Stock Price Forecasting Based on Improved Support Vector Regression

Yuhan Fang

doi:10.1109/ICISCE50968.2020.00272

Stock Price Forecasting Based on Improved Support Vector Regression

Yuhan Fang

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

3 Citations (Scopus)

Abstract

The prediction of stock price is of great significance to social and economic development, market supervision, enterprise management and investors' investment behavior. Through obtaining and analyzing the stock historical data of many domestic and foreign companies, this paper finds that the prediction deviation of Support Vector Regression (SVR) for individual stocks is very large. Therefore, in order to solve the problem that the deviation on specific stocks is too large, an improved SVR method based on dividing data segments is proposed. The experimental results on Amazon (AMZN) stock data show that our improved algorithm outperforms the original SVR in R2 and RMSE, which proves its effectiveness.

Original language	English
Title of host publication	Proceedings - 2020 7th International Conference on Information Science and Control Engineering, ICISCE 2020
Editors	Shaozi Li, Ying Dai, Jianwei Ma, Yun Cheng
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1351-1354
Number of pages	4
ISBN (Electronic)	9781728164069
DOIs	https://doi.org/10.1109/ICISCE50968.2020.00272
Publication status	Published - Dec 2020
Externally published	Yes
Event	7th International Conference on Information Science and Control Engineering, ICISCE 2020 - Changsha, Hunan, China Duration: 18 Dec 2020 → 20 Dec 2020

Publication series

Name	Proceedings - 2020 7th International Conference on Information Science and Control Engineering, ICISCE 2020

Conference

Conference	7th International Conference on Information Science and Control Engineering, ICISCE 2020
Country/Territory	China
City	Changsha, Hunan
Period	18/12/20 → 20/12/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Amazon stock data
modeling on data segments
prediction of stock price
Support Vector Regression (SVR)

Access to Document

10.1109/ICISCE50968.2020.00272

Cite this

Fang, Y. (2020). Stock Price Forecasting Based on Improved Support Vector Regression. In S. Li, Y. Dai, J. Ma, & Y. Cheng (Eds.), Proceedings - 2020 7th International Conference on Information Science and Control Engineering, ICISCE 2020 (pp. 1351-1354). (Proceedings - 2020 7th International Conference on Information Science and Control Engineering, ICISCE 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICISCE50968.2020.00272

Fang, Yuhan. / Stock Price Forecasting Based on Improved Support Vector Regression. Proceedings - 2020 7th International Conference on Information Science and Control Engineering, ICISCE 2020. editor / Shaozi Li ; Ying Dai ; Jianwei Ma ; Yun Cheng. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 1351-1354 (Proceedings - 2020 7th International Conference on Information Science and Control Engineering, ICISCE 2020).

@inproceedings{58ebafbe598f4c20b143b7b491bad70f,

title = "Stock Price Forecasting Based on Improved Support Vector Regression",

abstract = "The prediction of stock price is of great significance to social and economic development, market supervision, enterprise management and investors' investment behavior. Through obtaining and analyzing the stock historical data of many domestic and foreign companies, this paper finds that the prediction deviation of Support Vector Regression (SVR) for individual stocks is very large. Therefore, in order to solve the problem that the deviation on specific stocks is too large, an improved SVR method based on dividing data segments is proposed. The experimental results on Amazon (AMZN) stock data show that our improved algorithm outperforms the original SVR in R2 and RMSE, which proves its effectiveness.",

keywords = "Amazon stock data, modeling on data segments, prediction of stock price, Support Vector Regression (SVR)",

author = "Yuhan Fang",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 7th International Conference on Information Science and Control Engineering, ICISCE 2020 ; Conference date: 18-12-2020 Through 20-12-2020",

year = "2020",

month = dec,

doi = "10.1109/ICISCE50968.2020.00272",

language = "English",

series = "Proceedings - 2020 7th International Conference on Information Science and Control Engineering, ICISCE 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1351--1354",

editor = "Shaozi Li and Ying Dai and Jianwei Ma and Yun Cheng",

booktitle = "Proceedings - 2020 7th International Conference on Information Science and Control Engineering, ICISCE 2020",

