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

Author

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  • Ibrahim Dikmen

    (Istanbul Technical University)

  • Kaya Tokmakcioglu

    (Istanbul Technical University)

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.

Suggested Citation

  • Ibrahim Dikmen & Kaya Tokmakcioglu, 2025. "Comparative Analysis of Turkish and German Stock-Markets as a Hedge Product Against Inflation by Using Machine Learning Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 66(3), pages 2593-2618, September.
    • Handle: RePEc:kap:compec:v:66:y:2025:i:3:d:10.1007_s10614-024-10810-2
    DOI: 10.1007/s10614-024-10810-2
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    References listed on IDEAS

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    1. A., Rjumohan, 2019. "Stock Markets: An Overview and A Literature Review," MPRA Paper 101855, University Library of Munich, Germany.
    2. Deeksha Chandola & Akshit Mehta & Shikha Singh & Vinay Anand Tikkiwal & Himanshu Agrawal, 2023. "Forecasting Directional Movement of Stock Prices using Deep Learning," Annals of Data Science, Springer, vol. 10(5), pages 1361-1378, October.
    3. Vivek Arora & Athanasios Vamvakidis, 2004. "The Impact of U.S. Economic Growth on the Rest of the World: How Much Does It Matter?," Journal of Economic Integration, Center for Economic Integration, Sejong University, vol. 19, pages 1-18.
    4. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    5. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    6. Firth, Michael, 1979. "The Relationship between Stock Market Returns and Rates of Inflation," Journal of Finance, American Finance Association, vol. 34(3), pages 743-749, June.
    7. Collin Chikwira & Jahed Iqbal Mohammed, 2023. "The Impact of the Stock Market on Liquidity and Economic Growth: Evidence of Volatile Market," Economies, MDPI, vol. 11(6), pages 1-19, May.
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