Applying Deep Learning Models in Forecasting Real Estate Stock Prices: Empirical Evidence from the Vietnamese Stock Market
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DOI:
https://doi.org/10.63640/3030-4091/jpd.apd.153Từ khóa:
Stock price forecasting, LSTM, GRU, CNN-LSTM, Real estate stocksTóm tắt
This study compares the traditional ARIMA model with three deep learning models—LSTM, GRU, and CNN-LSTM—in forecasting the closing prices of 15 listed real estate companies in Vietnam over the period from January 2015 to August 2025. The experiments show that the GRU model achieves the lowest RMSE and MAE and higher directional accuracy (DA) than the other models. The results indicate that GRU is a suitable model for short-term forecasting in the Vietnamese market. The study also suggests that future research should integrate macroeconomic and unstructured data to improve directional forecasting performance.
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