The capital market plays a vital role in marketing operations for the rapid development of the 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 (RNN).

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. The study is for the prediction of aerospace industries at pre-COVID-19 time. 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.

资本市场在航空航天业快速发展的营销运作中发挥着至关重要的作用。但由于股市的不确定性和复杂性以及诸多周期性因素，航空航天上市公司股价波动较大。这使得股价预测具有挑战性。为了提高对航空航天行业股价的预测，更好地了解各种指标对股价的影响，我们提供了主成分分析（PCA）和循环神经网络（RNN）相结合的混合预测模型。

我们调查了两种类型的航空航天工业（制造商和运营商）。实验结果表明，PCA可以提高预测的准确性和效率。各种因素都会影响预测模型的性能，例如财务数据、提取的特征、优化算法和预测模型的参数。特征的选择可能取决于历史数据的稳定性：当股价稳定时，技术特征可能是首选，而当股价波动较大时，基本特征可能更好。RNN 的延迟还取决于不同类型公司历史数据的稳定性。通过使用航空航天制造商的短期历史数据会更准确，

所开发的模型可以作为股票自动预测系统中的智能代理，金融行业可以通过预测未来股价，迅速做出经济战略和业务活动的决策，从而提高投资回报率。该研究用于预测 COVID-19 之前的航空航天工业。目前，COVID-19 严重影响了航空航天业。开发的方法可用于预测 COVID-19 后航空航天业的股价。