Deep Learning Stock Price Prediction System Based on Feedforward Neural Network

Cheng Cheng; Liying Zhu; Hongsheng Cheng

doi:10.1117/12.2646822

Deep Learning Stock Price Prediction System Based on Feedforward Neural Network

Cheng Cheng, Liying Zhu, Hongsheng Cheng^*

^*Corresponding author for this work

Department of Mathematics

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

Stock price prediction has sparked the interest of financial investors and scholars, and it is also a research topic for academics. Because of the non-linearity and fluctuation of stock prices, classic statistical methods for stock price prediction are less than ideal. When it comes to analyzing time series data, the deep learning model provides a lot of advantages. In this paper, we crawl the historical stock price data of GOOGLE and provide a stock prediction system based on a feedforward neural network to further optimize the prediction model. We use RMSE, MAE and MAPE to verify the model’s prediction accuracy. The empirical results indicate that the feedforward neural network provides good effectiveness and feasibility.

Original language	English
Title of host publication	Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022)
Editors	Shuangming Yang
Publisher	SPIE
ISBN (Electronic)	9781510657298
ISBN (Print)	9781510657281
DOIs	https://doi.org/10.1117/12.2646822
Publication status	Published - 2022
Event	3rd International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022) - Online, Changsha, China Duration: 8 Apr 2022 → 10 Apr 2022 http://www.allconfs.org/meeting/index_en.asp?id=11768

Publication series

Name	Proceedings of SPIE
Volume	12329
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	3rd International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022)
Place	China
City	Changsha
Period	8/04/22 → 10/04/22
Internet address	http://www.allconfs.org/meeting/index_en.asp?id=11768

Research Keywords

feedforward neural network
prediction of stock price
time series data

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10.1117/12.2646822

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@inproceedings{1776f75c2a2947e7b71f1d86fb037da8,

title = "Deep Learning Stock Price Prediction System Based on Feedforward Neural Network",

abstract = "Stock price prediction has sparked the interest of financial investors and scholars, and it is also a research topic for academics. Because of the non-linearity and fluctuation of stock prices, classic statistical methods for stock price prediction are less than ideal. When it comes to analyzing time series data, the deep learning model provides a lot of advantages. In this paper, we crawl the historical stock price data of GOOGLE and provide a stock prediction system based on a feedforward neural network to further optimize the prediction model. We use RMSE, MAE and MAPE to verify the model{\textquoteright}s prediction accuracy. The empirical results indicate that the feedforward neural network provides good effectiveness and feasibility.",

keywords = "feedforward neural network, prediction of stock price, time series data",

author = "Cheng Cheng and Liying Zhu and Hongsheng Cheng",

year = "2022",

doi = "10.1117/12.2646822",

language = "English",

isbn = "9781510657281",

series = "Proceedings of SPIE",

publisher = "SPIE",

editor = "Shuangming Yang",

booktitle = "Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022)",

address = "United States",

note = "3rd International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022) ; Conference date: 08-04-2022 Through 10-04-2022",

url = "http://www.allconfs.org/meeting/index_en.asp?id=11768",

}

Cheng, C, Zhu, L & Cheng, H 2022, Deep Learning Stock Price Prediction System Based on Feedforward Neural Network. in S Yang (ed.), Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022) ., 123290H, Proceedings of SPIE, vol. 12329, SPIE, 3rd International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), Changsha, China, 8/04/22. https://doi.org/10.1117/12.2646822

Deep Learning Stock Price Prediction System Based on Feedforward Neural Network. / Cheng, Cheng; Zhu, Liying; Cheng, Hongsheng.
Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022) . ed. / Shuangming Yang. SPIE, 2022. 123290H (Proceedings of SPIE; Vol. 12329).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Deep Learning Stock Price Prediction System Based on Feedforward Neural Network

AU - Cheng, Cheng

AU - Zhu, Liying

AU - Cheng, Hongsheng

PY - 2022

Y1 - 2022

N2 - Stock price prediction has sparked the interest of financial investors and scholars, and it is also a research topic for academics. Because of the non-linearity and fluctuation of stock prices, classic statistical methods for stock price prediction are less than ideal. When it comes to analyzing time series data, the deep learning model provides a lot of advantages. In this paper, we crawl the historical stock price data of GOOGLE and provide a stock prediction system based on a feedforward neural network to further optimize the prediction model. We use RMSE, MAE and MAPE to verify the model’s prediction accuracy. The empirical results indicate that the feedforward neural network provides good effectiveness and feasibility.

AB - Stock price prediction has sparked the interest of financial investors and scholars, and it is also a research topic for academics. Because of the non-linearity and fluctuation of stock prices, classic statistical methods for stock price prediction are less than ideal. When it comes to analyzing time series data, the deep learning model provides a lot of advantages. In this paper, we crawl the historical stock price data of GOOGLE and provide a stock prediction system based on a feedforward neural network to further optimize the prediction model. We use RMSE, MAE and MAPE to verify the model’s prediction accuracy. The empirical results indicate that the feedforward neural network provides good effectiveness and feasibility.

KW - feedforward neural network

KW - prediction of stock price

KW - time series data

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U2 - 10.1117/12.2646822

DO - 10.1117/12.2646822

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781510657281

T3 - Proceedings of SPIE

BT - Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022)

A2 - Yang, Shuangming

PB - SPIE

T2 - 3rd International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022)

Y2 - 8 April 2022 through 10 April 2022

ER -

Deep Learning Stock Price Prediction System Based on Feedforward Neural Network

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