Document Type : Original Research

Authors

1 Accounting Department, Business and Economic Faculty, Bina Bangsa University, Serang, Indonesia

2 Industrial Engineering Departmenet, Bina Bangsa University, Serang, Indonesia

3 Management Department, Bina Bangsa University, Serang, Indonesia

Abstract

This paper aims to predict stock prices using open, high, low, close variables using artificial neural networks, especially the adaptive fuzzy neural inference system (ANFIS). Each stock has a different pattern and can be predicted if you have complete data. This study is limited by stock data for 2012-2019. The survey was conducted to collect stock data from the Yahoo Finance website. The stock data used is data from 2001-2018. Learning patterns of data patterns using the Adaptive Neural Fuzzy Inference System (ANFIS) were compared with regression analysis, Mean Square Error (MSE) and Mean Prediction Error. The results show that stock price predictions using the Adaptive Neural Fuzzy Inference System (ANFIS) have a small error rate (below 1 percent). The stock price at closing is determined by the open price and the volume of the stock. The value of the highest price of the stock and the lowest value of the stock follows the determined value of the opening price. This paper contributes to existing research in economics, especially stock investment and Financial Technology.

Keywords

Main Subjects

COPYRIGHTS

©2021 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.

Asadi, S. (2019). Evolutionary fuzzification of RIPPER for regression: Case study of stock prediction. Neurocomputing, 331, 121–137. https://doi.org/10.1016/j.neuc om.2018.11.052
Bollen, J., & Mao, H. (2011). Twitter Mood as a Stock Market Predictor. Computer, 44(10), 91–94. https://doi.org/10.1109/mc.2011.323
Boučková, M. (2015). Management Accounting and Agency Theory. Procedia Economics and Finance, 25, 5–13. https://doi.org/10.1016/s2212-5671(15)00707-8
Dănescu, T., Prozan, M., & Prozan, R. D. (2015). Perspectives Regarding Accounting – Corporate Governance – Internal Control. Procedia Economics and Finance, 32, 588–594. https://doi.org/10.1016/s2212-5671(15)01436-7
G.Siegel, J. (2000). Dictionary of Accounting Terms.
Gálvez, R. H., & Gravano, A. (2017). Assessing the usefulness of online message board mining in automatic stock prediction systems. Journal of Computational Science, 19, 43–56. https://doi.org/10.1016/j.jocs.2017.01.001
Göçken, M., Özçalıcı, M., Boru, A., & Dosdoğru, A. T. (2016). Integrating metaheuristics and Artificial Neural Networks for improved stock price prediction. Expert Systems with Applications, 44, 320–331. https://doi.org/10.1016/j.eswa.2015.09.029
Goykhman, M., & Teimouri, A. (2018). Machine learning in sentiment reconstruction of the simulated stock market. Physica A: Statistical Mechanics and Its Applications, 492, 1729–1740. https://doi.org/10.1016/j.physa.2017.11.093
Gunduz, H., Yaslan, Y., & Cataltepe, Z. (2017). Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations. Knowledge-Based Systems, 137, 138–148. https://doi.org/10.1016/j.knosys.2017.09.023
Hu, H., Tang, L., Zhang, S., & Wang, H. (2018). Predicting the direction of stock markets using optimized neural networks with Google Trends. Neurocomputing, 285, 188–195. https://doi.org/10.1016/j.neucom.2018.01.038
Jang, J. S. R., Sun, C. T., & Mizutani, E. (2005). Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review]. In IEEE Transactions on Automatic Control (Vol. 42, Issue 10). Prentice-Hall, Inc. https://doi.org/10.1109/tac.1997.633847
Lahmiri, S. (2018). Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regression. Applied Mathematics and Computation, 320, 444–451. https://doi.org/10.1016/j.amc.2017.09.049
Larcker, D. F., Richardson, S. A., & Tuna, I. (2017). Corporate Governance, Accounting Outcomes, and Organizational Performance. The Accounting Review, 82(4), 963–1008. https://doi.org/10.2308/accr.2007.82.4.963
Packham, N. (2018). Optimal contracts under competition when uncertainty from adverse selection and moral hazard are present. Statistics & Probability Letters, 137, 99–104. https://doi.org/10.1016/j.spl.2018.01.014
Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques. Expert Systems with Applications, 42(1), 259–268. https://doi.org/10.1016/j.eswa.2014.07.040
Sedmihradská, L. (2015). Budget Transparency in Czech Local Government. Procedia Economics and Finance, 25, 598–606. https://doi.org/10.1016/s2212-5671(15)00774-1
Wan, Y., & Si, Y.-W. (2017). Adaptive neuro fuzzy inference system for chart pattern matching in financial time series. Applied Soft Computing, 57, 1–18. https://doi.org/10.1016/j.asoc.2017.03.023
Wang, J.-Z., Wang, J.-J., Zhang, Z.-G., & Guo, S.-P. (2011). Forecasting stock indices with back propagation neural network. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2011.04.222
Zahedi, J., & Rounaghi, M. M. (2015). Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange. Physica A: Statistical Mechanics and Its Applications, 438, 178–187. https://doi.org/10.1016/j.physa.2015.06.033