IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v66y2025i3d10.1007_s10614-024-10810-2.html

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-024-10810-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-024-10810-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. A., Rjumohan, 2019. "Stock Markets: An Overview and A Literature Review," MPRA Paper 101855, University Library of Munich, Germany.
    2. 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.
    3. 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.
    4. 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.
    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. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Haithem Awijen & Sami Ben Jabeur & Julien Pillot, 2026. "Interpretable machine learning models for ESG stock prices under transition and physical climate risk," Annals of Operations Research, Springer, vol. 357(1), pages 191-221, February.
    2. Ylinen, Mika & Ranta, Mikko, 2025. "Predicting corporate innovation using machine learning and social media data," Technovation, Elsevier, vol. 148(C).
    3. Shuangshuang Fan & Yichao Li & William Mbanyele & Xiufeng Lai, 2025. "Determinants and Pathways for Inclusive Growth in China: Investigation Based on Artificial Intelligence (AI) Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1231-1264, March.
    4. Bakalli, Gaetan & Guerrier, Stéphane & Scaillet, Olivier, 2023. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Journal of Econometrics, Elsevier, vol. 237(2).
    5. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    6. Tobias Götze & Marc Gürtler & Eileen Witowski, 2020. "Improving CAT bond pricing models via machine learning," Journal of Asset Management, Palgrave Macmillan, vol. 21(5), pages 428-446, September.
    7. Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, vol. 76(C).
    8. Gao, Daquan & Li, Songsong & Tian, Zhihong, 2025. "Geopolitical risk, energy market volatility, and corporate energy dependence: The role of green Total factor productivity and decentralized top management team network," Energy Economics, Elsevier, vol. 148(C).
    9. Ahmed Bouteska & Murad Harasheh, 2026. "Decoding the crypto crowd: how social media sentiment predicts Ethereum’s price," Journal of Asset Management, Palgrave Macmillan, vol. 27(1), pages 1-12, March.
    10. Kun Wang & Bozhou Li & Lu Qiao & Zhiyi Xia, 2026. "Fintech and Dividend Payouts: Evidence From China," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 31(2), pages 2964-2979, April.
    11. Daníelsson, Jón & Macrae, Robert & Uthemann, Andreas, 2022. "Artificial intelligence and systemic risk," Journal of Banking & Finance, Elsevier, vol. 140(C).
    12. Cong Wang, 2024. "Stock return prediction with multiple measures using neural network models," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-34, December.
    13. Liu, Yunting & Zhu, Yandi, 2025. "Good idiosyncratic volatility, bad idiosyncratic volatility, and the cross-section of stock returns," Journal of Banking & Finance, Elsevier, vol. 170(C).
    14. Guo, Li & Sang, Bo & Tu, Jun & Wang, Yu, 2024. "Cross-cryptocurrency return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 163(C).
    15. Cao, Sean & Jiang, Wei & Wang, Junbo & Yang, Baozhong, 2024. "From Man vs. Machine to Man + Machine: The art and AI of stock analyses," Journal of Financial Economics, Elsevier, vol. 160(C).
    16. Rad, Hossein & Low, Rand Kwong Yew & Miffre, Joëlle & Faff, Robert, 2023. "The commodity risk premium and neural networks," Journal of Empirical Finance, Elsevier, vol. 74(C).
    17. Chen, Andrew Y. & McCoy, Jack, 2024. "Missing values handling for machine learning portfolios," Journal of Financial Economics, Elsevier, vol. 155(C).
    18. repec:cam:camjip:2506 is not listed on IDEAS
    19. Alejandro Rodriguez Dominguez, 2025. "Is Causality Necessary for Efficient Portfolios? A Computational Perspective on Predictive Validity and Model Misspecification," Papers 2507.23138, arXiv.org, revised Feb 2026.
    20. Shunyao Wang & Ming Cheng & Christina Dan Wang, 2025. "NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networks," Papers 2505.06864, arXiv.org.
    21. Christian Fieberg & Daniel Metko & Thorsten Poddig & Thomas Loy, 2023. "Machine learning techniques for cross-sectional equity returns’ prediction," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 289-323, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:66:y:2025:i:3:d:10.1007_s10614-024-10810-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.