Paper: arXiv 2303.09407

Authors: Keer Yang, Guanqun Zhang, Chuan Bi, Qiang Guan, Hailu Xu, Shuai Xu

Abstract

In recent years, there have been quite a few attempts to apply intelligent techniques to financial trading, i.e., constructing automatic and intelligent trading framework based on historical stock price. Due to the unpredictable, uncertainty and volatile nature of financial market, researchers have also resorted to deep learning to construct the intelligent trading framework. In this paper, we propose to use CNN as the core functionality of such framework, because it is able to learn the spatial dependency (i.e., between rows and columns) of the input data. However, different with existing deep learning-based trading frameworks, we develop novel normalization process to prepare the stock data. In particular, we first empirically observe that the stock data is intrinsically heterogeneous and bursty, and then validate the heterogeneity and burst nature of stock data from a statistical perspective. Next, we design the data normalization method in a way such that the data heterogeneity is preserved and bursty events are suppressed. We verify out developed CNN-based trading framework plus our new normalization method on 29 stocks. Experiment results show that our approach can outperform other comparing approaches.

Complexity vs Empirical Score

  • Math Complexity: 4.5/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper introduces a novel data normalization scheme to address stock data heterogeneity and burstiness for CNN models. While the mathematical framework is relatively basic, the empirical validation across 29 stocks provides decent rigor for the proposed deep learning pipeline.

Research Flowchart

  flowchart TD
    A[Goal: Improve CNN Stock Trading by Handling Data Heterogeneity & Burst] --> B[Data Input: Historical Price Data for 29 Stocks]
    B --> C[Statistical Analysis: Validate Stock Data Heterogeneity & Burstiness]
    C --> D[Novel Data Normalization: Preserve Heterogeneity & Suppress Burst Events]
    D --> E[CNN Framework: Learn Spatial Dependency in Prepared Stock Matrices]
    E --> F[Findings: Superior Trading Performance Over Baseline Approaches]