Elliott Wave Prediction Using a Neural Network and Its Application to The Formation of Investment Portfolios on The Indonesian Stock Exchange

Authors

  • Muhammad Rifqi Arrahim Natadikarta Telkom University, Bandung
  • Deni Saepudin Telkom University, Bandung

DOI:

https://doi.org/10.30865/mib.v7i1.5525

Keywords:

Stock Market, Prediction, Neural Network, Elliott Wave, Fast Fourier Transform

Abstract

Predicting stock trends is a complex problem because their movements constantly fluctuate and are affected by many factors such as political elements, world economic situation, investor expectations, and psychological factors. One way to analyze stock prices is Technical Analysis. This method focuses on stock indicators and patterns formed. Elliott Wave is one of the Technical Analysis methods. This study suggests an approach based on the combination of Neural Networks and Elliott Wave theory which will predict the possible direction of future trends. This model uses Fast Fourier Transform (FFT) coefficients to look for similarities with Elliott Wave patterns. When the dataset is similar to Elliott Wave patterns, the Neural Network will predict the direction of the stock trend. This study was conducted to confirm that the Elliott Wave-based Neural Network method helps predict stock trends. Experimentation is being carried out in some stocks on the Indonesian Stock Exchange, with profits exceeding 90%.

References

E. B. Santoso and M. Ikhsan, “Efficient Market Hypothesis in Indonesia Stock Exchange 2019,†in Proceedings of the Annual International Conference on Accounting Research (AICAR 2019), 2020, pp. 51–53. doi: https://doi.org/10.2991/aebmr.k.200309.012.

E. B. Santoso and M. Ikhsan, “Efficient Market Hypothesis in Indonesia Stock Exchange 2019,†2020.

S. Jakpar, M. Tinggi, A. H. Tak, and C. W. Yi, “Fundamental Analysis VS Technical Analysis: The Comparison of Two Analysis in Malaysia Stock Market,†2018.

X. Pang, Y. Zhou, P. Wang, W. Lin, and V. Chang, “An innovative neural network approach for stock market prediction,†J Supercomput, vol. 76, no. 3, pp. 2098–2118, 2020, doi: 10.1007/s11227-017-2228-y.

H. Duan, X. Xiao, J. Yang, and B. Zeng, “Elliott wave theory and the Fibonacci sequence-gray model and their application in Chinese stock market,†Journal of Intelligent & Fuzzy Systems, vol. 34, pp. 1813–1825, 2018, doi: 10.3233/JIFS-17108.

K. S. Vishvaksenan, R. Kalaiarasan, R. Kalidoss, and R. Karthipan, “Real Time Experimental Study and Analysis of Elliott Wave Theory in Signal Strength Prediction,†Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, vol. 88, no. 1, pp. 107–119, 2018, doi: 10.1007/s40010-016-0323-8.

D. Zhang and S. Lou, “The application research of neural network and BP algorithm in stock price pattern classification and prediction,†Future Generation Computer Systems, vol. 115, pp. 872–879, Feb. 2021, doi: 10.1016/J.FUTURE.2020.10.009.

A. Moghar and M. Hamiche, “Stock Market Prediction Using LSTM Recurrent Neural Network,†Procedia Comput Sci, vol. 170, pp. 1168–1173, Jan. 2020, doi: 10.1016/J.PROCS.2020.03.049.

D. Selvamuthu, V. Kumar, and A. Mishra, “Indian stock market prediction using artificial neural networks on tick data,†Financial Innovation, vol. 5, no. 1, p. 16, 2019, doi: 10.1186/s40854-019-0131-7.

R. Jarusek, E. Volna, and M. Kotyrba, “FOREX rate prediction improved by Elliott waves patterns based on neural networks,†Neural Networks, vol. 145, pp. 342–355, 2022, doi: 10.1016/j.neunet.2021.10.024.

J. Tanuwijaya and S. Hansun, “LQ45 stock index prediction using k-nearest neighbors regression,†International Journal of Recent Technology and Engineering, vol. 8, no. 3, pp. 2388–2391, Sep. 2019, doi: 10.35940/ijrte.C4663.098319.

S. Hansun and J. C. Young, “Predicting LQ45 financial sector indices using RNN-LSTM,†J Big Data, vol. 8, no. 1, p. 104, 2021, doi: 10.1186/s40537-021-00495-x.

R. N. Elliott, Nature’s law: The secret of the universe. Institute for Economic & Financial Research, 1982.

P. R. Prechter, R.R, Kendall, Prechter’s Perspective. New Classics, 1996.

S. K. Chandar, “Stock market prediction using subtractive clustering for a neuro fuzzy hybrid approach,†Cluster Comput, vol. 22, no. s6, pp. 13159–13166, 2019, doi: 10.1007/s10586-017-1321-6.

Institute of Electrical and Electronics Engineers. Bangladesh Section and IEEE Communications Society, 2018 21st International Conference of Computer and Information Technology (ICCIT).

E. Chu, Discrete and continuous fourier transforms: Analysis, applications and fast algorithms. Chapman and Hall/CRC, 2008.

A. Yazdinejad, B. Zolfaghari, A. Dehghantanha, H. Karimipour, G. Srivastava, and R. M. Parizi, “Accurate threat hunting in industrial internet of things edge devices,†Digital Communications and Networks, Sep. 2022, doi: 10.1016/j.dcan.2022.09.010.

G. S. K. Ranjan, A. K. Verma, and S. Radhika, “K-Nearest Neighbors and Grid Search CV Based Real Time Fault Monitoring System for Industries,†in 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), 2019, pp. 1–5. doi: 10.1109/I2CT45611.2019.9033691.

Y. Shuai, Y. Zheng, and H. Huang, “Hybrid Software Obsolescence Evaluation Model Based on PCA-SVM-GridSearchCV,†in 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS), 2018, pp. 449–453. doi: 10.1109/ICSESS.2018.8663753.

Downloads

Published

2023-01-28

Issue

Vol. 7 No. 1 (2023): Januari 2023

Section

Articles

License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).