Comparative Analysis of Turkish and German Stock-Markets as a Hedge Product Against Inflation by Using Machine Learning Algorithms

Ibrahim Dikmen () and Kaya Tokmakcioglu ()
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Ibrahim Dikmen: Istanbul Technical University
Kaya Tokmakcioglu: Istanbul Technical University

Computational Economics, 2025, vol. 66, issue 3, No 29, 2593-2618

Abstract: Abstract Stock markets are places where investors meet the companies who seek capital to expand their business. One of the aims of investors is to hedge their capital against inflation and valuation of reserve currency, US Dollar. In this study, we apply various machine learning algorithms to predict hedge-ability of Turkiye’s and Germany’s stock markets. For this purpose, the automotive industry became subject for both countries. The outcomes of the study indicate machine learning algorithms are promising for prediction. However, they lack consistency and precision. Yet, it is possible to distinguish certain algorithms and try to improve performances with additional inputs, parameter tuning and more data. The algorithms should be combined with other methods for better results. It might also be beneficial if the study is supported by the views of sector experts.

Keywords: Stock-market; Inflation; Exchange rate; Machine learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10614-024-10810-2

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