STOCK PRICE MOVEMENT PREDICTION USING SUPERVISED MACHINE LEARNING ALGORITHM: KNIME

Dina Anggraeni (Universitas Indonesia)
Kris Sugiyanto (Universitas Indonesia)
Muhammad Irwan Zam Zam (Universitas Indonesia)
Harry Patria (Institut Teknologi Bandung)

Jurnal Akun Nabelo : Jurnal Akuntansi Netral, Akuntabel, Objektif
Vol 4, No 2 (2022)

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.