A Financial Deep Learning Framework

Zhenjun Li, Yinping Liao, Bo Hu, Liangyu Ni, Yunting Lu

OAI: oai:igi-global.com:302640 • DOI: 10.4018/IJWSR.302640

Prediction of stock price movement is regarded as a challenging task of financial time series prediction. Due to the complexity and massive financial market data, the research of deep learning approaches for predicting the future price is very difficult. This study attempted to develop a novel framework, named 13f-LSTM, where the AutoRegressive Integrated Moving Average (ARIMA), for the first time, as one of the technical features, Fourier transforms for trend analysis and Long-Short Term Memory (LSTM), including its variants, to forecast the future’s closing prices. Thirteen historical and technical features of stock were selected as inputs of the proposed 13f-LSTM model. Three typical stock market indices in the real world and their corresponding closing prices in 30 trading days are chosen to examine the performance and predictive accuracy of it. The experimental results show that the 13f-LSTM model outperforms other proposed models in both profitability performance and predictive accuracy.

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