Received:
2025-06-18 | Accepted:
2025-09-26 | Published:
2025-12-30
Title
Capital market behavior and stock forecasting – a neural network approach to Lufthansa’s shares
Abstract
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 company´s 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 Lufthansa´s 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 company´s 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.
Keywords
stocks, capital market, historical prices, airline industry, Artificial Neural Networks (ANNs), Artificial Intelligence (AI), investment
JEL classifications
C45
, G11
, G15
, L93
URI
http://jssidoi.org/jesi/article/1370
DOI
Pages
222-237
Funding
This research was funded by Specific University Research under Institute of Technology and Business in České Budějovice, grant number 05SVV2306
This is an open access issue and all published articles are licensed under a
Creative Commons Attribution 4.0 International License
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