Implementation of Linear Regression Algorithm to Predict Stock Prices Based on Historical Data

Authors

    Jelvin Putra Halawa( 1 ) Aditiya Hermawan( 2 ) Junaedi .( 3 )

    (1) Universitas Buddhi Dharma
    (2) Universitas Buddhi Dharma
    (3) Universitas Buddhi Dharma

DOI:


https://doi.org/10.32877/bt.v5i2.616

Keywords:


Historical Data , Investment, Linear Regression, Prediction , Stock Price

Abstract

Stock investment is in great demand by investors because it can provide large profits with large risks or losses, in accordance with the investment principle of low risk low return, high risk high return. Stock prices that fluctuate in a very short time make it difficult for investors to predict stock prices in the future, so investors must pay more attention and gather as much information as possible regarding the shares to be bought or sold. This study aims to create a data mining model using a Linear Regression algorithm that can predict daily stock closing prices to provide information that supports investors in stock transactions. The data used is historical data on daily stock prices for 10 companies in the last 8 years for the period 25 February 2013 – 25 February 2021. Historical stock price data will be prepared using the Noving Average method and create a data mining model using the linear regression method to generate stock price prediction models. The resulting model can be used to predict stock prices well enough to assist investors in making investment decisions to obtain large profits with low risk.

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References

kseinews, “Ksei Menjadi Kustodian Sentral Terbaik Di Asia Tenggara Ketiga Kalinya,” Edisi 01, p. 10, 04 Juni 2020.

I. Fahmi, dalam Analisis Laporan Keuangan, Bandung, Alfabeta, 2014, p. 324.

M. Azis, S. Mintarti dan M. Nadir, dalam Manajemen Investasi Fundamental, Teknikal, Perilaku Investor dan Return Saham, Yogyakarta, Deepublish (Grup Penerbitan CV Budi Utama), 2015, p. 80.

R. Hidayat, “Prediksi Harga Saham Menggunakan Neural Network,” Jurnal Gema Aktualita, p. 65, 2016.

F. S. Gharehchopogh, T. H. Bonab dan S. R. Khaze, “A Linear Regression Approach To Prediction of Stock Market Trading Volume : A Case Study,” International Journal of Managing Value and Supply Chains (IJMVSC), 2013.

D. T. Larose, Discovering Knowledge in Data : An Introduction to Data Mining, New Jersey: John Wiley & Sons, Inc, 2014.

Kamal, I. M., P, T. H., & Ilyas, R. (2017). Prediksi Penjualan Buku Menggunakan Data Mining. Seminar Nasional Teknologi Informasi Dan Multimedia, 49–54.

E. S. Tataming, T. K. Sendow, O. H. Kaseke och S. Diantje, ”Analisis Besar Kontribusi Hambatan Samping Terhadap Kecepatan dengan Menggunakan Model Regresi Linier Berganda,” Jurnal Sipil Statik, pp. 29-36, 2014.

A. Nurlifa Och S. Kusumadewi, ”Sistem Peramalan Jumlah Penjualan Menggunakan Metode Moving Average Pada Rumah Jilbab Zaky,” Jurnal Inovtek Polbeng, Vol. Ii, Pp. 18-25, 2017.

H. Utari, M. Och N. Silalahi, ”Perancangan Aplikasi Peramalan Permintaan Kebutuhan Tenaga Kerja Pada Perusahaan Outsourcing Menggunakan Algoritma Simple Moving Average,” Jurnal Times, Vol. V, Pp. 1-5, 2016.

H. Budiman, ”Analisis Dan Perbandingan Akurasi Model Prediksi Rentet Waktu Support Vector Machines Dengan Support Vector Machines Particle Swarm Optimization Untuk Arus Lalu Lintas Jangka Pendek,” Systemic, Vol. %1 Av %22, No. 01, Pp. 19-24, 2016.

A. Fadholi, ”Pemanfaatan Suhu Udara Dan Kelembapan Udara Dalam Persamaan Regresi Untuk Simulasi Prediksi Total Hujan Bulanan Di Pangkalpinang,” Jurnal Cauchy, Vol. %1 Av %2iii, No. 1, Pp. 1-9, 2013.

Riduwan, Dasar-Dasar Statistika, Bandung: Alfabeta, 2010.

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Published

2022-12-14

How to Cite

[1]
J. P. Halawa, A. Hermawan, and J. ., “Implementation of Linear Regression Algorithm to Predict Stock Prices Based on Historical Data”, bit-Tech, vol. 5, no. 2, pp. 103–112, Dec. 2022.

Issue

Vol. 5 No. 2 (2022): bit-Tech

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Articles

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DOI : https://doi.org/10.32877/bt.v5i2.616
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