Comparing neural network with linear Regression for stock market prediction

Fachrul Kurniawan; Yunifa Miftachul  Arif; Fresy  Nugroho; Mohammed Ikhlayel

doi:10.31763/businta.v7i1.621

Authors

Fachrul Kurniawan University Maulana Malik Ibrahim Malang
Yunifa Miftachul Arif University Maulana Malik Ibrahim Malang
Fresy Nugroho University Maulana Malik Ibrahim Malang
Mohammed Ikhlayel Al-Quds Open University

DOI:

https://doi.org/10.31763/businta.v7i1.621

Keywords:

neural network, linear regression, stock market, prediction

Abstract

There are both gains and losses possible in stock market investing. Brokerage firms' stock investments carry a higher risk of loss since their stock prices are not being tracked or analyzed, which might be problematic for businesses seeking investors or individuals. Thanks to progress in information and communication technologies, investors may now easily collect and analyze stock market data to determine whether to buy or sell. Implementing machine learning algorithms in data mining to obtain information close to the truth from the desired objective will make it easier for an individual or group of investors to make stock trades. In this study, we test hypotheses on the performance of a financial services firm's stock using various machine learning and regression techniques. The relative error for the neural network method is only 0.72 percentage points, while it is 0.78 percentage points for the Linear Regression. More training cycles must be applied to the Algortima neural network to achieve more accurate results.

Author Biographies

Yunifa Miftachul Arif, University Maulana Malik Ibrahim Malang

Fresy Nugroho, University Maulana Malik Ibrahim Malang

Mohammed Ikhlayel, Al-Quds Open University

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