Journals / Ekonomi İşletme ve Maliye Araştırmaları Dergisi / 2026 / Cilt: 7 Sayı: 2

A Financial Analysis on Chinese Steel Markets and the German Automobile Industry

Çin Çelik Piyasaları ve Alman Otomobil Endüstrisi Üzerine Finansal Bir Analiz

Olcay Ölçen(İSTANBUL GELİŞİM ÜNİVERSİTESİ)0000-0002-4835-1171
Pages
268–285
Publication Date
2025-08-30
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This research addresses the research gap regarding the financial relationship between the stock returns of automotive giants and the dominant market returns of their major complements.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:International relations, supply chains, and marketing can have direct impacts on today's automotive industry.
📊
Key Findings & Data:It is interesting to note that the economic movements of steel, the industry's main commodity, are statistically insignificant for two automotive giants and insignificant for one.
💡
Conclusion & Impact:This research addresses the research gap regarding the financial relationship between the stock returns of automotive giants and the dominant market returns of their major complements.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Alman Otomobil Endüstrisi · Çin Çelik Piyasası · Markov Switching Regresyon

Abstract

International relations, supply chains, and marketing can have direct impacts on today's automotive industry. Therefore, market participants must monitor and evaluate complementary industrial structures and commodities such as Brent crude oil, metals, and the import and export characteristics of these commodities. This article analyses the financial returns of three automotive giants on the major exchanges and the returns of Chinese steel futures as a commodity. It is interesting to note that the economic movements of steel, the industry's main commodity, are statistically insignificant for two automotive giants and insignificant for one. The analysis utilises the Markov Regime Switching Model. These models are used to provide additional insight into data relationships under certain conditions, such as non-stationarity, heteroskedasticity, and structural change, by dividing the data into regimes. This research addresses the research gap regarding the financial relationship between the stock returns of automotive giants and the dominant market returns of their major complements.

Özet

Uluslararası ilişkiler, tedarik zincirleri ve pazarlama, bugün otomobil endüstrisi üzerinde doğrudan etkilere sahip olabilir. Bu nedenle, piyasa katılımcıları Brent petrol, metaller ve bu emtiaların ithalat ve ihracat özellikleri gibi tamamlayıcı endüstriyel yapıları ve emtiaları izlemeli ve değerlendirmelidir. Bu makale, üç otomobil devinin ana borsadaki finansal getirilerini ve bir emtia olarak Çin çelik vadeli işlemlerinin getirilerini analiz etmeyi amaçlamaktadır. Endüstrinin ana emtiası olan çeliğin ekonomik hareketlerinin istatistiksel olarak iki otomobil deviyle önemsiz ve birisiyle ilişkili olması ilginçtir. Analiz bölümünde, Markov Rejim Değiştirme Modeli kullanılmıştır. Bu modeller, verileri rejimlere bölerek durağan olmama, heteroskedastisite ve yapısal değişim gibi belirli koşullarda veri ilişkileri için ek bilgi sağlamak için kullanılır. Bu araştırmanın yapılmasıyla, otomobil devlerinin hisse senedi getirileri ile ana tamamlayıcılarının baskın piyasa getirileri arasındaki finansal ilişki hakkındaki araştırma boşluğu doldurulmaktadır.

🔖

Cite This Article

Ölçen, O. (2025). A Financial Analysis on Chinese Steel Markets and the German Automobile Industry. Ekonomi İşletme ve Maliye Araştırmaları Dergisi, Cilt: 7 Sayı: 2, 268–285. https://doi.org/10.38009/ekimad.1733639

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (63)

