Improving the Machine Learning Stock Trading System: An N-Period Volatility Labeling and Instance Selection Technique
Financial technology is crucial for the sustainable development of financial systems. Algorithmic trading, a key area in financial technology, involves automated trading based on predefined rules. However, investors cannot manually analyze all market patterns and establish rules, necessitating the d...
Saved in:
Main Authors: | Young Hun Song, Myeongseok Park, Jaeyun Kim |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2024-01-01
|
Series: | Complexity |
Online Access: | http://dx.doi.org/10.1155/2024/5036389 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Advantages of Combining Factorization Machine with Elman Neural Network for Volatility Forecasting of Stock Market
by: Fang Wang, et al.
Published: (2021-01-01) -
Hybrid Model for Stock Market Volatility
by: Kofi Agyarko, et al.
Published: (2023-01-01) -
Stock volatility as an anomalous diffusion process
by: Rubén V. Arévalo, et al.
Published: (2024-12-01) -
Revolutionizing agricultural stock volatility forecasting: a comparative study of machine learning and HAR-RV models
by: Houjian Li, et al.
Published: (2025-12-01) -
Fundamentalist Signals in Volatility Scenarios: Evidence in the Brazilian Stock Market
by: Edson Bastos, et al.
Published: (2020-01-01)