Work

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

Year: 2024

Type: conference-paper

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.

Source: International Conference on Image Processing, Computer Vision and Machine Learning (ICICML)

DOI: 10.1109/icicml63543.2024.10958142

Authors: Zong Ke, Jingyu Xu, Zizhou Zhang, Yu Cheng, Wenjun Wu

Institutions: National University of Singapore, Northern Arizona University, University of Illinois Urbana-Champaign, Columbia University

Language: English

Topic: Stock Market Forecasting Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W4409427031

FWCI: 11.1

Cites: 26

Cited by: 16

Related to: 10