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FORECASTING STOCK PRICES ON THE LQ45 INDEX USING THE VARIMAX METHOD

*Dinul Darma Atmaja  -  Department of Mathematics, Faculty of Science and Mathematics, Diponegoro University, Indonesia
Widowati Widowati  -  Department of Mathematics, Faculty of Science and Mathematics, Diponegoro University, Indonesia
Budi Warsito  -  Department of Statistics, Faculty of Science and Mathematics, Diponegoro University, Indonesia
Open Access Copyright (c) 2021 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
Forecasting using the Autoregressive Integrated Moving Average (ARIMA) method is not appropriate to predict more than one stock price because this method is only able to model one dependent variable. Therefore, to expect more than one stock prices, the ARIMA method expansion can be used, namely the Vector Autoregressive Integrated Moving Average (VARIMA) method. Furthermore, this research will discuss forecasting stock prices on the LQ45 index using the Vector Autoregressive Integrated Moving Average with Exogenous Variable (VARIMAX) method. Then, after the initial model formation process, the best model is the VARIMAX (0,1,2) model. Finally, the results of this study using the VARIMAX (0,1,2) model obtained the predictive value of the prices and the error values of stocks on the LQ45 index.

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Keywords: Forecasting; Multivariate Time Series Analysis; VARMA, Indonesia Stock Exchange (IDX); LQ45.

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  1. Adebiyi, A.A., Adewumi, A.O., Ayo, C.K. 2014. Stock Price Prediction Using the ARIMA Model. Proceedings on the 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation. United Kingdom, pp 106-112
  2. Chai, T. and Draxler, R. R. 2014. Root Mean Square Error (RMSE) or Mean Absolute Error (MAE)? – Arguments Against Avoiding RMSE in the Literature, Geoscientific Model Development, 7, pp 1247-1250
  3. Das, P. 2019. Econometrics in Theory and Practice. Singapore. Springer
  4. Du, Y. 2018. Application and Analysis of Forecasting Stock Price Index Based on Combination of ARIMA Model and BP Neural Network. Proceedings on the Chinese Control and Decision Conference (CCDC). China, pp 2854-2857
  5. Fathurahman, M. 2009. Pemilihan Model Regresi Terbaik Menggunakan Metode Akaike’s Information Criterion dan Schwarz Information Criterion, Jurnal Informatika Mulawarman, Vol. 4, No. 3, pp 37-41
  6. Idrees, S. M., Alam, M. A., Agarwal, P. 2019. A Prediction Approach for Stock Market Volatility Based on Time Series Data, Journal of IEEE Access, Vol. 7
  7. Majumder, M. R. and Hossain, I. 2019. Limitation of ARIMA in Extremely Collapsed Market: A Proposed Method. Proceedings on the International Conference on Electrical, Computer and Communication Engineering (ECCE). Bangladesh
  8. Mauludiyanto, A., Hendrantoro, G., Mauridhi, H. P., Suhartono. 2009. Pemodelan VARIMA dengan Efek Deteksi Outlier Terhadap Data Curah Hujan. Proceedings on Seminar Nasional Aplikasi Teknologi Informasi 2009 (SNATI 2009). Yogyakarta, pp 1-4
  9. Pratama, R. I. and Saputro, D. R. S. 2018. Model Runtun Waktu Vector Autoregressive Moving Average with Exogenous Variable (VARMAX). Proceedings on Konferensi Nasional Penelitian Matematika dan Pembelajarannya III (KNPMP III). Solo, pp 490-497
  10. T. Trimono, A. Sonhaji, and U. Mukhaiyar, Forecasting Farmer Exchange Rate in Central Java Province Using Vector Integrated Moving Average, Media Statistika, Vol. 13, no. 2, pp. 182-193, Dec. 2020. https://doi.org/10.14710/medstat.13.2.182-193
  11. Warsono, Russel, E., Wamiliana, Widiarti, Usman, M. 2019. Modeling and Forecasting by the Vector Autoregressive Moving Average Model for Export of Coal and Oil Data (Case Study from Indonesia over the Years 2002-2017), Journal of Econjournal, Vol. 9, No. 4, pp 240-247
  12. Wei, W. W. S. 2006. Time Series Analysis Univariate and Multivariate Methods 2nd Edition. United States. Pearson
  13. Wichaidit, S. and Kittitornkun, S. 2015. Predicting SET50 Stock Prices using CARIMA (Cross Correlation ARIMA). Proceedings on the International Computer Science and Engineering Conference (ICSEC). Thailand
  14. Ye, T. 2017. Stock Forecasting Method Based on Wavelet Analysis and ARIMA-SVR model. Proceedings on the 3rd International Conference on Information Management (ICIM). China, pp 102-106
  15. Zadrozny, P. A., & Chen, B. (2019). Weighted-Covariance Factor Decomposition of Varma Models Applied to Forecasting Quarterly U.S. Real GDP at Monthly Intervals. Journal of Time Series Analysis, 40(6), 968–986. https://doi.org/10.1111/jtsa.12506

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