Prediction of Nike’s Stock Price Based on the Best Time Series Modeling
Abstract
Nike is one of the world's largest shoe, clothing, and sports equipment companies. The more modern the development of the era, the more diverse the fashion. Of course, investors can consider this when deciding whether to invest in Nike's brand shares. Stock prices constantly fluctuate up and down, so investors need to implement strategies to minimize losses in investing to achieve economic growth. This supports the Sustainable Development Goals (SDGs) in point 8 regarding the importance of sustainable economic growth and investment in infrastructure development to improve economic welfare. Investors can minimize losses by predicting or forecasting stock prices. Stock prices can be analyzed using specific methods. The update that will be brought in this study is the Nike brand stock price prediction for the 2020-2024 period using the best model from the time series method comparison conducted using classical nonparametric, which consists of the kernel estimator method and the Fourier series estimator method and modern nonparametric using the Support Vector Regression (SVR) method. Based on the analysis method, the best method is selected through the minimum MAPE value. A comparison of the results of Nike brand stock price predictions using several methods shows that the MAPE value of the Nike brand stock price data analysis is the minimum obtained using the kernel estimator approach, which is 1.564%. Thus, the kernel estimator approach predicts the Nike brand stock price much better. Predictions using the best methods can be recommendations and evaluations for economic actors to prepare better economic planning.
Keywords
Full Text:
PDFReferences
R. C. Pajar and A. Pustikaningsih, "Pengaruh Motivasi Investasi dan Pengetahuan Investasi Terhadap Minat Investasi di Pasar Modal Pada Mahasiswa FE UNY," Jurnal Profita: Kajian Ilmu Akuntansi, vol. 5, no. 1, 2017.
Z. M. Wang and C. Wu, "Application of Support Vector Regression Method in Stock Market Forecasting," International Conference on Management and Service Science, Wuhan, pp. 1-4, 2010.
Y. Ujianto and I. M. Irawan, "Perbandingan Performansi Metode Peramalan Fuzzy Time Series yang Dimodifikasi dan Jaringan Syaraf Tiruan Backpropagation (Studi Kasus: Penutupan Harga IHSG)," Jurnal Sains dan Seni ITS, vol. 4, no. 2, 2016.
A. Marjuni, "Peramalan Harga Saham Serentak Menggunakan Model Multivariate Singular Spectrum Analysis," Jurnal Sistem Informasi Bisnis, vol. 12, no. 1, pp. 17-25, 2022.
R. J. Abreau, R. M. Souze and J. G. Oliveira, "Applying Singular Spectrum Analysis and ARIMA-GARCH for Forecasting EUR/USD Exchange Rate," Revista de Administração Mackenzie, vol. 20, no. 4, pp. 1-32, 2019.
R. B. Wiranata and A. Djunaidy, "The Stock Exchange Prediction Using Machine Learning Techniques: A Comprehensive and Systematic Literature Review," Jurnal Ilmu Komputer dan Informatika, vol. 14, no. 2, p. 91–112, 2021.
M. C. Tsai, C. H. Cheng, M. I. Tsai and H. Y. Shiu , "Forecasting Leading Industry Stock Prices Based On A Hybrid Time-Series Forecast Model," PLoS One, vol. 13, no. 12, p. 1–24, 2018.
H. R. Ogrosky, S. N. Stechmann, N. Chen and A. J. Majda, "Singular Spectrum Analysis With Conditional Predictions for Real-Time State Estimation and Forecasting," Geophysical Research Letters, vol. 46, no. 3, p. 1851–1860, 2019.
M. P. Rajakumar, R. Jegatheesan, R. Chandy and T. Sampath, "Prediction of Stock Prices Using Unstructured and Semi-Structured Qualitative Data-A Neural Network Approach," International Journal of Intelligent Engineering and Systems, vol. 12, no. 2, p. 156–169, 2019.
S. Lahmiri, "Minute-Ahead Stock Price Forecasting Based on Singular Spectrum Analysis and Support Vector Regression," Applied Mathematics and Computation 320,, vol. 320, p. 444–451, 2018.
A. L. Putra and A. K. Kurniawati, "Analisis Prediksi Harga Saham PT. Astra International Tbk Menggunakan Metode Autoregressive Integrated Moving Average (ARIMA) dan Support Vector Regression (SVR): Array.," Jurnal Ilmiah Komputasi, vol. 20, no. 3, pp. 417-424, 2021.
C. F. F. Purwoko, S. T. Saifudin and M. F. F. Mardianto, "Prediksi Harga Ekspor Non Migas di Indonesia Berdasarkan Metode Estimator Deret Fourier dan Support Vector Regression," Inferensi, vol. 6, no. 1, pp. 45-55, 2023.
B. A. Ramadhan and B. Rikumahu, "Analisis Perbandingan Metode Arima Dan Metode Garch Untuk Memprediksi Harga Saham (Studi Kasus Pada Perusahaan Telekomunikasi Yang Terdaftar Di Bursa Efek Indonesia Periode Mei 2012-April 2013)," eProceedings of Management, vol. 2, no. 1, 2015.
A. Gautam and V. Singh, "Parametric Versus Non-Parametric Time Series Forecasting Methods: A Review," Journal of Engineering Science & Technology Review, vol. 13, no. 3, 2020.
I. F. Yuliati and P. Sihombing, "Pemodelan Fertilitas Di Indonesia Tahun 2017 Menggunakan Pendekatan Regresi Nonparametrik Kernel dan Spline," Jurnal Statistika Dan Aplikasinya, vol. 4, no. 1, pp. 48-60, 2020.
N. Chamidah and B. Lestari, Analisis Regresi Nonparametrik dengan Perangkat Lunak R, Surabaya: Airlangga University Press, 2022.
M. F. F. Mardianto, N. Afifah, S. A. D. Safitri, I. Syahzaqi and S. , "Estimated Price of Shallots Commodities National Based on Parametric and Nonparametric Approaches," AIP Conference Proceedings, vol. 2329, 2021.
N. D. Maulana, B. D. Setiawan and C. Dewi, ". Implementasi Metode Support Vector Regression (SVR) Dalam Peramalan Penjualan Roti (Studi Kasus: Harum Bakery)," Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 3, no. 3, pp. 2986-2995, 2019.
Y. Ensafi, S. H. Amin, G. Zhang and B. Shah, "Time-Series Forecasting of Seasonal Items Sales Using Machine Learning-A Comparative Analysis," . International Journal of Information Management Data Insight, vol. 2, no. 1, 2022.
S. Aisyah, S. Wahyuningsih and F. Amijaya, "Peramalan Jumlah Titik Panas Provinsi Kalimantan Timur Menggunakan Metode Radial Basis Function Neural Network," Jambura Journal of Probability and Statistics, vol. 2, no. 2, pp. 64-74, 2021.
M. P. Raharyani, R. R. M. Putri and B. D. Setiawan, "Implementasi Algoritme Support Vector Regression Pada Prediksi Jumlah Pengunjung Pariwisata," Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 4, pp. 1501-1509, 2018.
J. J. M. Moreno, A. P. Pol, A. S. Abad and B. C. Blasco, "Using The R-MAPE Index As A Resistant Measure of Forecast Accuracy," Psicothema, vol. 25, no. 4, pp. 500-506, 2013.
DOI: http://dx.doi.org/10.12962%2Fj27213862.v8i2.21737
Refbacks
- There are currently no refbacks.
Inferensi by Department of Statistics ITS is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://iptek.its.ac.id/index.php/inferensi.
ISSN: 0216-308X
e-ISSN: 2721-3862
View My Stats