Abstract
The VN-Index prediction problem is a part of a research project that predicts macroeconomic indicators for developing economies to detect the risk of inflation and economic crisis. In this study, given the records of VN-Index in the Vietnam Stock Exchange (VSE), we compare two different VN-Index forecast methods: the ARIMA econometric model and the Deep-learning approach with the LSTM-RNN model. Empirical results shows that: (1) the LSTM-RNN model is more accurate than the ARIMA model in the VN-Index forecast problems, (2) when applying the LSTM-RNN model, the window size would converge if it was larger than 15 days for LSTM-256 and 10 days for LSTM-512. From such results, we conclude that the LSTM-RNN model based on one-month data is optimal, which is appropriate according to the Wave theory and the psychology of the market.
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Co, N.T., Son, H.H., Hoang, N.T., Lien, T.T.P., Ngoc, T.M. (2020). Comparison Between ARIMA and LSTM-RNN for VN-Index Prediction. In: Ahram, T., Karwowski, W., Vergnano, A., Leali, F., Taiar, R. (eds) Intelligent Human Systems Integration 2020. IHSI 2020. Advances in Intelligent Systems and Computing, vol 1131. Springer, Cham. https://doi.org/10.1007/978-3-030-39512-4_168
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DOI: https://doi.org/10.1007/978-3-030-39512-4_168
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