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Applying machine learning algorithms to predict the stock price trend in the stock market - The case of Vietnam

Business

Applying machine learning algorithms to predict the stock price trend in the stock market - The case of Vietnam

T. Phuoc, P. T. K. Anh, et al.

This study reveals a groundbreaking approach to predicting stock price trends in Vietnam's emerging market using the Long Short-Term Memory (LSTM) algorithm. With an impressive accuracy rate of 93%, the research conducted by Tran Phuoc, Pham Thi Kim Anh, Phan Huy Tam, and Chien V. Nguyen showcases the model's effectiveness in analyzing stock price movements based on technical indicators and key stock data.

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Playback language: English
Abstract
This study aims to predict stock price trends in Vietnam's emerging market using the Long Short-Term Memory (LSTM) algorithm. The model incorporates technical indicators (SMA, MACD, RSI) and data from VN-Index and VN-30 stocks. Results show a 93% accuracy rate for most stocks, demonstrating the LSTM model's effectiveness in analyzing and forecasting stock price movements.
Publisher
Humanities & Social Sciences Communications
Published On
Mar 12, 2024
Authors
Tran Phuoc, Pham Thi Kim Anh, Phan Huy Tam, Chien V. Nguyen
Tags
stock price prediction
LSTM algorithm
Vietnam
technical indicators
accuracy

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