Prediction of LQ45 Index in Indonesia Stock Exchange: A Comparative Study of Machine Learning Techniques

Abstract

LQ45 is an Indonesia Stock Exchange Index (ISX) incorporate of 45 companies that meet certain criteria to target investors for selecting certain stocks. The prediction of stock price direction in the financial world is a issue. The implementation of machine learning and other algorithms for market price analysis and forecasting is a very promising field. Different types of classification algorithms were used to predict the stock market. However, individual studies are considered separately there is no clear consensus that algorithms work best. In this research, a comparison framework is proposed, which aims to benchmark the performance of a wide range of classification models and use them to predict the LQ45 index. The data in this research contains the transaction level and capitalization size are obtained from the Indonesian Stock Exchange (ISX). For analysis purposes, we set out 10 classifiers that can be used to build classification models and test their performance in the LQ45 dataset. The performance criterion chosen to measure this effect is accuracy, recall, and precision. The results showed that the random forest algorithm had the best performance for predicting the LQ45 index. Whilst the classification and regression trees, C4.5, support vector machine, and logistic regression algorithms also perform well. Besides, the models based on traditional statisticalbased learners that are Naïve Bayes and linear discriminant analysis seem to underperform for predicting the LQ45 index. These results are not only beneficial to enrichment the machine learning techniques literature but also have a significant influence on the stock market prediction in terms of the ability to predict the LQ45 index.