Comparing neural network with linear Regression for stock market prediction


  • 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



neural network, linear regression, stock market, prediction


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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How to Cite

kurniawan, F., Arif, Y. M. ., Nugroho, F. ., & Ikhlayel, M. (2023). Comparing neural network with linear Regression for stock market prediction. Bulletin of Social Informatics Theory and Application, 7(1), 8–13.