DOI: https://doi.org/10.21456/vol12iss1pp17-25

Peramalan Harga Saham Serentak Menggunakan Model Multivariate Singular Spectrum Analysis

*Aris Marjuni orcid scopus publons  -  Program Magister Teknik Informatika, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro, Semarang, 50131, Indonesia
Open Access Copyright (c) 2022 JSINBIS (Jurnal Sistem Informasi Bisnis)

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Abstract

Stock price fluctuations in the stock market are widely influenced by financial environment changes in both micro and macro that are usually unpredictable and can not be controlled by stock players. On the other side, stock price information is very essential and much needed for both buyers and traders. Stock price forecasting is one of the analytical techniques to obtain stock price prediction based on the previous historical stock prices. The open and close prices are important information in stock trading. The opening price can influence the movement towards the closing price, and the closing price becomes important for the next day's opening price. In technical analysis, the relationship between the two stock prices can be parametric or non-parametric. This study discusses the stock price prediction or forecasting through the non-parametric approach using a multivariate singular spectrum analysis method with the consideration that open and close prices are simultaneously working in the same system and time. Performance evaluation using Mean Absolute Percentage Error shows that the multivariate singular spectrum analysis method can produce predicted open and close prices with an error rate of 3.18% and 3.21%, respectively. Hence, this method can be used as an alternative for stock price forecasting simultaneously.

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Keywords: Stock Price Forecasting; Multivariate Model; Singular Spectrum Analysis; Non-Parametric Approach

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Section: Research Articles
Language : ID
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  1. Abreau, R.J., Souze, R.M., Oliveira, J.G., 2019. Applying Singular Spectrum Analysis and ARIMA-GARCH for forecasting EUR/USD exchange rate. Revista de Administração Mackenzie 20 (4), 1–32
  2. Adnyana, I.M., 2020. Manajemen Investasi dan Portofolio. Lembaga Penerbitan Universitas Nasional, Jakarta
  3. Jin, X., Liu, X., Guo, J., Shen, Y., 2021. Multi-channel singular spectrum analysis on geocenter motion and its precise prediction. Sensors 21 (4), 1–15
  4. Lahmiri, S., 2018. Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regression. Applied Mathematics and Computation 320, 444–451
  5. Leles, M.C.R., Mozelli, L.A., Júnior, C.L.N., Sbruzzi, E.F., Guimarães, H.N., 2018. Study on singular spectrum analysis as a new technical oscillator for trading rules design. Fluctuation and Noise Letters 17 (4), 1–21
  6. Mao X., Shang, P., 2019. Multivariate singular spectrum analysis for traffic time series,” Physica A: Statistical Mechanics and its Applications 526, 121063:1–13
  7. Ogrosky, H.R., Stechmann, S.N., Chen, N., Majda, A.J., 2019. Singular spectrum analysis with conditional predictions for real-time state estimation and forecasting. Geophysical Research Letters 46 (3), 1851–1860
  8. Rajakumar, M.P., Jegatheesan, R., Chandy, R., Sampath, T., 2019. Prediction of stock prices using unstructured and semi-structured qualitative data - a neural network approach. International Journal of Intelligent Engineering and Systems 12 (2), 156–169
  9. Rodrigues, P.C., Mahmoudvand, R., 2018. The benefits of multivariate singular spectrum analysis over the univariate version,” Journal of The Franklin Institute 355 (1), 544–564
  10. Saayman A., Klerk, J.de, 2019. Forecasting tourist arrivals using multivariate singular spectrum analysis,” Tourism Economics 25 (3), 330–354
  11. Shen, J., Shafiq, M.O., 2020. Short-term stock market price trend prediction using a comprehensive deep learning system. Journal of Big Data 7 (66), 1–33
  12. Stiawan, E., 2021. Pasar Modal Syariah. CV Sinar Jaya Berseri, Bengkulu
  13. Suhartono, Setyowati, E., Salehah, N.A., Lee, M.H., Rahayu, S.P., Ulama, B.S.S., 2019. A hybrid singular spectrum analysis and neural networks for forecasting inflow and outflow currency of Bank Indonesia. In: Communications in Computer and Information Science, vol. 937. Springer, Singapore, 3–18
  14. Syukur, A., Marjuni, A., 2020. Stock price forecasting using univariate singular spectral analysis through Hadamard transform. International Journal of Intelligent Engineering and Systems 13 (2), 96–107
  15. Tsai, M.C., Cheng, C.H., Tsai, M.I., Shiu, H.Y. Forecasting leading industry stock prices based on a hybrid time-series forecast model. PLoS One 13 (12), 1–24
  16. Valenzuela, O., Rojas, F., Pomares, H., Rojas, I., 2019. Theory and Applications of Time Series Analysis. Springer Cham, Switzerland
  17. Vijh, M., Chandola, D., Tikkiwal, V.A. dan Kumar, A., 2020. Stock closing price prediction using machine learning techniques. Procedia Computer Science 167 (2019), 599–606
  18. Wiranata, R.B., Djunaidy, A., The stock exchange prediction using machine learning techniques: a comprehensive and systematic literature review. Jurnal Ilmu Komputer dan Informatika 14 (2), 91–112
  19. Xiao, J., Zhu, X., Huang, C., Yang, X., Wen, F., Zhong, M., 2019. A new approach for stock price analysis and prediction based on SSA and SVM. International Journal of Information Technology & Decision Making 18 (1), 287–310

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