Predicting Stock Market Trends of Iran Using Elliott Wave Oscillation and Relative Strength Index

Document Type : Research Paper

Authors

1 PhD Candidate, Department of Accounting, Faculty of Humanities, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.

2 , Assistant Prof., Department of Accounting, Faculty of Humanities, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.

3 Assistant Prof., Department of Accounting, Faculty of Humanities, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.

4 Assistant Prof., Department of Computer, Faculty of Engineering, Hamadan Branch, Islamic Azad University, Hamadan, Iran.

Abstract

Objective: Elliott wave theory is one of the tools of technical analysis based on the psychology of individuals; which in recent years has become an important tool for analysts and investors. This theory exists in all financial markets, especially the stock market, which is widely welcomed and popular. Based on this theory, this study seeks to determine the future trend of the Iranian stock market through Elliott wave oscillators and machine learning algorithms supervised and classification.
Methods: Total index data from 2008-05-14 to 2020-11-25 were reviewed daily and Elliott wave patterns were identified using the Elliott wave oscillator and relative motion strength index and labeled into three categories: LONG, SHORT, and HOLD. Machine learning algorithms include Decision tree, Naive Bayes, Support vector machine to repeat these learning patterns, then tested on test data.
Results: The results showed that in the Tehran Stock Exchange index, identifiable Elliott waves and Support vector machine and Decision tree algorithms are able to predict the future trend of the total index with an accuracy of over 90 percent.
Conclusion: In the Iranian capital market, the chart of the Elliott Behavior Index is observed and all active persons in the Tehran Stock Exchange can use the proposed method for their trading system.

Keywords

References

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Financial Research Journal
Volume 23, Issue 1
2021
Pages 134-157

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