}

Fang, Y 2020, Stock Price Forecasting Based on Improved Support Vector Regression. in S Li, Y Dai, J Ma & Y Cheng (eds), Proceedings - 2020 7th International Conference on Information Science and Control Engineering, ICISCE 2020. Proceedings - 2020 7th International Conference on Information Science and Control Engineering, ICISCE 2020, Institute of Electrical and Electronics Engineers Inc., pp. 1351-1354, 7th International Conference on Information Science and Control Engineering, ICISCE 2020, Changsha, Hunan, China, 18/12/20. https://doi.org/10.1109/ICISCE50968.2020.00272

Stock Price Forecasting Based on Improved Support Vector Regression. / Fang, Yuhan.
Proceedings - 2020 7th International Conference on Information Science and Control Engineering, ICISCE 2020. ed. / Shaozi Li; Ying Dai; Jianwei Ma; Yun Cheng. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1351-1354 (Proceedings - 2020 7th International Conference on Information Science and Control Engineering, ICISCE 2020).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

TY - GEN

T1 - Stock Price Forecasting Based on Improved Support Vector Regression

AU - Fang, Yuhan

PY - 2020/12

Y1 - 2020/12

N2 - The prediction of stock price is of great significance to social and economic development, market supervision, enterprise management and investors' investment behavior. Through obtaining and analyzing the stock historical data of many domestic and foreign companies, this paper finds that the prediction deviation of Support Vector Regression (SVR) for individual stocks is very large. Therefore, in order to solve the problem that the deviation on specific stocks is too large, an improved SVR method based on dividing data segments is proposed. The experimental results on Amazon (AMZN) stock data show that our improved algorithm outperforms the original SVR in R2 and RMSE, which proves its effectiveness.

AB - The prediction of stock price is of great significance to social and economic development, market supervision, enterprise management and investors' investment behavior. Through obtaining and analyzing the stock historical data of many domestic and foreign companies, this paper finds that the prediction deviation of Support Vector Regression (SVR) for individual stocks is very large. Therefore, in order to solve the problem that the deviation on specific stocks is too large, an improved SVR method based on dividing data segments is proposed. The experimental results on Amazon (AMZN) stock data show that our improved algorithm outperforms the original SVR in R2 and RMSE, which proves its effectiveness.

KW - Amazon stock data

KW - modeling on data segments

KW - prediction of stock price

KW - Support Vector Regression (SVR)

UR - https://www.scopus.com/pages/publications/85116326421

U2 - 10.1109/ICISCE50968.2020.00272

DO - 10.1109/ICISCE50968.2020.00272

M3 - Conference Paper published in a book

AN - SCOPUS:85116326421

T3 - Proceedings - 2020 7th International Conference on Information Science and Control Engineering, ICISCE 2020

SP - 1351

EP - 1354

BT - Proceedings - 2020 7th International Conference on Information Science and Control Engineering, ICISCE 2020

A2 - Li, Shaozi

A2 - Dai, Ying

A2 - Ma, Jianwei

A2 - Cheng, Yun

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 7th International Conference on Information Science and Control Engineering, ICISCE 2020

Y2 - 18 December 2020 through 20 December 2020

ER -

Fang Y. Stock Price Forecasting Based on Improved Support Vector Regression. In Li S, Dai Y, Ma J, Cheng Y, editors, Proceedings - 2020 7th International Conference on Information Science and Control Engineering, ICISCE 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1351-1354. (Proceedings - 2020 7th International Conference on Information Science and Control Engineering, ICISCE 2020). doi: 10.1109/ICISCE50968.2020.00272

Stock Price Forecasting Based on Improved Support Vector Regression

Abstract

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Bibliographical note

Keywords

Access to Document

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