Stock Price Forecasting on Telecommunication Sector Companies in Indonesia Stock Exchange Using Machine Learning Algorithms

2020 2nd International Conference on Cybernetics and Intelligent System (ICORIS) Pub Date : 2020-10-27 DOI:10.1109/ICORIS50180.2020.9320758

Jimmy Moedjahedy, Reymon Rotikan, Wien Fitrian Roshandi, J. Y. Mambu

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引用次数: 3

Abstract

Stock investment is a demand-driven and demanding monetary practice. Hence, the study of stock forecasts or, more precisely, the forecasting of stock prices, plays an essential role in the stock market. Mistakes in forecasting share prices have a significant impact on global finance; thus, they require an effective method of predicting changes in share prices. Machine learning is one of the methods that can be used to predict the stock price. To predict the stock price of five companies in the telecommunications sector, Bakrie Telecom Tbk (BTEL), PT. XL Axiata Tbk (EXCL), PT. Smartfren Telecom Tbk (FREN), PT. Telekomunikasi Indonesia Tbl (TLKM), and PT. Indosat Tbk (ISAT), two algorithms are used to predict the stock prices, which are the Gaussian Process and SMOreg and train dataset from January 1, 2017, to December 31, 2019. The result of this study is SMOreg has the best result than the Gaussian Process with an RMSE value of 0.00005, MAPE 1.88%, and MBE 0.00025.

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利用机器学习算法预测印尼证券交易所电信行业公司股价

股票投资是一种需求驱动和苛刻的货币行为。因此，股票预测的研究，或者更准确地说，股票价格的预测，在股票市场中起着至关重要的作用。预测股价的失误会对全球金融产生重大影响;因此，他们需要一种有效的方法来预测股价的变化。机器学习是可以用来预测股票价格的方法之一。为了预测Bakrie Telecom Tbk (BTEL)、PT. XL Axiata Tbk (EXCL)、PT. Smartfren Telecom Tbk (FREN)、PT. Telekomunikasi Indonesia Tbl (TLKM)和PT. Indosat Tbk (ISAT)这五家电信行业公司的股价，我们使用了两种算法来预测股价，即2017年1月1日至2019年12月31日的高斯过程和SMOreg和训练数据集。本研究结果表明，SMOreg比高斯过程效果最好，RMSE值为0.00005,MAPE为1.88%，MBE为0.00025。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2020 2nd International Conference on Cybernetics and Intelligent System (ICORIS)

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