Đánh giá hiệu suất mô hình phức hợp LSTM-GRU: nghiên cứu điển hình về dự báo chỉ số đo lường xu hướng biến động giá cổ phiếu trên sàn giao dịch chứng khoán Hồ Chí Minh
Abstract
The stock market is a highly complex non-linear system, and its volatility is influenced by numerous factors, making stock price prediction a challenging task. Long Short Term Memory networks (LSTM) model, Gated Recurrent Unit (GRU) model, and their hybrids are designed using the Python programming language with available supporting packages, and they demonstrate high accuracy in forecasting. The LSTM-GRU Hybrid model performs the best among the complex models. Through the LSTM-GRU Hybrid model, the study predicts the trend of the VNIndex for the next 100 days, indicating a rising trend. This indirectly suggests a potential resurgence in the Vietnamese stock market, driven by new government policies.
Tóm tắt
Thị trường chứng khoán là một hệ thống chuyển động phi tuyến rất phức tạp và quy luật biến động của nó bị ảnh hưởng bởi rất nhiều yếu tố, vì vậy việc dự đoán chỉ số giá cổ phiếu là một nhiệm vụ rất khó khăn. Mô hình mạng nơ-ron với bộ nhớ ngắn hạn định hướng dài hạn (LSTM), mạng nơ-ron hồi tiếp với nút cổng (GRU) và các phức hợp được thiết kế bằng ngôn ngữ lập trình Python với các gói phụ trợ có sẵn, cho thấy kết quả dự báo với độ chính xác cao, hiệu suất của mô hình LSTM-GRU Hybrid cho kết quả tốt nhất. Thông qua mô hình LSTM-GRU Hybrid, nghiên cứu dự báo xu hướng biến động chỉ số VNIndex 100 ngày tiếp theo cho kết quả chỉ số VNIndex có xu hướng tăng. Điều đó gián tiếp chỉ ra rằng thị trường chứng khoán Việt Nam có dấu hiệu khởi sắc trở lại cùng với các chính sách mới của Chính phủ.
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