Unsupervised feature extraction with convolutional autoencoder with application to daily stock market prediction

Due to the volatility and noise of the stock market, accurately obtaining the trend of the stock market is a challenging problem, and gets the attention of many researchers and speculators. Recently, convolutional neural network (CNN) has been used to automatically learn effective features and predi...

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Vydané v:	Concurrency and computation Ročník 33; číslo 16
Hlavní autori:	Xie, Li, Yu, Sheng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Hoboken Wiley Subscription Services, Inc 25.08.2021
Predmet:	Artificial neural networks convolutional autoencoder network convolutional neural network Data processing Economic models Feature extraction Machine learning Securities markets stock market prediction support vector machine Support vector machines Time series
ISSN:	1532-0626, 1532-0634
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Popis
Shrnutí:	Due to the volatility and noise of the stock market, accurately obtaining the trend of the stock market is a challenging problem, and gets the attention of many researchers and speculators. Recently, convolutional neural network (CNN) has been used to automatically learn effective features and predict stock market trends. In CNN‐based methods reported so far, less focus has been paid to time series information of the stock, but is very crucial for stock forecasting. In this study, an unsupervised feature extraction method with convolutional autoencoder (CAE) with application to daily stock market prediction is proposed, which has a higher prediction than traditional models. The proposed method mainly consists of the data processing part, unsupervised feature learning part, and the support vector machine model part. Data processing part includes time series data transform into two‐dimensional data and data normalization. CAE network‐based unsupervised feature learning is designed by fusing convolution and autoencoder. In order to verify the performance of the model, various initial financial and economic variables of stock indices are chosen for prediction experiments. The experimental results on different stock indices demonstrate a significant improvement in prediction's performance compared with the baseline methods.
Bibliografia:	Funding information A preliminary version of this article has been published by the seventh International Conference on Information Science and Control Engineering. Shaoguan science and technology plan project, 2019sn064; Shaoguan University research project, SY2018KJ03, SY2019ZK09; Shaoguan University talent introduction research project ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	1532-0626 1532-0634
DOI:	10.1002/cpe.6282