Dự báo kiệt quệ tài chính của các doanh nghiệp Việt Nam: Ứng dụng Machine Learning

Ngọc Lê Hồng; Long Nguyễn Thế; Lam Hồ Thị; Hoài Hồ Thu

doi:10.24311/jabes/2023.34.12.6

Các tác giả

Lê Hồng Ngọc Trường Đại học Tài chính - Marketing Tác giả
Nguyễn Thế Long Ngân hàng TMCP Xuất nhập khẩu Việt Nam Tác giả
Hồ Thị Lam Trường Đại học Tài chính - Marketing Tác giả
Hồ Thu Hoài Đại học Kinh tế Thành phố Hồ Chí Minh Tác giả

DOI:

https://doi.org/10.24311/jabes/2023.34.12.6

Từ khóa:

Kiệt quệ tài chính, Dự báo kiệt quệ tài chính, XGBoost, Machine Learning

Tóm tắt

Dự báo kiệt quệ tài chính (KQTC) là một trong những nhiệm vụ quan trọng trong đánh giá rủi ro doanh nghiệp. Trong nghiên cứu này, nhóm tác giả ứng dụng các thuật toán Machine Learning nhằm dự báo KQTC, đồng thời xem xét các yếu tố ảnh hưởng đến nguy cơ KQTC của các doanh nghiệp tại Việt Nam. Nghiên cứu này sử dụng dữ liệu của 657 doanh nghiệp niêm yết trên hai sàn, gồm: (1) Sở Giao dịch Chứng khoán TP. Hồ Chí Minh (HOSE), và (2) Sở Giao dịch Chứng khoán Hà Nội (HNX), giai đoạn 2009–2022 với sáu thuật toán gồm: Logistic Regression, KNN, Decision Trees, Random Forests, AdaBoost, và XGBoost. Nghiên cứu đã chỉ ra rằng mô hình XGBoost là phù hợp nhất cho dự báo KQTC tại Việt Nam. Từ kết quả nghiên cứu, nhóm tác giả cũng đã đề xuất một số hàm ý quản trị và hàm ý chính sách trong việc lựa chọn mô hình dự báo KQTC và theo dõi các yếu tố tác động đến KQTC để phát triển bền vững doanh nghiệp.

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