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The capital market plays a vital role in marketing operations for aerospaceindustry. However, due to the uncertainty and complexity of the stock marketand many cyclical factors, the stock prices of listed aerospace companiesfluctuate significantly. This makes the share price prediction challengeable.To improve the prediction of share price for aerospace industry sector and wellunderstand the impact of various indicators on stock prices, we provided ahybrid prediction model by the combination of Principal Component Analysis(PCA) and Recurrent Neural Networks. We investigated two types of aerospaceindustries (manufacturer and operator). The experimental results show that PCAcould improve both accuracy and efficiency of prediction. Various factors couldinfluence the performance of prediction models, such as finance data, extractedfeatures, optimisation algorithms, and parameters of the prediction model. Theselection of features may depend on the stability of historical data: technicalfeatures could be the first option when the share price is stable, whereasfundamental features could be better when the share price has high fluctuation.The delays of RNN also depend on the stability of historical data for differenttypes of companies. It would be more accurate through using short-termhistorical data for aerospace manufacturers, whereas using long-term historicaldata for aerospace operating airlines. The developed model could be anintelligent agent in an automatic stock prediction system, with which, thefinancial industry could make a prompt decision for their economic strategiesand business activities in terms of predicted future share price, thusimproving the return on investment. Currently, COVID-19 severely influencesaerospace industries. The developed approach can be used to predict the shareprice of aerospace industries at post COVID-19 time.

Share Price Prediction of Aerospace Relevant Companies with Recurrent Neural Networks based on PCA