Ấn phẩm:
Applying semi-supervised machine learning methods to predict stock prices in the Vietnam stock market
Tóm tắt
This study proposes an approach based on semi-supervised machine learning to predict stock price movement trends in the Vietnam stock market. The proposed algorithm models the relationships between stocks as an influence graph. A set of basic prediction algorithms is used to assign initial labels to a subset of nodes in the graph. The Graph Convolutional Network (GCN) algorithm is then applied to analyze this partially labeled graph to predict the final labels for all nodes. Experimental results on data of 100 stocks listed on the Ho Chi Minh City Stock Exchange (HOSE) and HNX exchange show that the proposed method achieves higher accuracy than current common methods. This is a new approach to applying advanced machine learning technology for financial forecasting in Vietnam.
Mô tả
Tác giả
Ha Van Sang PhD
Nguyen Thanh Son MSc
Phan The Khai
Nguyen Thanh Son MSc
Phan The Khai
Tác giả khác
Người hướng dẫn
item.page.field1
Nhà xuất bản
Học Viện Tài chính
Năm xuất bản
2023
ISSN tạp chí
Nhan đề tập
Từ khóa chủ đề
Semi-supervised learning, influence graph , GCN , Stock price prediction
Địa chỉ truy cập
Vui lòng sử dụng ứng dụng DRM AOF để đọc tài liệu số
