework in portfolio selection process

  • 期刊论文

  • 发表日期：

    2026年5月9日

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  • David Neděla,
  • Audrius Kabašinskas,
  • Megang Nkamga Junile Staures

摘要

Portfolio selection is a critical issue in financial management under uncertainty. In this paper, we propose a complex approach for portfolio selection with quantile return approximation. In particular, we propose a practical workflow that combines principal component analysis with quantile regression into the Quantile Regression-Principal Component Analysis (QR-PCA) framework. The use of quantile regression allows to capture asymmetric and heterogeneous conditional behavior of return distributions. This strengthens the dimensionality reduction and robust regression techniques. For comparison, we consider parametric and nonparametric approximation techniques. We also design a new performance measure called quantile ratio (qR) based on approximate quantile expectations of returns incorporated in a portfolio optimization task. The proposed model is applied to a real-world dataset of financial assets, demonstrating its effectiveness in constructing portfolios that outperform traditional portfolio models. The empirical results reveal better risk-adjusted performance compared to those optimized using traditional models.