关键词: 长短期记忆（LSTM）神经网络, 深度学习, 股票指数预测

Abstract: The Long-short Term Memory (LSTM) neural network, as one of the classic models in deep learning technology, is advantageous in mining long-term dependency of sequential data. Based on optimized technology of deep neural network, this paper constructs a deep LSTM neural network to forecast 30 stock indices in three scenarios with different horizons. The results show that i) the LSTM neural network is highly capable of generalization in financial forecast and can generate stable forecasts for 30 stock indices; ii) the LSTM neural network offers high accuracy in long and short-term forecasts in comparison with other three models (SVR, MLP and ARIMA), escalating the average accuracy of all indices for different scenarios; iii) the LSTM neural network can effectively control the error fluctuations and enhances the average stability of forecasts of all indices in the three scenarios compared to the other three models. In view of the advantages in terms of the forecast accuracy and stability, the LSTM neural network for sure will be widely used in the forecast for financial market in the coming days.

Key words: Long-short Term Memory (LSTM) Neural Network, Deep Learning, Forecast of Stock Indices