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

Autor:	Ke, Zong, Xu, Jingyu, Zhang, Zizhou, Cheng, Yu, Wu, Wenjun
Rok vydání:	2024
Předmět:	Quantitative Finance - Computational Finance Computer Science - Machine Learning Computer Science - Neural and Evolutionary Computing
Druh dokumentu:	Working Paper
Popis:	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. Comment: 6 pages, 7 figures, 1 table, The paper will be published by IEEE on conference: 2024 3rd International Conference on Image Processing, Computer Vision and Machine Learning (ICICML 2024)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.07223 Zobrazit plný text záznamu View this record from Arxiv