Ứng dụng phương pháp học máy dự báo mức độ căng thẳng tài chính của doanh nghiệp niêm yết tại Việt Nam=Predicting financial distress of Vietnamese listed companies using machine learning

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Tác giả: Thị Vân Trang Đỗ, Viết Thắng Đoàn, Thị Thu Huyền Giang, Thị Ngọc Ánh Hoàng, Đức Minh Lê, Minh Thủy Tiên Nguyễn, Thị Hồng Nguyễn, Thị Thanh Nguyễn

Ngôn ngữ: vie

Ký hiệu phân loại:

Thông tin xuất bản: Tạp chí Kinh tế - Luật và Ngân hàng (Tên cũ: Tạp chí Khoa học và Đào tạo Ngân hàng), 2024

Mô tả vật lý: tr.90-104

Bộ sưu tập: Metadata

ID: 482958

Thêm vào giỏ

Bài báo sử dụng các phương pháp học máy và phương pháp thống kê truyền thống để dự báo mức độ căng thẳng tài chính của các doanh nghiệp niêm yết tại Việt Nam. Dữ liệu sử dụng trong bài báo được thu thập từ 646 doanh nghiệp niêm yết trên hai sàn giao dịch HOSE và HNX trong giai đoạn 2012- 2022. Nghiên cứu tiến hành thử nghiệm bộ dữ liệu thu thập trên các mô hình học máy: Multiple Regression Model (MRL), LASSO, Generalized Additive Model (GAM), Random Forests (RF), Gradient Boosting Regression Trees (GBRT), và Single-layer, Fed-forward neural network (NN) và phương pháp thống kê truyền thống. Kết quả của nghiên cứu cho thấy có sự phù hợp giữa kết quả của phương pháp truyền thống và các mô hình học máy hiện đại. Trong đó, mô hình có độ chính xác cao nhất là Random Forest (RF) với tỉ lệ chính xác 98,8%. Các biến có ảnh hưởng nhiều nhất đến tình trạng căng thẳng tài chính của doanh nghiệp là TANG, ROA, LTD, NPM. Dựa trên kết quả này, bài báo đề xuất một số khuyến nghị giúp hỗ trợ ra quyết định tài chính đúng đắn và có hiệu quả cho các bên liên quan (doanh nghiệp, cơ quan quản lý, cổ đông, chủ nợ, nhà đầu tư).This study utilizes the traditional statistical method and machine learning methods to forecast the level of financial distress of listed companies in Vietnam. The data used in the study was collected from 646 listed companies on the HOSE and HNX exchanges during the period 2012-2022. The study experiments with the dataset on 6 machine learning models: Multiple Regression Model (MRL), LASSO, Generalized Additive Model (GAM), Random Forests (RF), Gradient Boosting Regression Trees (GBRT), and a single-layer, feed-forward neural network (NN), as well as the traditional statistical method. The research results demonstrate a congruence between the outcomes of the traditional method and modern machine learning models. Among these, the model with the highest accuracy is Random Forest (RF) with an accuracy rate of 98.8%. The variables that most influence the financial distress status of companies are TANG, ROA, LTD, NPM. Based on these results, our study proposes recommendations to support informed and effective financial decisions for stakeholders (companies, regulatory agencies, shareholders, creditors, investors).

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