The Validity of Using Technical Indicators When forecasting Stock Prices Using Deep Learning Models: Empirical Evidence Using Saudi Stocks

Salahadin Mohammed

doi:10.1109/CICN56167.2022.10008298

The Validity of Using Technical Indicators When forecasting Stock Prices Using Deep Learning Models: Empirical Evidence Using Saudi Stocks

Salahadin Mohammed^*

^*Corresponding author for this work

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

Abstract

Many researchers use deep learning and technical indicators to forecast future stock prices. There are several hundred technical indicators and each one of them has a number of parameters. Finding the optimal combination of indicators with their optimal parameter values is very challenging. The aim of this work is to study if there is any benefit of feeding deep learning models with technical indicators instead of only feeding them with price and volume. After all, technical indicators are just functions of price and volume. Empirical studies done in this work using Saudi stocks show that deep learning models can benefit from technical indicators only if the right combination of technical indicators together with their right parameter values are used. The experimental results show that the right combination of technical indicators can improve the forecasting accuracy of deep learning modules. They also showed that using the wrong combination of indicators is worse than using no indicator even if they were assigned the best parameter values.

Original language	English
Title of host publication	Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	520-524
Number of pages	5
ISBN (Electronic)	9781665487719
DOIs	https://doi.org/10.1109/CICN56167.2022.10008298
State	Published - 2022
Event	14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022 - Al-Khobar, Saudi Arabia Duration: 4 Dec 2022 → 6 Dec 2022

Publication series

Name	Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022

Conference

Conference	14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022
Country/Territory	Saudi Arabia
City	Al-Khobar
Period	4/12/22 → 6/12/22

Bibliographical note

Funding Information:
The author would like to thank King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia, for the support during this work.

Publisher Copyright:
© 2022 IEEE.

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition

Access to Document

10.1109/CICN56167.2022.10008298

Cite this

Mohammed, S. (2022). The Validity of Using Technical Indicators When forecasting Stock Prices Using Deep Learning Models: Empirical Evidence Using Saudi Stocks. In Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022 (pp. 520-524). (Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CICN56167.2022.10008298

Mohammed, Salahadin. / The Validity of Using Technical Indicators When forecasting Stock Prices Using Deep Learning Models : Empirical Evidence Using Saudi Stocks. Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 520-524 (Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022).

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title = "The Validity of Using Technical Indicators When forecasting Stock Prices Using Deep Learning Models: Empirical Evidence Using Saudi Stocks",

abstract = "Many researchers use deep learning and technical indicators to forecast future stock prices. There are several hundred technical indicators and each one of them has a number of parameters. Finding the optimal combination of indicators with their optimal parameter values is very challenging. The aim of this work is to study if there is any benefit of feeding deep learning models with technical indicators instead of only feeding them with price and volume. After all, technical indicators are just functions of price and volume. Empirical studies done in this work using Saudi stocks show that deep learning models can benefit from technical indicators only if the right combination of technical indicators together with their right parameter values are used. The experimental results show that the right combination of technical indicators can improve the forecasting accuracy of deep learning modules. They also showed that using the wrong combination of indicators is worse than using no indicator even if they were assigned the best parameter values.",

author = "Salahadin Mohammed",

note = "Funding Information: The author would like to thank King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia, for the support during this work. Publisher Copyright: {\textcopyright} 2022 IEEE.; 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022 ; Conference date: 04-12-2022 Through 06-12-2022",

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doi = "10.1109/CICN56167.2022.10008298",

language = "English",

series = "Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022",

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Mohammed, S 2022, The Validity of Using Technical Indicators When forecasting Stock Prices Using Deep Learning Models: Empirical Evidence Using Saudi Stocks. in Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022. Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022, Institute of Electrical and Electronics Engineers Inc., pp. 520-524, 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022, Al-Khobar, Saudi Arabia, 4/12/22. https://doi.org/10.1109/CICN56167.2022.10008298

The Validity of Using Technical Indicators When forecasting Stock Prices Using Deep Learning Models: Empirical Evidence Using Saudi Stocks. / Mohammed, Salahadin.
Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 520-524 (Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022).

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

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AB - Many researchers use deep learning and technical indicators to forecast future stock prices. There are several hundred technical indicators and each one of them has a number of parameters. Finding the optimal combination of indicators with their optimal parameter values is very challenging. The aim of this work is to study if there is any benefit of feeding deep learning models with technical indicators instead of only feeding them with price and volume. After all, technical indicators are just functions of price and volume. Empirical studies done in this work using Saudi stocks show that deep learning models can benefit from technical indicators only if the right combination of technical indicators together with their right parameter values are used. The experimental results show that the right combination of technical indicators can improve the forecasting accuracy of deep learning modules. They also showed that using the wrong combination of indicators is worse than using no indicator even if they were assigned the best parameter values.

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Mohammed S. The Validity of Using Technical Indicators When forecasting Stock Prices Using Deep Learning Models: Empirical Evidence Using Saudi Stocks. In Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 520-524. (Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022). doi: 10.1109/CICN56167.2022.10008298

The Validity of Using Technical Indicators When forecasting Stock Prices Using Deep Learning Models: Empirical Evidence Using Saudi Stocks

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

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Cite this