Google Trends and Technical Indicator based Machine Learning for Stock Market Prediction
Keywords:
Forecasting, Google Trends, Machine Learning, Prediction, Stock Price, Technical Indicator
Abstract
The stock market often attracts investors to invest, but it is not uncommon for investors to experience losses when buying and selling shares. This causes investors to hesitate to determine when to sell or buy shares in the stock market. The accurate stock price prediction will help investors to decide when to buy or sell their shares. In this study, we propose a new approach to predicting stocks using machine learning with a combination of features from stock price features, technical indicators, and Google trends data. Three well-known machine learning algorithms such as Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Multiple Linear regression are used to predict future stock prices. The test results show that the SVR outperformed the MLP and Multiple Linear Regression to predict stock prices for Indonesian stocks with an average MAPE is 0.50%. The SVR can predict the stock price close to the actual price.
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to It],” dec 2021. [Online]. Available: https://ajaib.co.id/fluktuatif-dalam-investasi-saham-dan-cara-menyikapinya/
[2] M. Mayadi and A. Anggrawan, “Pengembangan Sistem Informasi Pemantauan Harga Beras dan Gabah dengan Short Message
Gateway [Development of Rice and Grain Price Monitoring Information System with Short Message Gateway],” MATRIK :
Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 2, pp. 237–248, mar 2022. [Online]. Available:
https://journal.universitasbumigora.ac.id/index.php/matrik/article/view/1546
[3] A. Santoso and S. Hansun, “Prediksi IHSG dengan Backpropagation Neural Network [JCI Prediction with Backpropagation
Neural Network],” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 3, no. 2, pp. 313–318, aug 2019. [Online].
Available: https://jurnal.iaii.or.id/index.php/RESTI/article/view/887
[4] M. M. Kumbure, C. Lohrmann, P. Luukka, and J. Porras, “Machine learning techniques and data for stock market forecasting:
A literature review,” Expert Systems with Applications, vol. 197, no. 1, p. 116659, jul 2022.
[5] N. N. M. Cahyani and L. P. Mahyuni, “Akurasi Moving Average Dalam Prediksi Saham LQ45 di Bursa Efek Indonesia
[Moving Average Accuracy In Prediction Of LQ45 Share In The Indonesia Stock Exchange],” Jurnal Manajemen, vol. 9, no. 7,
pp. 2769–2789, jul 2020. [Online]. Available: https://ojs.unud.ac.id/index.php/manajemen/article/view/60655
[6] A. Raudys and ˇ Z. Pabarˇskait, “Optimising the smoothness and accuracy of moving average for stock price data,” Technological
and Economic Development of Economy, vol. 24, no. 3, pp. 984–1003, jan 2018.
[7] M. C. Rivo and R. T. Ratnasari, “Faktor Yang Mempengaruhi Perilaku Investor Muslim Dalam Keputusan Berinvestasi
Saham Syariah [Factors Affecting The Behavior Of Muslim Investors In Investment Decision Of Shariah Stock],”
Jurnal Ekonomi Syariah Teori dan Terapan, vol. 7, no. 11, pp. 2202–2220, nov 2020. [Online]. Available:
https://e-journal.unair.ac.id/JESTT/article/view/22644
[8] B. M. Henrique, V. A. Sobreiro, and H. Kimura, “Literature review: Machine learning techniques applied to financial market
prediction,” Expert Systems with Applications, vol. 124, no. 1, pp. 226–251, jun 2019.
[9] ——, “Stock price prediction using support vector regression on daily and up to the minute prices,” The Journal of Finance and
Data Science, vol. 4, no. 3, pp. 183–201, sep 2018.
[10] Y. Shynkevich, T. M. McGinnity, S. A. Coleman, A. Belatreche, and Y. Li, “Forecasting price movements using technical
indicators: Investigating the impact of varying input window length,” Neurocomputing, vol. 264, no. 1, pp. 71–88, nov 2017.
[11] R. K. Nayak, D. Mishra, and A. K. Rath, “A Na¨ıve SVM-KNN based stock market trend reversal analysis for Indian benchmark
indices,” Applied Soft Computing, vol. 35, no. 1, pp. 670–680, oct 2015.
