STOCK PRICE FORECASTING BY USING FUZZY INFERENCE SYSTEM
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Institusion
Universitas Atma Jaya Yogyakarta
Author
Trisetiawanto, Kintakaloka
Subject
International Financial Management
Datestamp
2013-05-03 13:08:42
Abstract :
This research is to establish a Fuzzy Inference System forecasting model to help the decision making process. In this research, the decision making is about decision making about stocks with the used of the historical data. The data used in this research are secondary data that consist of daily stock price fluctuation, daily closing price, daily trade volume, Price Earning Ratio and Dividend per Share and the name of companies listed in the LQ45 in the period of January 1st 2000 to December 31st 2010. The total sample is one company. For testing the model is performed by compare the real stock price fluctuation and forecasted stock price fluctuation. Every model has its limitation, called as an error. In this research, error is a differences between the real fluctuation compared with forecasting stock price fluctuation. In this research, to know whether the model is applicable or not, errors indexes used. Many errors indexes such as Mean Absolute Deviation, Mean Square Error, Mean Absolute Percentage Error, and Mean Percentage Error.
This research is to establish a Fuzzy Inference System forecasting model to help the decision making process. In this research, the decision making is about decision making about stocks with the used of the historical data. The data used in this research are secondary data that consist of daily stock price fluctuation, daily closing price, daily trade volume, Price Earning Ratio and Dividend per Share and the name of companies listed in the LQ45 in the period of January 1st 2000 to December 31st 2010. The total sample is one company. For testing the model is performed by compare the real stock price fluctuation and forecasted stock price fluctuation. Every model has its limitation, called as an error. In this research, error is a differences between the real fluctuation compared with forecasting stock price fluctuation. In this research, to know whether the model is applicable or not, errors indexes used. Many errors indexes such as Mean Absolute Deviation, Mean Square Error, Mean Absolute Percentage Error, and Mean Percentage Error.
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