Stock Price Forecasting for AmazonE-281, Indiana University SoutheastEricka StumlerApril 17, 2018AbstractStock price forecasting is a popular and important topic in financial and educational studies. Time series analysis is the most common and fundamental method used to predict future stock prices to provide adequate guidelines for investors. The data I use in this paper is the daily stock price of Amazon (AMZN) from October 19, 2017, to April 19, 2018, which I got from Yahoo finance website. The dataset contains open, high, low, close, and adjusted close prices of AMZN stock each day of the past 6 months. I chose this stock mainly because it is extremely popular right now and there is a large amount of information on the internet about Amazon. Stock price forecasting is a popular and important topic in financial and educational studies. Time series analysis is the most common and fundamental method used to performthis task. This paper aims to combine the conventional time series analysis technique with information from the Google trend website and the Yahoo finance website to predict weeklychanges in stock price. Important news/events related to a selected stock over a five-year span are recorded and the weekly Google trend index values on this stock are used to provide a measure of the magnitude of these events. The result of this experiment shows significant correlation between the changes in weekly stock prices and the values of important news/events computed from the Google trend website. The algorithm proposed in this paper can potentially outperform the conventional time series analysis in stock priceforecasting.
I.IntroductionThe objective of this study is to construct the proper model for AMZN dataset.