R.I.P.
π»
Ghosted
A Consolidated Volatility Prediction with Back Propagation Neural Network and Genetic Algorithm
December 10, 2024 Β· Declared Dead Β· π 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML)
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Zong Ke, Jingyu Xu, Zizhou Zhang, Yu Cheng, Wenjun Wu
arXiv ID
2412.07223
Category
q-fin.CP
Cross-listed
cs.LG,
cs.NE
Citations
25
Venue
2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML)
Last Checked
3 months ago
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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β q-fin.CP
R.I.P.
π»
Ghosted
Deep Reinforcement Learning for Trading
R.I.P.
π»
Ghosted
Solving the Optimal Trading Trajectory Problem Using a Quantum Annealer
R.I.P.
π»
Ghosted
Neural networks for option pricing and hedging: a literature review
R.I.P.
π»
Ghosted
Lagged correlation-based deep learning for directional trend change prediction in financial time series
R.I.P.
π»
Ghosted
QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted