Conformal Predictive Portfolio Selection

TL;DR

This paper introduces Conformal Predictive Portfolio Selection (CPPS), a flexible framework that uses conformal prediction to forecast portfolio returns and select portfolios based on prediction intervals, improving performance over traditional methods.

Contribution

The paper proposes a novel conformal prediction-based framework for portfolio selection that accommodates various predictive models and enhances return performance.

Findings

CPPS delivers superior returns compared to simpler strategies.

The framework is flexible and compatible with multiple predictive models.

Empirical validation confirms the effectiveness of CPPS.

Abstract

This study examines portfolio selection using predictive models for portfolio returns. Portfolio selection is a fundamental task in finance, and a variety of methods have been developed to achieve this goal. For instance, the mean-variance approach constructs portfolios by balancing the trade-off between the mean and variance of asset returns, while the quantile-based approach optimizes portfolios by considering tail risk. These methods often depend on distributional information estimated from historical data using predictive models, each of which carries its own uncertainty. To address this, we propose a framework for predictive portfolio selection via conformal prediction , called \emph{Conformal Predictive Portfolio Selection} (CPPS). Our approach forecasts future portfolio returns, computes the corresponding prediction intervals, and selects the portfolio of interest based on these intervals. The framework is flexible and can accommodate a wide range of predictive models, including autoregressive (AR) models, random forests, and neural networks. We demonstrate the effectiveness of the CPPS framework by applying it to an AR model and validate its performance through empirical studies, showing that it delivers superior returns compared to simpler strategies.