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Two-Dimensional Attention-Based LSTM Model for Stock Index Prediction

  • Yu, Yeonguk (Dept. of Computer Engineering, Hanbat National University) ;
  • Kim, Yoon-Joong (Dept. of Computer Engineering, Hanbat National University)
  • Received : 2019.07.01
  • Accepted : 2019.09.04
  • Published : 2019.10.31

Abstract

This paper presents a two-dimensional attention-based long short-memory (2D-ALSTM) model for stock index prediction, incorporating input attention and temporal attention mechanisms for weighting of important stocks and important time steps, respectively. The proposed model is designed to overcome the long-term dependency, stock selection, and stock volatility delay problems that negatively affect existing models. The 2D-ALSTM model is validated in a comparative experiment involving the two attention-based models multi-input LSTM (MI-LSTM) and dual-stage attention-based recurrent neural network (DARNN), with real stock data being used for training and evaluation. The model achieves superior performance compared to MI-LSTM and DARNN for stock index prediction on a KOSPI100 dataset.

Keywords

Attention Mechanism;LSTM;Stock Index Prediction;Two-Dimensional Attention

Acknowledgement

Supported by : Hanbat National University

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