Info: Zenodo’s user support line is staffed on regular business days between Dec 23 and Jan 5. Response times may be slightly longer than normal.

Published January 26, 2022 | Version v1
Journal article Open

Stock Price Index Prediction Using Adaptive Neural Fuzzy Inference System

Creators

  • 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

Description

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.

Files

Nopianti et al.pdf

Files (400.6 kB)

Name Size Download all
md5:1c8121816dafc7ae4e6d9871cf5b072a
400.6 kB Preview Download

Additional details

References

  • 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