  1. Ailliot, P., & Monbet, V. (2012). Markov-switching autoregressive models for wind time series. Environmental Modelling & Software, 30, 92-101. https://doi.org/10.1016/j.envsoft.2011.10.011
  2. Arık, E., & Mutlu, E. (2014). Chinese steel market in the post-futures period. Resources Policy, 42, 10-17. https://doi.org/10.1016/j.resourpol.2014.08.002
  3. Asadi, M., Tiwari, A. K., Gholami, S., Ghasemi, H. R., & Roubaud, D. (2023a). Understanding interconnections among steel, coal, iron ore, and financial assets in the US and China using an advanced methodology. International Review of Financial Analysis, 89, 102789. https://doi.org/10.1016/j.irfa.2023.102789
  4. Asadi, M., Balcilar, M., Sheikh, U. A., Roubaud, D., & Ghasemi, H. R. (2023b). Are there inextricable connections among automobile stocks, crude oil, steel, and the US dollar?. Energy Economics, 128, 107176. https://doi.org/10.1016/j.eneco.2023.107176
  5. Arumugam, D., Kumar, A., & Preetha, R. (2016). Factors Determining profitability in the Indian automobile industry. Indian Journal Of Commerce And Management Studies, 7(2), 64-69. https://www.ijcms.in/index.php/ijcms/article/view/342
  6. Birat, J. P. (2020). Society, materials, and the environment: The case of steel. Metals, 10(3), 331. https://doi.org/10.3390/met10030331
  7. Boopathi, S. (2024). Implementation of green manufacturing practices in the automobile field: A review. Sustainable Machining and Green Manufacturing, 221-248. https://doi.org/10.1002/9781394197866.ch11
  8. Calabrese, G. G., Falavigna, G., & Ippoliti, R. O. B. E. R. T. O. (2024). Innovation policy and corporate finance: the Italian automotive supply chain and its transition to Industry 4.0. Journal of Policy Modelling, 46(2), 336-353. https://doi.org/10.1016/j.jpolmod.2024.01.007
  9. Durling, J. P., & Prusa, T. J. (2006). The trade effects associated with an antidumping epidemic: The hot-rolled steel market, 1996–2001. European Journal of Political Economy, 22(3), 675-695. https://doi.org/10.1016/j.ejpoleco.2005.08.006
  10. De Castro Matias, M., & Tabak, B. M. (2025). Comparison of indicator saturation and Markov regime-switching models for Brazilian electricity prices. Energy Economics, 108341. https://doi.org/10.1016/j.eneco.2025.108341
  11. Do Carmo, M., Neto, M. S., & Donadone, J. C. (2019). Financialization in the automotive industry: shareholders, managers, and salaries. Journal of Economic Issues, 53(3), 841-862. https://doi.org/10.1080/00213624.2019.1646609
  12. Evci, S., Sak, N., & Karaagaç, G. A. (2016). Altin Fiyatlarindaki Degisimin Markov Rejim Degisim Modelleriyle Incelenmesi/Analysis of Volatility in Gold Prices with the Markov Regime-Switching Models. Business and Economics Research Journal, 7(4), 67. DOI: 10.20409/berj.2016422339
  13. Falát, L., & Holubčík, M. (2017). The influence of marketing communication on the financial situation of the company–a case from the automobile industry. Procedia Engineering, 192, 148-153. https://doi.org/10.1016/j.proeng.2017.06.026
  14. Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56. https://doi.org/10.1016/0304-405X(93)90023-5
  15. Fama, E. F., & French, K. R. (1995). Size and book‐to‐market factors in earnings and returns. The Journal Of Finance, 50(1), 131-155. https://doi.org/10.1111/j.1540-6261.1995.tb05169.x
  16. Fama, E. F., & French, K. R. (1996). The CAPM is wanted, dead or alive. The Journal of Finance, 51(5), 1947-1958. https://doi.org/10.1111/j.1540-6261.1996.tb05233.x Access time: 12.08.2025
  17. Fama, E. F., & French, K. R. (2004). The capital asset pricing model: Theory and evidence. Journal of Economic Perspectives, 18(3), 25-46. https://www.aeaweb.org/articles?id=10.1257/0895330042162430, Access time: 12.08.2025
  18. Fama, E. F., & MacBeth, J. D. (1973). Risk, return, and equilibrium: Empirical tests. Journal of Political Economy, 81(3), 607-636. https://www.jstor.org/stable/1831028, Access time: 12.08.2025
  19. Fiaz, A., Khurshid, N., Satti, A., & Malik, M. S. (2021). Real exchange rate misalignment in Pakistan: An application of the regime-switching model. The Journal of Asian Finance, Economics and Business, 8(12), 63-73. DOI: 10.13106/jafeb. 2021.vol8.no12.0063,
  20. Fraga-Lamas, P., & Fernández-Caramés, T. M. (2019). A review of blockchain technologies for an advanced and cyber-resilient automotive industry. IEEE Access, 7, 17578-17598. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8626103, Access time: 12.08.2025
  21. Largest Automakers, https://companiesmarketcap.com/automakers/largest-automakers-by-market-cap/, Access Date: 17.09.2023. Top steel producers, https://worldsteel.org/steel-topics/statistics/top-producers/, Access time: 20.09.2023.
  22. Gajdzik, B., Wolniak, R., & Grebski, W. (2024). Changes in the steel chain in Industry 4.0. Some results of the survey on the Polish steel market. Production Engineering Archives, 30. https://doi.org/10.30657/pea.2024.30.1
