Integration of GRU and LSTM With Fundamental and Technical Analysis for Stock Price Prediction

Adrian Chieng Hong Jie; Hakim Abdulrab; Hussein Shutari; Talal Abdullah; Raheel Zafar; Adel Althahebi

doi:10.1007/978-3-031-99025-0_13

Integration of GRU and LSTM With Fundamental and Technical Analysis for Stock Price Prediction

Adrian Chieng Hong Jie
, Hakim Abdulrab^*
, Hussein Shutari
, Talal Abdullah
, Raheel Zafar
, Adel Althahebi

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Accurate stock price prediction remains a challenging task due to the inherent complexities and high volatility of financial markets. Traditional models like ARIMA often fail to capture non-linear dynamics, while standalone deep learning models such as GRU and LSTM, though effective, may not fully leverage the benefits of combining technical and fundamental analysis. This paper proposes an integrated GRU-LSTM model that incorporates both technical indicators (e.g., MACD, RSI) and fundamental data (e.g., P/E ratio, profitability) to enhance predictive accuracy. The model is evaluated on two Chinese stocks, 600,719.SS and 000679.SZ, using historical data from Yahoo Finance. Experimental results demonstrate that the integrated GRU-LSTM model outperforms traditional ARIMA and standalone GRU/LSTM models, achieving RMSE values of 0.0141 and 0.0360 for the respective stocks. The study also explores the impact of varying GRU and LSTM layer configurations, finding that balanced architectures yield the best performance. This research highlights the potential of combining technical and fundamental analysis within a deep learning framework for more accurate stock price prediction.

Original language	English
Title of host publication	Projects, Processes, Systems and Networks in the Digital Age - Proceedings of the 4th International Conference on the Leadership and Management of Projects in the Digital Age - ICLAMP 2025
Editors	Hatem Masri, Nabil Elkadhi, Saeed Aldulaimi, Kouzou Abdellah
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	165-176
Number of pages	12
ISBN (Print)	9783031990243
DOIs	https://doi.org/10.1007/978-3-031-99025-0_13
State	Published - 2025
Externally published	Yes
Event	4th International Conference on the Leadership and Management of Projects in the Digital Age, ICLAMP 2025 - Al Eker, Bahrain Duration: 13 Apr 2025 → 14 Apr 2025

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1548 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	4th International Conference on the Leadership and Management of Projects in the Digital Age, ICLAMP 2025
Country/Territory	Bahrain
City	Al Eker
Period	13/04/25 → 14/04/25

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Keywords

Fundamental Analysis
Gated Recurrent Unit (GRU)
Long Short-Term Memory (LSTM)
Stock Price Prediction
Technical Analysis

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-3-031-99025-0_13

Cite this

Jie, A. C. H., Abdulrab, H., Shutari, H., Abdullah, T., Zafar, R., & Althahebi, A. (2025). Integration of GRU and LSTM With Fundamental and Technical Analysis for Stock Price Prediction. In H. Masri, N. Elkadhi, S. Aldulaimi, & K. Abdellah (Eds.), Projects, Processes, Systems and Networks in the Digital Age - Proceedings of the 4th International Conference on the Leadership and Management of Projects in the Digital Age - ICLAMP 2025 (pp. 165-176). (Lecture Notes in Networks and Systems; Vol. 1548 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-99025-0_13

Jie, Adrian Chieng Hong ; Abdulrab, Hakim ; Shutari, Hussein et al. / Integration of GRU and LSTM With Fundamental and Technical Analysis for Stock Price Prediction. Projects, Processes, Systems and Networks in the Digital Age - Proceedings of the 4th International Conference on the Leadership and Management of Projects in the Digital Age - ICLAMP 2025. editor / Hatem Masri ; Nabil Elkadhi ; Saeed Aldulaimi ; Kouzou Abdellah. Springer Science and Business Media Deutschland GmbH, 2025. pp. 165-176 (Lecture Notes in Networks and Systems).

@inproceedings{07936b943c884300ac8c7796d2d5f520,

title = "Integration of GRU and LSTM With Fundamental and Technical Analysis for Stock Price Prediction",

abstract = "Accurate stock price prediction remains a challenging task due to the inherent complexities and high volatility of financial markets. Traditional models like ARIMA often fail to capture non-linear dynamics, while standalone deep learning models such as GRU and LSTM, though effective, may not fully leverage the benefits of combining technical and fundamental analysis. This paper proposes an integrated GRU-LSTM model that incorporates both technical indicators (e.g., MACD, RSI) and fundamental data (e.g., P/E ratio, profitability) to enhance predictive accuracy. The model is evaluated on two Chinese stocks, 600,719.SS and 000679.SZ, using historical data from Yahoo Finance. Experimental results demonstrate that the integrated GRU-LSTM model outperforms traditional ARIMA and standalone GRU/LSTM models, achieving RMSE values of 0.0141 and 0.0360 for the respective stocks. The study also explores the impact of varying GRU and LSTM layer configurations, finding that balanced architectures yield the best performance. This research highlights the potential of combining technical and fundamental analysis within a deep learning framework for more accurate stock price prediction.",

keywords = "Fundamental Analysis, Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Stock Price Prediction, Technical Analysis",

author = "Jie, \{Adrian Chieng Hong\} and Hakim Abdulrab and Hussein Shutari and Talal Abdullah and Raheel Zafar and Adel Althahebi",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 4th International Conference on the Leadership and Management of Projects in the Digital Age, ICLAMP 2025 ; Conference date: 13-04-2025 Through 14-04-2025",

year = "2025",

doi = "10.1007/978-3-031-99025-0\_13",

language = "English",

isbn = "9783031990243",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "165--176",

editor = "Hatem Masri and Nabil Elkadhi and Saeed Aldulaimi and Kouzou Abdellah",

