Forecasting Stock Price by Using Artificial Neural Networks

Nur’azra Alia Nisa, Zulpakar (2023) Forecasting Stock Price by Using Artificial Neural Networks. [Final Year Project Report] (Unpublished)

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Abstract

Machine learning is widely used in predicting the stock prices. A stock market trade is an activity that requires investors to obtain accurate and timely information in order to make informed decisions. Due to the large number of stocks that are traded on a stock exchange, a variety of factors are considered in the decision-making process. In addition, it is also difficult to predict the behaviour of stock prices due to the uncertainty associated with them. There have been a number of studies conducted on the topic of forecasting stock values using machine learning. Hence, in this study, an Artificial Neural Network model is proposed as a machine learning algorithm for forecasting stock prices. This study utilizes the daily stock prices of Apple Inc. and Microsoft Corp gathered from the NASDAQ stock exchange. The processed data are then evaluated using the Root Mean Square Error (RMSE) and Absolute Error to analyse the performance of the model proposed.

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