  Application of machine learning for stock prediction is attracting a lot of
attention in recent years. A large amount of research has been conducted in
this area and multiple existing results have shown that machine learning
methods could be successfully used toward stock predicting using stocks
historical data. Most of these existing approaches have focused on short term
prediction using stocks historical price and technical indicators. In this
paper, we prepared 22 years worth of stock quarterly financial data and
investigated three machine learning algorithms: Feed-forward Neural Network
(FNN), Random Forest (RF) and Adaptive Neural Fuzzy Inference System (ANFIS)
for stock prediction based on fundamental analysis. In addition, we applied RF
based feature selection and bootstrap aggregation in order to improve model
performance and aggregate predictions from different models. Our results show
that RF model achieves the best prediction results, and feature selection is
able to improve test performance of FNN and ANFIS. Moreover, the aggregated
model outperforms all baseline models as well as the benchmark DJIA index by an
acceptable margin for the test period. Our findings demonstrate that machine
learning models could be used to aid fundamental analysts with decision-making
regarding stock investment.
