Stock Price Index Prediction Using Adaptive Neural Fuzzy Inference System

Document Type : Original Research

Authors

1 Accounting Department, Business and Economic Faculty, Bina Bangsa University, Serang, Indonesia

2 Industrial Engineering Departmenet, Bina Bangsa University, Serang, Indonesia

3 Management Department, Bina Bangsa University, Serang, Indonesia

Abstract

This paper aims to predict stock prices using open, high, low, close variables using artificial neural networks, especially the adaptive fuzzy neural inference system (ANFIS). Each stock has a different pattern and can be predicted if you have complete data. This study is limited by stock data for 2012-2019. The survey was conducted to collect stock data from the Yahoo Finance website. The stock data used is data from 2001-2018. Learning patterns of data patterns using the Adaptive Neural Fuzzy Inference System (ANFIS) were compared with regression analysis, Mean Square Error (MSE) and Mean Prediction Error. The results show that stock price predictions using the Adaptive Neural Fuzzy Inference System (ANFIS) have a small error rate (below 1 percent). The stock price at closing is determined by the open price and the volume of the stock. The value of the highest price of the stock and the lowest value of the stock follows the determined value of the opening price. This paper contributes to existing research in economics, especially stock investment and Financial Technology.

Keywords

Main Subjects


COPYRIGHTS

©2021 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.

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