Beyond Sequential Prediction: Learning Financial Market Dynamics in Volatile and Non-Stationary Environments through Sentiment-Conditioned Generative Modelling

Paper: arXiv 2604.22801

Authors: Alexis Lazanas, Spyridon Karpouzis

Abstract

The problem of time-series forecasting in non-stationary and complex environments is a challenging task in machine learning, especially with heterogeneous numerical and textual data present. Traditional statistical models like AutoRegressive Integrated Moving Average (ARIMA) are based on the assumptions of linearity and stationarity, whereas recurrent neural networks like Long Short-Term Memory (LSTM) models do not necessarily represent distributional properties in highly volatile settings. This paper proposes a hybrid model that combines Generative Adversarial Networks (GANs) with Natural Language Processing (NLP)-based sentiment analysis to enable sentiment-conditioned time-series prediction. The model integrates adversarial learning on numerical sequences with contextual sentiment representations derived from unstructured text, enabling them to be jointly modelled to capture temporal dynamics and exogenous information. These results demonstrate the promise of hybrid generative and language-aware methods to enhance prediction robustness in non-stationary environments.

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 5.5/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper introduces a compelling framework combining Generative Adversarial Networks (GANs) with sentiment analysis to model non-stationary financial dynamics. While mathematically sound and conceptually novel, the empirical validation lacks extensive multi-asset stress testing and public code release for full reproducibility.

Research Flowchart

  flowchart TD
    A["Research Goal: Robust Time-Series Forecasting<br/>in Non-Stationary Financial Markets"] --> B["Data Inputs: Heterogeneous Data<br/>(Numerical Time-Series + Unstructured Text)"]
    B --> C["NLP Sentiment Analysis<br/>(Extract Contextual Sentiment Features)"]
    B --> D["Numerical Sequence Processing<br/>(Capture Temporal Dynamics)"]
    C & D --> E["Sentiment-Conditioned GAN<br/>(Joint Generative Adversarial Learning)"]
    E --> F["Outcomes: Enhanced Prediction Robustness<br/>& Dynamic Distributional Representation"]