Stock Price Prediction using Dynamic Neural Networks

ArXiv ID: 2306.12969 “View on arXiv”

Authors: Unknown

Abstract

This paper will analyze and implement a time series dynamic neural network to predict daily closing stock prices. Neural networks possess unsurpassed abilities in identifying underlying patterns in chaotic, non-linear, and seemingly random data, thus providing a mechanism to predict stock price movements much more precisely than many current techniques. Contemporary methods for stock analysis, including fundamental, technical, and regression techniques, are conversed and paralleled with the performance of neural networks. Also, the Efficient Market Hypothesis (EMH) is presented and contrasted with Chaos theory using neural networks. This paper will refute the EMH and support Chaos theory. Finally, recommendations for using neural networks in stock price prediction will be presented.

Keywords: Time Series Dynamic Neural Network, Chaos Theory, Efficient Market Hypothesis (EMH), Stock Price Prediction, Non-Linear Patterns, Equity (Stocks)

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 5.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical derivations for the Levenberg-Marquardt algorithm and NARX models, placing it in the high math category. It also outlines a specific implementation plan with data acquisition, training parameters, and performance metrics, giving it moderate empirical rigor.

  flowchart TD
    A["Research Goal<br>Refute EMH & Support Chaos Theory"] --> B{"Data & Inputs"}
    B --> B1["Historical Stock Data<br>Time Series"]
    B --> B2["Contemporary Methods<br>Fundamental/Technical/Regression"]
    
    B1 & B2 --> C["Methodology<br>Dynamic Neural Network DNN"]
    
    C --> D["Computational Process<br>Pattern Recognition in Non-Linear Data"]
    D --> D1["Training DNN on Time Series"]
    D --> D2["Comparing DNN vs Contemporary Methods"]
    
    D1 & D2 --> E{"Key Findings & Outcomes"}
    E --> E1["DNN Outperforms Current Techniques"]
    E --> E2["Refutation of Efficient Market Hypothesis EMH"]
    E --> E3["Validation of Chaos Theory"]
    E --> E4["Recommendations for DNN Use in Prediction"]