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- Title
High-Frequency Forecasting of Stock Volatility Based on Model Fusion and a Feature Reconstruction Neural Network.
- Authors
Shi, Zhiwei; Wu, Zhifeng; Shi, Shuaiwei; Mao, Chengzhi; Wang, Yingqiao; Zhao, Laiqi
- Abstract
Stock volatility is an important measure of financial risk. Due to the complexity and variability of financial markets, time series forecasting in the financial field is extremely challenging. This paper proposes a "model fusion learning algorithm" and a "feature reconstruction neural network" to forecast the future 10 min volatility of 112 stocks from different industries over the past three years. The results show that the model in this paper has higher fitting accuracy and generalization ability than the traditional model (CART, MLR, LightGBM, etc.). This study found that the "model fusion learning algorithm" can be well applied to financial data modeling; the "feature reconstruction neural network" can well-model data sets with fewer features.
- Subjects
MACHINE learning; BUSINESS forecasting; FINANCIAL risk; TIME series analysis; FINANCIAL markets
- Publication
Electronics (2079-9292), 2022, Vol 11, Issue 23, p4057
- ISSN
2079-9292
- Publication type
Article
- DOI
10.3390/electronics11234057