booktitle = "Projects, Processes, Systems and Networks in the Digital Age - Proceedings of the 4th International Conference on the Leadership and Management of Projects in the Digital Age - ICLAMP 2025",

address = "Germany",

}

Jie, ACH, Abdulrab, H, Shutari, H, Abdullah, T, Zafar, R & Althahebi, A 2025, Integration of GRU and LSTM With Fundamental and Technical Analysis for Stock Price Prediction. in H Masri, N Elkadhi, S Aldulaimi & K Abdellah (eds), Projects, Processes, Systems and Networks in the Digital Age - Proceedings of the 4th International Conference on the Leadership and Management of Projects in the Digital Age - ICLAMP 2025. Lecture Notes in Networks and Systems, vol. 1548 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 165-176, 4th International Conference on the Leadership and Management of Projects in the Digital Age, ICLAMP 2025, Al Eker, Bahrain, 13/04/25. https://doi.org/10.1007/978-3-031-99025-0_13

Integration of GRU and LSTM With Fundamental and Technical Analysis for Stock Price Prediction. / Jie, Adrian Chieng Hong; Abdulrab, Hakim; Shutari, Hussein et al.
Projects, Processes, Systems and Networks in the Digital Age - Proceedings of the 4th International Conference on the Leadership and Management of Projects in the Digital Age - ICLAMP 2025. ed. / Hatem Masri; Nabil Elkadhi; Saeed Aldulaimi; Kouzou Abdellah. Springer Science and Business Media Deutschland GmbH, 2025. p. 165-176 (Lecture Notes in Networks and Systems; Vol. 1548 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Integration of GRU and LSTM With Fundamental and Technical Analysis for Stock Price Prediction

AU - Jie, Adrian Chieng Hong

AU - Abdulrab, Hakim

AU - Shutari, Hussein

AU - Abdullah, Talal

AU - Zafar, Raheel

AU - Althahebi, Adel

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

PY - 2025

Y1 - 2025

N2 - Accurate stock price prediction remains a challenging task due to the inherent complexities and high volatility of financial markets. Traditional models like ARIMA often fail to capture non-linear dynamics, while standalone deep learning models such as GRU and LSTM, though effective, may not fully leverage the benefits of combining technical and fundamental analysis. This paper proposes an integrated GRU-LSTM model that incorporates both technical indicators (e.g., MACD, RSI) and fundamental data (e.g., P/E ratio, profitability) to enhance predictive accuracy. The model is evaluated on two Chinese stocks, 600,719.SS and 000679.SZ, using historical data from Yahoo Finance. Experimental results demonstrate that the integrated GRU-LSTM model outperforms traditional ARIMA and standalone GRU/LSTM models, achieving RMSE values of 0.0141 and 0.0360 for the respective stocks. The study also explores the impact of varying GRU and LSTM layer configurations, finding that balanced architectures yield the best performance. This research highlights the potential of combining technical and fundamental analysis within a deep learning framework for more accurate stock price prediction.

AB - Accurate stock price prediction remains a challenging task due to the inherent complexities and high volatility of financial markets. Traditional models like ARIMA often fail to capture non-linear dynamics, while standalone deep learning models such as GRU and LSTM, though effective, may not fully leverage the benefits of combining technical and fundamental analysis. This paper proposes an integrated GRU-LSTM model that incorporates both technical indicators (e.g., MACD, RSI) and fundamental data (e.g., P/E ratio, profitability) to enhance predictive accuracy. The model is evaluated on two Chinese stocks, 600,719.SS and 000679.SZ, using historical data from Yahoo Finance. Experimental results demonstrate that the integrated GRU-LSTM model outperforms traditional ARIMA and standalone GRU/LSTM models, achieving RMSE values of 0.0141 and 0.0360 for the respective stocks. The study also explores the impact of varying GRU and LSTM layer configurations, finding that balanced architectures yield the best performance. This research highlights the potential of combining technical and fundamental analysis within a deep learning framework for more accurate stock price prediction.

KW - Fundamental Analysis

KW - Gated Recurrent Unit (GRU)

KW - Long Short-Term Memory (LSTM)

KW - Stock Price Prediction

KW - Technical Analysis

UR - https://www.scopus.com/pages/publications/105019293161

U2 - 10.1007/978-3-031-99025-0_13

DO - 10.1007/978-3-031-99025-0_13

M3 - Conference contribution

AN - SCOPUS:105019293161

SN - 9783031990243

T3 - Lecture Notes in Networks and Systems

SP - 165

EP - 176

BT - Projects, Processes, Systems and Networks in the Digital Age - Proceedings of the 4th International Conference on the Leadership and Management of Projects in the Digital Age - ICLAMP 2025

A2 - Masri, Hatem

A2 - Elkadhi, Nabil

A2 - Aldulaimi, Saeed

A2 - Abdellah, Kouzou

PB - Springer Science and Business Media Deutschland GmbH

T2 - 4th International Conference on the Leadership and Management of Projects in the Digital Age, ICLAMP 2025

Y2 - 13 April 2025 through 14 April 2025

ER -

Jie ACH, Abdulrab H, Shutari H, Abdullah T, Zafar R, Althahebi A. Integration of GRU and LSTM With Fundamental and Technical Analysis for Stock Price Prediction. In Masri H, Elkadhi N, Aldulaimi S, Abdellah K, editors, Projects, Processes, Systems and Networks in the Digital Age - Proceedings of the 4th International Conference on the Leadership and Management of Projects in the Digital Age - ICLAMP 2025. Springer Science and Business Media Deutschland GmbH. 2025. p. 165-176. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-99025-0_13

Integration of GRU and LSTM With Fundamental and Technical Analysis for Stock Price Prediction

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this