Hidformer: Transformer-Style Neural Network in Stock Price Forecasting

ArXiv ID: 2412.19932 “View on arXiv”

Authors: Unknown

Abstract

This paper investigates the application of Transformer-based neural networks to stock price forecasting, with a special focus on the intersection of machine learning techniques and financial market analysis. The evolution of Transformer models, from their inception to their adaptation for time series analysis in financial contexts, is reviewed and discussed. Central to our study is the exploration of the Hidformer model, which is currently recognized for its promising performance in time series prediction. The primary aim of this paper is to determine whether Hidformer will also prove itself in the task of stock price prediction. This slightly modified model serves as the framework for our experiments, integrating the principles of technical analysis with advanced machine learning concepts to enhance stock price prediction accuracy. We conduct an evaluation of the Hidformer model’s performance, using a set of criteria to determine its efficacy. Our findings offer additional insights into the practical application of Transformer architectures in financial time series forecasting, highlighting their potential to improve algorithmic trading strategies, including human decision making.

Keywords: Stock price forecasting, Transformer models, Hidformer, Technical analysis, Time series analysis

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 6.0/10
• Quadrant: Street Traders
• Why: The paper focuses on applying a known Transformer architecture (Hidformer) to financial data with standard deep learning practices like MSE loss, showing moderate empirical rigor through evaluation, but lacks deep mathematical derivations, keeping math complexity low.

  flowchart TD
    A["Research Goal:<br>Assess Hidformer for<br>Stock Price Forecasting"] --> B["Data Preparation<br>Financial Time Series<br>Technical Indicators"]
    B --> C["Model Architecture<br>Modified Hidformer<br>Transformer-based"]
    C --> D["Experimental Process<br>Training & Validation"]
    D --> E{"Performance Evaluation"}
    E --> F["Key Finding 1: Positive<br>Effective for Stock Prediction"]
    E --> G["Key Finding 2: Impact<br>Potential for Algorithmic Trading"]
    E --> H["Key Finding 3: Synthesis<br>ML + Technical Analysis"]