深度学习
人工智能
计算机科学
期货合约
机器学习
盈利能力指数
时间序列
小波
短时记忆
股票市场
库存(枪支)
股市预测
计量经济学
人工神经网络
财务
循环神经网络
经济
工程类
生物
机械工程
古生物学
马
作者
Bajin Wei,Jun Yue,Yulei Rao
出处
期刊:PLOS ONE
[Public Library of Science]
日期:2017-07-14
卷期号:12 (7): e0180944-e0180944
被引量:636
标识
DOI:10.1371/journal.pone.0180944
摘要
The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day’s closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance.
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