Comparing Different Data Types for Predicting the Apple Stock

Abstract

Stock markets are at the heart of market economies, and stock market prediction is a hot topic in research. There are many methods of prediction, and they often use different types of data. The two main types are technical and fundamental data. This paper explores company-specific prediction through predicting the direction of the Apple stock. Three neural networks that use different input data are compared. The Tech model uses technical data, the Comp model uses numerical fundamental data with an emphasis on company data, and the Text model uses textual news data. Several metrics, including ones useful for preventing bias when dealing with imbalanced data, were used to compare the models. The Comp and Text models showed prediction bias, and two more models, W-Comp and W-Text, were trained to attempt to mitigate that bias. The Comp model performed the best out of the original models, and W-Comp also showed good performance. W-Text performed the best out of all the models. This suggests that numerical fundamental data is useful in predicting the market, and textual data also has potential. Future research could be used to improve the performance of all the models and more thoroughly compare the types of data used.