Comparison of Grid Search and Evolutionary Parameter Optimization with Neural Networks on JCI Stock Price Movements during the Covid 19

  • Wresti Universitas Dian Nuswantoro Semarang
  • Gunawan STMIK YMI Tegal
  • Purwanto Universitas Dian Nuswantoro Semarang
  • Catur Supriyanto Universitas Dian Nuswantoro Semarang
Keywords: neural network, optimization, grid search, evolutionary, preprocessing, RMSE.

Abstract

This study aims to determine the effect of covid 19 on the movement of the JCI Stock Price by testing various combinations of the input variables of closed price stock data on the JCI. The analysis is carried out to find the best RMSE value from the combination of these input variables using the neural network method. The best RMSE results are compared using the optimization of grid search and evolutionary parameters. The data used in this study was taken from the Yahoo.finance.com page on the JCI Historical Data, during the covid pandemic, from 12/11/2019 to 12/30/2021. The data obtained are 509 records. The input variable used is the closing price data (closed price) as a target. The preprocessing data used are data cleansing, filtering, and windowing until seven days before. The results obtained an RMSE value of 0.104 five days before Close t (P=5), training cycle 9000. Momentum 0.9 and learning rate 0.2 is then optimized using the grid search parameter to produce RMSE 0.101, training cycle 100. Learning rate 1 and momentum 0.1 are then compared with evolutionary parameters, which make RMSE 0.103 at learning rate 0.029, momentum 0.68, and training cycle 86. Based on this research, optimizing grid search parameters produces better RMSE than evolutionary parameter optimization. This small RMSE result shows that investors are still safe to invest.

 

Downloads

Download data is not yet available.

References

C. H. Cheng, T. L. Chen, and L. Y. Wei, “A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting,” Inf. Sci. (Ny)., vol. 180, no. 9, pp. 1610–1629, 2010, doi: 10.1016/j.ins.2010.01.014.

W. H. Beaver, “Perspectives on recent capital market research,” Account. Rev., vol. 77, no. 2, pp. 453–474, 2002, doi: 10.2308/accr.2002.77.2.453.

W. Ma, Y. Wang, and N. Dong, “Study on stock price prediction based on BP neural network,” Proc. - 2010 IEEE Int. Conf. Emerg. Manag. Manag. Sci. ICEMMS 2010, pp. 57–60, 2010, doi: 10.1109/ICEMMS.2010.5563502.

WHO, “Coronavirus Disease Coronavirus Disease Ikhtisar kegiatan World Health World Health Organization Organization,” World Heal. Organ., vol. 19, pp. 1–13, 2020.

S. Samsir et al., “Implementation Learning Vector Quantization Using Neural Network for Classification of Ear, Nose and Throat Disease,” in Journal of Physics: Conference Series, Dec. 2022, p. 012016. doi: 10.1088/1742-6596/2394/1/012016

S. Lahmiri and S. Bekiros, “The impact of COVID-19 pandemic upon stability and sequential irregularity of equity and cryptocurrency markets,” Chaos, Solitons and Fractals, vol. 138, p. 109936, 2020, doi: 10.1016/j.chaos.2020.109936.

Y. Ding, X. Song, and Y. Zen, “Forecasting financial condition of Chinese listed companies based on support vector machine,” Expert Syst. Appl., vol. 34, no. 4, pp. 3081–3089, 2008, doi: 10.1016/j.eswa.2007.06.037.

Y. Zuo and E. Kita, “Up/Down Analysis of Stock Index by Using Bayesian Network,” Eng. Manag. Res., vol. 1, no. 2, 2012, doi: 10.5539/emr.v1n2p46.

S. I. Sulaiman, T. K. A. Rahman, I. Musirin, and S. Shaari, “Artificial Neural Network versus linear regression for predicting grid-connected photovoltaic system output,” Proc. - 2012 IEEE Int. Conf. Cyber Technol. Autom. Control. Intell. Syst. CYBER 2012, pp. 170–174, 2012, doi: 10.1109/CYBER.2012.6392548.

Z. W. Zheng, Y. Y. Chen, X. W. Zhou, M. M. Huo, B. Zhao, and M. Y. Guo, “Short-term prediction model for a grid-connected photovoltaic system using EMD and GABPNN,” Appl. Mech. Mater., vol. 291–294, pp. 74–82, 2013, doi: 10.4028/www.scientific.net/AMM.291-294.74.

E. Ndiaye, T. Le, O. Fercoq, J. Salmon, and I. Takeuchi, “Safe grid search with optimal complexity,” 36th Int. Conf. Mach. Learn. ICML 2019, vol. 2019-June, pp. 8362–8371, 2019.

H. Abbasimehr and R. Paki, “Prediction of COVID-19 Confirmed Cases Combining Deep Learning Methods and Bayesian Optimization,” Chaos, Solitons Fractals Interdiscip. J. Nonlinear Sci. Nonequilibrium Complex Phenom., p. 110511, 2020, doi: 10.1016/j.chaos.2020.110511.

F. Ronaghi, M. Salimibeni, F. Naderkhani, and A. Mohammadi, “COVID19-HPSMP: COVID-19 adopted Hybrid and Parallel deep information fusion framework for stock price movement prediction,” Expert Syst. Appl., vol. 187, no. September 2021, p. 115879, 2022, doi: 10.1016/j.eswa.2021.115879.

T. M. Martins and R. F. Neves, “Applying genetic algorithms with speciation for optimization of grid template pattern detection in financial markets,” Expert Syst. Appl., vol. 147, 2020, doi: 10.1016/j.eswa.2020.113191.

W. Chen, H. Zhang, M. K. Mehlawat, and L. Jia, “Mean–variance portfolio optimization using machine learning-based stock price prediction,” Appl. Soft Comput., vol. 100, p. 106943, 2021, doi: 10.1016/j.asoc.2020.106943.

P. K. Prasetyanto, “Pengaruh Produk Domestik Bruto Dan Inflasi Terhadap Indeks Harga Saham Gabungan Di Bursa Efek Indonesia Tahun 2002-2009,” J. Ris. Akunt. Dan Bisnis Airlangga, vol. 1, no. 1, pp. 60–84, 2017, doi: 10.31093/jraba.v1i1.9.

F. Fuad and I. Yuliadi, “Determinants of the Composite Stock Price Index (IHSG) on the Indonesia Stock Exchange,” J. Econ. Res. Soc. Sci., vol. 5, no. 1, pp. 27–41, 2021, doi: 10.18196/jerss.v5i1.11002.

M. Ćalasan, S. H. E. Abdel Aleem, and A. F. Zobaa, “On the root mean square error (RMSE) calculation for parameter estimation of photovoltaic models: A novel exact analytical solution based on Lambert W function,” Energy Convers. Manag., vol. 210, no. March, p. 112716, 2020, doi: 10.1016/j.enconman.2020.112716.

F. Miguel, M. Frutos, F. Tohme, and M. M. Babey, “A decision support tool for urban freight transport planning based on a multi-objective evolutionary algorithm,” IEEE Access, vol. 7, pp. 156707–156721, 2019, doi: 10.1109/ACCESS.2019.2949948.

Published
2022-12-30
How to Cite
Wresti, Gunawan, Purwanto, & Catur Supriyanto. (2022). Comparison of Grid Search and Evolutionary Parameter Optimization with Neural Networks on JCI Stock Price Movements during the Covid 19. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(6), 1079 - 1087. https://doi.org/10.29207/resti.v6i6.4402
Section
Artikel Teknologi Informasi