Application of Conventional Modeling and Deep Learning to Stock Data with Outliers

Abstract

Apple Inc. stock (AAPL), one of the leading technology companies, is one of the concerns of investors as it continues to see an increase in the number of users every year. Therefore, forecasting Apple's stock price is important to help investors mitigate risks and optimize investment decisions. This forecasting can be done using two main approaches, namely conventional approaches such as Autoregressive Integrated Moving Average (ARIMA) and deep learning-based approaches such as Long Short-term Memory Network (LSTM). This study aims to find the best model using both methods, as well as compare the accuracy of the models based on datasets with outliers and datasets with handled outliers. The dataset analyzed in this study comes from weekly AAPL stock closing price data for 500 periods, from January 26, 2015 to August 19, 2024 obtained from Yahoo Finance. This study obtained the ARIMA(1,1,1) model as the best model for both datasets, with the outlier-handled dataset producing better test MAPE, while the dataset with outliers had better training MAPE. The LSTM method produced smaller MAPE values than ARIMA, demonstrating its superiority in capturing the fluctuating patterns of the AAPL stock data. Outlier handling was shown to improve model accuracy, as seen in the outlier-handled dataset. This research provides insight into the effectiveness of statistical and deep learning methods in modeling stock prices, and emphasizes the importance of outlier handling in financial data analysis.