Volatility autocorrelation in the stock market with artificial neural networks
Predicting the trend of financial features in complex financial systems is important and challenging, one useful tool is looking at the autocorrelation function, used in technical analysis as it shows how closely related a pattern reappears in the future. In this paper, we demonstrate a way to op...
Main Author: | Tham, Zhi Rong |
---|---|
Other Authors: | Cheong Siew Ann |
Format: | Final Year Project (FYP) |
Language: | English |
Published: |
Nanyang Technological University
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/175690 |
Similar Items
-
An artificial neural network model for multi-flexoelectric actuation of plates
by: Fan, Mu, et al.
Published: (2023) -
Selecting correct methods to extract fuzzy rules from artificial neural network
by: Tan, Xiao, et al.
Published: (2021) -
Sentiment-aware volatility forecasting
by: Xing, Frank Z., et al.
Published: (2021) -
Representation learning in the artificial and biological neural networks underlying sensorimotor integration
by: Suhaimi, Ahmad, et al.
Published: (2023) -
An interpretable Neural Fuzzy Hammerstein-Wiener network for stock price prediction
by: Xie, Chen, et al.
Published: (2022)