STOCK PRICE MOVEMENT PREDICTION USING SUPERVISED MACHINE LEARNING ALGORITHM: The Konstanz Information Miner (KNIME)

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 30 manufacturing companies over the last five years (2015–2019), including financial and non-financial data, as well as stock price variance over each year. The objective of our research is to find the best models for predicting the movement of the stock price in a given year based on the parameters provided using KNIME. The finding in our research is that Support Vector Machine (SVM) with 90.7% perform better than Naïve Bayes with 80% followed by Decision Tree, Tree Ensambles, and Random Forest with 75%, 74%, and 74%, respectively, in terms of classification accuracy. From our research, we expect the algorithms to be able to predict which company will return a capital gain for the investor.