A Consolidated Volatility Prediction with Back Propagation Neural Network and Genetic Algorithm

ArXiv ID: 2412.07223 “View on arXiv”

Authors: Unknown

Abstract

This paper provides a unique approach with AI algorithms to predict emerging stock markets volatility. Traditionally, stock volatility is derived from historical volatility,Monte Carlo simulation and implied volatility as well. In this paper, the writer designs a consolidated model with back-propagation neural network and genetic algorithm to predict future volatility of emerging stock markets and found that the results are quite accurate with low errors.

Keywords: Back-Propagation Neural Network, Genetic Algorithm, Volatility Prediction, Emerging Markets, Machine Learning, Equities

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 6.5/10
• Quadrant: Street Traders
• Why: The paper applies a standard GA-BP neural network architecture without deep mathematical derivations or novel theory, but it includes detailed empirical implementation using a specific 10-year dataset, data preprocessing steps, and references to real financial databases like Wind.

  flowchart TD
    A["Research Goal: Predict Volatility<br>in Emerging Markets"] --> B["Data Inputs:<br>Historical Market Data"]
    B --> C["Key Methodology:<br>Back-Propagation Neural Network<br>and Genetic Algorithm"]
    C --> D{"Computational Process:<br>Model Training & Optimization"}
    D --> E["Key Finding:<br>Accurate Volatility Prediction<br>with Low Error Rates"]