Forecasting stock return distributions around the globe with quantile neural networks

Jozef Baruník, Martin Hronec and Ondrej Tobek

Papers from arXiv.org

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.

Date: 2024-08, Revised 2025-08
New Economics Papers: this item is included in nep-big and nep-mac
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