Paper: arXiv 2604.16835

Authors: Haibin Jiao

Abstract

Shanghai Composite Index prediction has become a hot issue for many investors and academic researchers. Deep learning models are widely applied in multivariate time series forecasting, including recurrent neural networks (RNN), convolutional neural networks (CNN), and transformers. Specifically, the Transformer encoder, with its unique attention mechanism and parallel processing capabilities, has become an important tool in time series prediction, and has an advantage in dealing with long sequence dependencies and multivariate data correlations. Drawing on the strengths of various models, we propose the CNN-Transformer-LSTM Networks (CTLNet). This paper explores the application of CTLNet for Shanghai Composite Index prediction and the comparative experiments show that the proposed model outperforms state-of-the-art baselines.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 3.0/10
  • Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper relies on standard, well-documented deep learning model architectures (CNN, Transformer, LSTM) without providing novel mathematical derivations or deep theoretical proofs. The empirical section is generic, lacking detailed backtesting methodology, performance attribution, or rigorous transaction cost analysis relevant to actual trading applications.

Research Flowchart

  flowchart TD
    A[Research Goal: Predict Shanghai Composite Index] --> B[Data Input: Multivariate Time Series]
    B --> C[Methodology: CTLNet Architecture]
    C --> D[Computational Process: CNN-Transformer-LSTM]
    D --> E[Training & Comparative Experiments]
    E --> F[Outcome: Outperforms State-of-the-Art Baselines]