Generalized Distribution Prediction for Asset Returns

ArXiv ID: 2410.23296 “View on arXiv”

Authors: Unknown

Abstract

We present a novel approach for predicting the distribution of asset returns using a quantile-based method with Long Short-Term Memory (LSTM) networks. Our model is designed in two stages: the first focuses on predicting the quantiles of normalized asset returns using asset-specific features, while the second stage incorporates market data to adjust these predictions for broader economic conditions. This results in a generalized model that can be applied across various asset classes, including commodities, cryptocurrencies, as well as synthetic datasets. The predicted quantiles are then converted into full probability distributions through kernel density estimation, allowing for more precise return distribution predictions and inferencing. The LSTM model significantly outperforms a linear quantile regression baseline by 98% and a dense neural network model by over 50%, showcasing its ability to capture complex patterns in financial return distributions across both synthetic and real-world data. By using exclusively asset-class-neutral features, our model achieves robust, generalizable results.

Keywords: Long Short-Term Memory (LSTM), Quantile Regression, Return Distribution Prediction, Kernel Density Estimation

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Holy Grail
  • Why: The paper presents advanced mathematical concepts including quantile regression, kernel density estimation, and statistical metrics like Wasserstein distance and CRPS, demonstrating high mathematical sophistication. It is highly empirical, utilizing real-world and synthetic datasets, public code repositories, backtest-ready evaluation metrics (VaR, CRPS), and rigorous out-of-sample testing across multiple asset classes.
  flowchart TD
    A["Research Goal: Predict<br>Asset Return Distributions"] --> B["Method: Two-Stage<br>LSTM Quantile Regression"]
    B --> C["Stage 1: Predict Asset-Specific<br>Quantiles using Features"]
    C --> D["Stage 2: Adjust Predictions<br>with Market Data"]
    D --> E["Compute: Kernel Density<br>Estimation (KDE)"]
    E --> F["Outcomes: Full Probability<br>Distribution Prediction"]
    F --> G{"Performance Results"}
    G --> H["98% improvement vs Linear Quantile Regression"]
    G --> I[">50% improvement vs Dense NN"]