Phân tích trắc lượng thư mục các nghiên cứu về kỹ thuật co gọn, ước lượng ma trận hiệp phương sai trong việc lựa chọn danh mục đầu tư tối ưu=Bibliometric Analysis of Studies on Shrinkage Techniques and Covariance Matrix Estimation in Optimal Portfolio Selection

0 Người đánh giá. Xếp hạng trung bình 0

Tác giả: Thị Anh Quyên Lê, Minh Nhật Nguyễn

Ngôn ngữ: vie

Ký hiệu phân loại:

Thông tin xuất bản: Tạp chí Kinh tế và Ngân hàng châu Á, 2024

Mô tả vật lý: tr.47-63

Bộ sưu tập: Metadata

ID: 483737

Thêm vào giỏ

Nghiên cứu này sử dụng phân tích trắc lượng thư mục sử dụng ước lượng ma trận hiệp phương sai (MTHPS) và các kỹ thuật co gọn trong việc tối ưu hóa danh mục đầu tư (DMĐT) nhận được nhiều sự quan tâm. Khi kích thước dữ liệu tài chính ngày càng tăng, MTHPS mẫu truyền thống trở nên không ổn định, thường không có độ chính xác cao để hỗ trợ cho việc lựa chọn và quản lý DMĐT hiệu quả. Các ước lượng co gọn cung cấp một phương án thay thế bằng cách thu nhỏ kích thước của dữ liệu giúp cải thiện độ chính xác của ước lượng MTHPS. Thông qua việc xem xét toàn diện các công bố học thuật từ cơ sở dữ liệu Web of Science (WOS), phân tích này làm nổi bật các xu hướng chính, sự phát triển theo chủ đề và các nhóm nghiên cứu cốt lõi trong lĩnh vực này. Các phát hiện của nhóm tác giả cho thấy sự tăng trưởng đáng kể trong hoạt động nghiên cứu, đặc biệt là trong các phương pháp luận tích hợp các kỹ thuật co gọn với các lý thuyết học thống kê hiện đại. Nghiên cứu không chỉ vẽ bản đồ cấu trúc của lĩnh vực này mà còn xác định các tác giả, tổ chức và quốc gia có ảnh hưởng dẫn đầu trong lĩnh vực này. Nghiên cứu trắc lượng thư mục này nhấn mạnh xu hướng nghiên cứu hiện nay về ước lượng MTHPS cũng như các kỹ thuật co gọn trong việc tối ưu hóa DMĐT.This study employs bibliometric analysis to explore heavily researched areas (covariance matrix estimation) and emerging fields (shrinkage techniques) within the domain of portfolio optimization. As the volume of financial data grows, traditional sample covariance matrix estimation becomes unstable, often lacking the high precision required to support effective portfolio selection and management. Shrinkage estimators offer an alternative by reducing data dimensionality, thereby improving the accuracy of covariance matrix estimation. Through a comprehensive review of scholarly publications across Web Of Science (WOS) database, this analysis highlights key trends, thematic developments, and core research clusters in this field. Our findings reveal substantial growth in research activity, particularly in methodologies that integrate shrinkage techniques with modern statistical learning theories. The study not only maps the field's structure but also identifies influential authors, institutions, and countries at the forefront of this research. This bibliometric analysis underscores current research trends in covariance matrix estimation and shrinkage techniques within the context of portfolio optimization.

1. Đọc trực tuyến

1. Đọc trực tuyến

Tạo bộ sưu tập với mã QR