Forecasting stock return distributions around the globe with quantile neural networks

ArXiv ID: 2408.07497 “View on arXiv”

Authors: Unknown

Abstract

We propose a novel machine learning approach for forecasting the distribution of stock returns using a rich set of firm-level and market predictors. Our method combines a two-stage quantile neural network with spline interpolation to construct smooth, flexible cumulative distribution functions without relying on restrictive parametric assumptions. This allows accurate modelling of non-Gaussian features such as fat tails and asymmetries. Furthermore, we show how to derive other statistics from the forecasted return distribution such as mean, variance, skewness, and kurtosis. The derived mean and variance forecasts offer significantly improved out-of-sample performance compared to standard models. We demonstrate the robustness of the method in US and international markets.

Keywords: Quantile Neural Networks, Spline Interpolation, Return Distribution Forecasting, Fat Tails, Asymmetry, Stocks (US and International)

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced machine learning methods (quantile neural networks, spline interpolation) and detailed mathematical derivations for distribution forecasting, resulting in high math complexity. It demonstrates strong empirical rigor through extensive out-of-sample testing across multiple markets, comparison to benchmarks, and derivation of practical statistics like mean and variance.

  flowchart TD
    A["Research Goal:<br>Forecast Stock Return Distributions"] --> B["Data & Predictors"]
    B --> C["Two-Stage Quantile Neural Network"]
    C --> D["Spline Interpolation<br>to CDFs"]
    D --> E["Compute Statistics<br>Mean, Variance, Skewness, Kurtosis"]
    E --> F["Key Outcomes<br>Accurate Non-Gaussian Modelling<br>Superior Mean/Variance Forecasts<br>Robust in US & Int'l Markets"]
    
    subgraph Inputs
        B
    end