[12] M. Y. Huang, R. R. Rojas, and P. D. Convery, “Forecasting stock market movements using Google Trend searches,”
Empirical Economics, vol. 59, no. 6, pp. 2821–2839, dec 2020. [Online]. Available: https://link.springer.com/article/10.1007/
s00181-019-01725-1
[13] T. Preis, H. S. Moat, and H. Eugene Stanley, “Quantifying Trading Behavior in Financial Markets Using Google Trends,”
Scientific Reports 2013 3:1, vol. 3, no. 1, pp. 1–6, apr 2013. [Online]. Available: https://www.nature.com/articles/srep01684
[14] H. Hu, L. Tang, S. Zhang, and H.Wang, “Predicting the direction of stock markets using optimized neural networks with Google
Trends,” Neurocomputing, vol. 285, no. 1, pp. 188–195, apr 2018.
[15] G. Budiprasetyo, M. Hani’ah, and D. Z. Alfah, “Prediksi Harga Saham Syariah Menggunakan Algoritma Long
Short-Term Memory (LSTM) [Sharia Stock Price Prediction Using Long Short-Term Memory (LSTM) Algorithm],”
Jurnal Nasional Teknologi dan Sistem Informasi, vol. 8, no. 3, pp. 164–172, jan 2022. [Online]. Available:
https://teknosi.fti.unand.ac.id/index.php/teknosi/article/view/2142
[16] H. Zhu, X. You, and S. Liu, “Multiple Ant Colony Optimization Based on Pearson Correlation Coefficient,” IEEE Access, vol. 7,
no. 1, pp. 61 628–61 638, 2019.
[17] A. Hasan, O. Kalipsiz, and S. Akyoku, “Modeling Traders’ Behavior with Deep Learning and Machine Learning Methods,”
Complexity, vol. 2020, no. 1, pp. 1–16, 2020. [Online]. Available: https://dl.acm.org/doi/10.1155/2020/8285149
[18] A. D.W. Sumari, G. Budiprasetyo, M. Hani’ah, V. A. H. Firdaus, and R. Arianto, Machine Learning. Malang: Polinema Press,
oct 2021.
[19] M. Alida and M. Mustikasari, “Rupiah Exchange Prediction of US Dollar Using Linear, Polynomial, and Radial Basis Function
Kernel in Support Vector Regression,” Jurnal Online Informatika, vol. 5, no. 1, pp. 53–60, jul 2020. [Online]. Available:
http://join.if.uinsgd.ac.id/index.php/join/article/view/537
[20] A. Malar, P. Vincent, and H. Salleh, “An Investigation Into The Performance Of The Multilayer Perceptron Architecture Of
Deep Learning In Forecasting Stock Prices,” Universiti Malaysia Terengganu Journal of Undergraduate Research, vol. 3,
no. 2, pp. 61–68, apr 2021. [Online]. Available: https://journal.umt.edu.my/index.php/umtjur/article/view/205
[21] N.Wei, C. Li, X. Peng, F. Zeng, and X. Lu, “Conventional models and artificial intelligence-based models for energy consumption
forecasting: A review,” Journal of Petroleum Science and Engineering, vol. 181, no. 1, p. 106187, oct 2019.
[22] A. A. Winoto and A. F. V. Roy, “Model of Predicting the Rating of Bridge Conditions in Indonesia with Regression and K-Fold
Cross Validation,” International Journal of Sustainable Construction Engineering and Technology, vol. 14, no. 1, pp. 249–259,
feb 2023. [Online]. Available: https://publisher.uthm.edu.my/ojs/index.php/IJSCET/article/view/12441
Published
2023-03-31
How to Cite
Hani’ah, M., Abdullah, M., Sabilla, W., Akbar, S., & Shafara, D. (2023). Google Trends and Technical Indicator based Machine Learning for Stock Market Prediction. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 22(2), 271-284. https://doi.org/https://doi.org/10.30812/matrik.v22i2.2287
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