Stock Volume Forecasting with Advanced Information by Conditional Variational Auto-Encoder

ArXiv ID: 2406.19414 “View on arXiv”

Authors: Unknown

Abstract

We demonstrate the use of Conditional Variational Encoder (CVAE) to improve the forecasts of daily stock volume time series in both short and long term forecasting tasks, with the use of advanced information of input variables such as rebalancing dates. CVAE generates non-linear time series as out-of-sample forecasts, which have better accuracy and closer fit of correlation to the actual data, compared to traditional linear models. These generative forecasts can also be used for scenario generation, which aids interpretation. We further discuss correlations in non-stationary time series and other potential extensions from the CVAE forecasts.

Keywords: Conditional Variational Encoder (CVAE), Stock Volume Forecasting, Time Series Generation, Scenario Generation, Non-stationary Data, Equities

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper introduces advanced mathematical concepts such as Conditional Variational Auto-Encoders, non-linear latent variable models, and generative forecasting schemes with heavy LaTeX formalism. It also includes empirical analysis on real stock volume data (EURO STOXX 50), compares against linear baselines, and discusses practical applications like derivative pricing, indicating substantial implementation and backtesting effort.

  flowchart TD
    A["Research Goal:<br>Forecast Stock Volume<br>using Advanced Information"] --> B["Data & Inputs:<br>Stock Volume Time Series +<br>Advanced Info e.g. Rebalancing Dates"]
    B --> C["Methodology:<br>Conditional Variational Auto-Encoder<br>Generative Deep Learning"]
    C --> D["Computational Process:<br>Training on Non-Stationary Data<br>to Learn Conditional Latent Distributions"]
    D --> E{"Forecasting Phase"}
    E --> F["Short-Term Forecasting"]
    E --> G["Long-Term Forecasting &<br>Scenario Generation"]
    F --> H["Key Findings/Outcomes:<br>Superior Accuracy vs Linear Models<br>Better Correlation Fit<br>Interpretable Generative Scenarios"]
    G --> H