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STOCK PRICE MOVEMENT PREDICTION USING SUPERVISED MACHINE LEARNING ALGORITHM: KNIME | Anggraeni | Jurnal Akun Nabelo: Jurnal Akuntansi Netral, Akuntabel, Objektif

STOCK PRICE MOVEMENT PREDICTION USING SUPERVISED MACHINE LEARNING ALGORITHM: KNIME

Dina Anggraeni, Kris Sugiyanto, Muhammad Irwan Zam Zam, Harry Patria

Abstract


What happened to the money that was invested? Especially in light of the obscene wealth. Obviously, it refers to financial instruments. Let us start with the most basic investment instruments: firm stocks traded on stock exchanges. We have gathered field data from a few manufacturing companies over the last five years (2015–2019), including financial and non-financial data, as well as stock price variance over each year. We used KNIME's machine learning to find the best models for predicting the variance of the stock price in a given year based on the parameters provided. We expect the algorithms to be able to predict which company will return a capital gain for the investor.


Keywords


KNIME, Earning Per Share, Customer Focus, Sustainability, R&D Intensity, Corporate Governance, Organizational Culture, Asset Size, Stock Price, Decision Tree, Naïve Bayes, SVM, Random Forest, and Tree Ensemble

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References


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