An Empirical Research on the Effectiveness of Different LSTM Architectures on Vietnamese Stock Market

Pham Ngoc Hai, Nguyen Tien Manh, Hoang Trung Hieu, Pham Quoc Chung, N. T. Son, P. Ha, Ngo Tung Son
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引用次数: 4

Abstract

Stock price prediction is a challenging financial time-series forecasting problem. In recent years, on account of the rapid progression of deep learning, researchers have developed highly accurate, state-of-the-art time-series models. Long short-term memory (LSTM) stands out as one of the most reliable architecture at capturing long-time temporal dependences. In Vietnam, there is a lack of research papers that solely focused on the effectiveness of deep-learning in stock price prediction. This paper surveys three different variations of LSTM (Vanilla, Stacked, Bidirectional) when applied to 20 companies’ stock prices over a period of 5 years from 2015 to 2020 in the VN-index stock exchange. The results show that Bidirectional LSTM is the most accurate model.
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An Empirical Research on the Effectiveness of Different LSTM Architectures on Vietnamese Stock Market
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