Stacked Long Short-Term Memory for Vietnamese Stock Market Returns Prediction
Keywords:
LSTM, Stock Market, Stock ReturnsAbstract
Investment wealth management and macroeconomic research both use historical indices that describe the overall movements of the entire market over a period to make objective judgments and insightful decisions. Since their inception, exchange stock indices have provided a broad picture of the national economy, reflected various stages of economic development, and forecasted potential risks that could harm investments or lead to a large-scale crisis. Many articles have studied the volatility of stock prices and stock indexes in the world, but the separate research on the profitability of the whole stock market is relatively scarce. This paper focuses on studying the profitability of the Vietnamese stock market from 2010 to 2021 through the returns of the VN-Index. The aim of this paper is to propose a Stacked Long Short-Term Memory (LSTM) neural network to predict the volatility of VN-Index returns, in comparison with the autoregressive methods (ARIMA, SARIMA) often used in econometrics. Taking Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) as the objective cost functions, the research results show that the LSTM neural network model gives superior performance compared to the econometric methods and better predictive meaning in practice thanks to its flexible learning and memory capabilities.
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