Conformal Predictive Portfolio Selection

ArXiv ID: 2410.16333 “View on arXiv”

Authors: Unknown

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.

Keywords: Conformal Prediction, Portfolio Selection, Predictive Models, Prediction Intervals, Risk Management, Equities

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 5.5/10
  • Quadrant: Holy Grail
  • Why: The paper involves advanced statistical theory (conformal prediction) with rigorous probability statements and proofs, scoring high on math complexity, while it includes empirical validation with specific models and performance comparisons, indicating moderate implementation and data-heavy work.
  flowchart TD
    A["Research Goal:<br>Conformal Predictive Portfolio Selection"] --> B["Data Input:<br>Historical Asset Returns"]
    B --> C["Methodology:<br>Fit Predictive Model<br>e.g., AR, RF, NN"]
    C --> D["Computation:<br>Conformal Prediction<br>Generate Prediction Intervals"]
    D --> E["Decision:<br>Select Portfolio based on<br>Quantile/Interval Metrics"]
    E --> F["Outcome:<br>Superior Returns<br>with Valid Risk Coverage"]