Capital market behavior and stock forecasting – a neural network approach to Lufthansa’s shares

The study deals with the development and prediction of stock prices of a selected European airline, Deutsche Lufthansa Group, in the context of its presence on capital markets between 2004 and 2024.The main objective is to analyse the time series of historical daily stock prices of Deutsche Lufthansa Group and use this data to forecast price development in the near future using the method of artificial neural networks in the Statistica 14 software.The study further identifies the key external and internal factors influencing the volatility of the companys stock, as financial crisis, political unrest, escalation of the European debt crisis, government strikes, acquired parts of other companies, increasing competition from low-cost carriers, growing pressure to reduce CO2 emissions etc.The results show that predictive models based on multiplayer perceptron networks can effectively predict short term development of stock prices in the dynamic environment of capital markets.The results revealed that the prediction for Lufthansas share prices show a slightly upward and stable trend.This trend reflects the positive expectation and stabilization of costs, which may indicate renewed investor confidence in the companys long-term recovery.The findings offer a valuable insight for investors, analysts, and the academic community and can serve as a foundation for further research on stock price prediction in the aviation industry.