  The capital market plays a vital role in marketing operations for aerospace
industry. However, due to the uncertainty and complexity of the stock market
and many cyclical factors, the stock prices of listed aerospace companies
fluctuate significantly. This makes the share price prediction challengeable.
To improve the prediction of share price for aerospace industry sector and well
understand the impact of various indicators on stock prices, we provided a
hybrid prediction model by the combination of Principal Component Analysis
(PCA) and Recurrent Neural Networks. We investigated two types of aerospace
industries (manufacturer and operator). The experimental results show that PCA
could improve both accuracy and efficiency of prediction. Various factors could
influence the performance of prediction models, such as finance data, extracted
features, optimisation algorithms, and parameters of the prediction model. The
selection of features may depend on the stability of historical data: technical
features could be the first option when the share price is stable, whereas
fundamental features could be better when the share price has high fluctuation.
The delays of RNN also depend on the stability of historical data for different
types of companies. It would be more accurate through using short-term
historical data for aerospace manufacturers, whereas using long-term historical
data for aerospace operating airlines. The developed model could be an
intelligent agent in an automatic stock prediction system, with which, the
financial industry could make a prompt decision for their economic strategies
and business activities in terms of predicted future share price, thus
improving the return on investment. Currently, COVID-19 severely influences
aerospace industries. The developed approach can be used to predict the share
price of aerospace industries at post COVID-19 time.
