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Stock Price Index Prediction Using Adaptive Neural Fuzzy Inference System

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  • Rina Nopianti
  • Andreas Panudju
  • Angrian Permana

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.

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Handle: RePEc:air:journl:v:8:y:2021:i:10:p:715
DOI: 10.5281/zenodo.5907662
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