Gradient Reduction Convolutional Neural Network Policy for Financial Deep Reinforcement Learning

ArXiv ID: 2408.11859 “View on arXiv”

Authors: Unknown

Abstract

Building on our prior explorations of convolutional neural networks (CNNs) for financial data processing, this paper introduces two significant enhancements to refine our CNN model’s predictive performance and robustness for financial tabular data. Firstly, we integrate a normalization layer at the input stage to ensure consistent feature scaling, addressing the issue of disparate feature magnitudes that can skew the learning process. This modification is hypothesized to aid in stabilizing the training dynamics and improving the model’s generalization across diverse financial datasets. Secondly, we employ a Gradient Reduction Architecture, where earlier layers are wider and subsequent layers are progressively narrower. This enhancement is designed to enable the model to capture more complex and subtle patterns within the data, a crucial factor in accurately predicting financial outcomes. These advancements directly respond to the limitations identified in previous studies, where simpler models struggled with the complexity and variability inherent in financial applications. Initial tests confirm that these changes improve accuracy and model stability, suggesting that deeper and more nuanced network architectures can significantly benefit financial predictive tasks. This paper details the implementation of these enhancements and evaluates their impact on the model’s performance in a controlled experimental setting.

Keywords: Convolutional Neural Networks (CNNs), Financial Tabular Data, Normalization Layer, Gradient Reduction Architecture, Deep Learning, General Financial Data (Tabular)

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 3.0/10
• Quadrant: Philosophers
• Why: The paper uses standard deep learning/RL concepts like MDPs and CNNs without advanced derivations, but describes a controlled experimental setting with hypothetical enhancements rather than presenting backtests or statistical metrics on real market data.

  flowchart TD
    A["Research Goal: Enhance CNN for Financial Tabular Data"] --> B["Methodology"]
    
    subgraph B ["Key Methodology Steps"]
        B1["Add Input Normalization Layer"]
        B2["Implement Gradient Reduction Architecture<br/>Wide Layer → Narrow Layer"]
    end
    
    B --> C["Input Data: General Financial Tabular Data"]
    C --> D["Computational Process<br/>1. Normalize Features<br/>2. Extract Complex Patterns via CNN<br/>3. Predict Financial Outcomes"]
    
    D --> E["Outcomes"]
    
    subgraph E ["Key Findings"]
        E1["Improved Prediction Accuracy"]
        E2["Enhanced Model Stability & Robustness"]
    end