Date of Award
Master of Science in Computer Science
School of Computer Science and Engineering
First Reader/Committee Chair
Numerous neural network models have been created to predict the rise or fall of stocks since deep learning has gained popularity, and many of them have performed quite well. However, since the share market is hugely influenced by various policy changes or unexpected news, it is challenging for investors to use such short-term predictions as a guide. In this paper, we try to find a suitable long-term predictor for the funds market by testing different kinds of neural network models, including the Long Short-Term Memory(LSTM) model with different layers, the Gated Recurrent Units(GRU) model with different layers, and the combination model of LSTM and GRU. These models were evaluated on two funds datasets with various stock market technical indicators added. Since the fund is a long-term investment, we attempted to predict the range of change in the future 20 trading days. The experimental results demonstrated that the single GRU model performed best, reached an accuracy of 92.14% to correctly predict the direction of rise or fall, and the accuracy of predicting the specific change also hit 85.35%.
KUANG, SHUIYI, "A LONG-TERM FUNDS PREDICTOR BASED ON DEEP LEARNING" (2023). Electronic Theses, Projects, and Dissertations. 1751.