Stock Price Prediction methods: A case Study of Apple

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Byambajav, Ariunzul
Issue Date
MSc in Financial Analytics
Dublin Business School
Predicting stock prices is very important for finance practitioners to best allocate their assets and to build better and more accurate asset pricing models. Accurate prediction of stock market returns is a very challenging task due to volatile and non-linear nature of the financial stock market. With the introduction of Apple Inc and increased computational capabilities, programmed methods of prediction have proved to be more efficient in predicting stock prices. In this work Long short-term memory and Linear regression techniques have been utilized for predicting the next day closing price for Apple Inc belonging sectors of operation. The financial data: Open, High, Low and Close prices of stock are used for creating new variables which are used as inputs to the model. The models are evaluated using standard strategic indicator: MSE. The low value of this indicator shows that the models are efficient in predicting stock closing price.