Dr. Wenlong Ni Profile

Dr. Wenlong Ni

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 23 December 2022 Paper

Stock price prediction in Chinese stock markets based on CNN-GRU-attention model

JinChao Qi, Shiting Huang, Jiayu Hu, Wenlong Ni, Hua Chen

Proceedings Volume 12508, 125080L (2022) https://doi.org/10.1117/12.2663261

KEYWORDS: Data modeling, Performance modeling, Statistical modeling, Feature extraction, Data processing, Convolution

Read Abstract +

Stock price prediction is a hot topic and has attracted the sufficient attention of both regulatory authorities and financial institutions. Because the fluctuation of stock prices is the result of many different factors, it is not easy to make stock price prediction. Traditional prediction solutions are mainly using simple linear models based on statistical and econometric models, these solutions are difficult to support nonstationary time series data. With the development of deep learning, some newly models can not only support non-linear data, but also retain useful information for better forecasting the stock prices. This paper aims to construct a CNN-GRU-Attention based model for price prediction in Chinese stock markets. First, the convolutional and pooling layers of CNN are used to extract features of factor correlation information from the input data; then, the output of feature matrix is used as input for the GRU model to forecast correlation; finally, the Attention mechanism is used to focus on the important characteristics of stock prices and optimize model structure. We collect multi-dimensional stock data of the China SSE 50 index from 2011 to 2021 as our dataset and conduct a set of experiments to compare the performance, which measured in terms of their Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and R squared (R²) score. The proposed model is superior to other models: MAPE decreased by 11.23%, RMSE decreased by 5.71% and R²score improved by 0.41%, which shows that the CNN-GRU-Attention model outperforms state-of-the-art approaches in forecasting stock price.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years