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Predicting Stock Prices Using Dynamic LSTM Models

  • Duc Huu Dat NguyenEmail author
  • Loc Phuoc Tran
  • Vu Nguyen
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1051)

Abstract

Predicting stock prices accurately is a key goal of investors in the stock market. Unfortunately, stock prices are constantly changing and affected by many factors, making the process of predicting them a challenging task. This paper describes a method to build models for predicting stock prices using long short-term memory network (LSTM). The LSTM-based model, which we call dynamic LSTM, is initially built and continuously retrained using newly augmented data to predict future stock prices. We evaluate the proposed method using data sets of four stocks. The results show that the proposed method outperforms others in predicting stock prices based on different performance metrics.

Keywords

LSTM Stock price prediction Dynamic models 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Duc Huu Dat Nguyen
    • 1
    Email author
  • Loc Phuoc Tran
    • 1
  • Vu Nguyen
    • 1
  1. 1.Faculty of Information Technology, University of ScienceVietnam National UniversityHo Chi MinhVietnam

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