  23. Ghezal, A. (2023). A doubly Markov switching AR model: Some probabilistic properties and strong consistency. Journal of Mathematical Sciences, 271(1), 66-75. https://doi.org/10.1007/s10958-023-06262-y
  24. Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica: Journal Of The Econometric Society, 357-384. https://doi.org/10.2307/1912559et al.
  25. Haugh, D., Mourougane, A., & Chatal, O. (2010). The Automobile Industry in and Beyond the Crisis (No. 745). OECD Publishing. https://www.oecd.org/en/publications/the-automobile-industry-in-and-beyond-the-crisis_5kmmp8wg6cmq-en.html, Access time:12.08.2025
  26. Indriawan, I., Liu, Q., & Tse, Y. (2019). Market quality and the connectedness of steel rebar and other industrial metal futures in China. Journal of Futures Markets, 39(11), 1383-1393. https://doi.org/10.1007/s00521-024-10270-7
  27. Ionescu, R. V., Zlati, M. L., Antohi, V. M., & Dincă, M. S. (2022). Automotive Sector Financial Performance Dynamic Model: Europe vs. Asia Case Study. Mathematics, 10(19), 3627. https://doi.org/10.3390/math10193627.
  28. Jin, B., & Xu, X. (2024). Predictions of steel price indices through machine learning for the regional northeast Chinese market. Neural Computing and Applications, 36(33), 20863-20882. https://www.springerprofessional.de/en/predictions-of-steel-price-indices-through-machine-learning-for-/27483106.
  29. Kim, J., Sovacool, B. K., Bazilian, M., Griffiths, S., Lee, J., Yang, M., & Lee, J. (2022). Decarbonizing the iron and steel industry: A systematic review of sociotechnical systems, technological innovations, and policy options. Energy Research & Social Science, 89, 102565. https://doi.org/10.1016/j.erss.2022.102565
  30. Kouvelis, P., & Turcic, D. (2021). Supporting operations with financial hedging: Cash hedging vs. cost hedging in the automotive industry. Production and Operations Management, 30(3), 738-749. https://doi.org/10.1111/poms.13314
  31. Labson, B. S. (1997). Changing patterns of trade in the world iron ore and steel market: An econometric analysis. Journal of Policy Modelling, 19(3), 237-251. https://doi.org/10.1016/S0161-8938(96)00049-X
  32. Labson, S., Gooday, P., & Manson, A. (1995). China Steel. China's Emerging Steel Industry and Its Impact on the World Iron Ore and Steel Market. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2719510
  33. Li, G., Zhang, J., Wang, Y., & Zhao, Z. (2024). Can government-guided funds promote corporate technology innovation? Evidence from the new energy automobile industry. International Review of Economics & Finance, 96, 103667. https://doi.org/10.1016/j.iref.2024.103667
  34. Lind, L., Pirttilä, M., Viskari, S., Schupp, F., & Kärri, T. (2012). Working capital management in the automotive industry: Financial value chain analysis. Journal Of Purchasing And Supply Management, 18(2), 92-100. https://doi.org/10.1016/j.pursup.2012.04.003
  35. Meitz, M., & Saikkonen, P. (2021). Testing for observation-dependent regime switching in mixture autoregressive models. Journal of Econometrics, 222(1), 601-624. https://doi.org/10.1016/j.jeconom.2020.04.048
  36. Muslemani, H., Liang, X., Kaesehage, K., Ascui, F., & Wilson, J. (2021). Opportunities and challenges for decarbonising steel production by creating markets for ‘green steel ’ products. Journal of Cleaner Production, 315, 128127. https://doi.org/10.1016/j.jclepro.2021.128127
  37. Nag, B., Banerjee, S., & Chatterjee, R. (2007). Changing features of the automobile industry in Asia: Comparison of production, trade and market structure in selected countries (No. 37). ARTNeT Working Paper Series. https://www.econstor.eu/handle/10419/178395, Access time: 12.08.2025
  38. Neuwirth, M., Fleiter, T., & Hofmann, R. (2024). Modelling the market diffusion of hydrogen-based steel and basic chemical production in Europe–A site-specific approach. Energy Conversion and Management, 322, 119117. https://doi.org/10.1016/j.enconman.2024.119117
  39. Oh, S. Y. (2014). Shifting gears: industrial policy and the automotive industry after the 2008 financial crisis. Business and Politics, 16(4), 641-665. https://doi.org/10.1515/bap-2014-0015
  40. Pal, S. (2015). Evaluation of financial performance in terms of financial ratios empirical study on the Indian automobile industry. International Journal of Business Management and Research (IJBMR), 5(3), 1-8.
  41. Pauwels, K., Silva-Risso, J., Srinivasan, S., & Hanssens, D. M. (2004). New products, sales promotions, and firm value: The case of the automobile industry. Journal of Marketing, 68(4), 142-156. https://doi.org/10.1509/jmkg.68.4.142.42724
  42. Phoong, S. W., Phoong, S. Y., & Khek, S. L. (2022). Systematic literature review with bibliometric analysis on Markov switching model: Methods and applications. Sage Open, 12(2), 21582440221093062. https://doi.org/10.1177/21582440221093062
  43. Pirttilä, M., Virolainen, V. M., Lind, L., & Kärri, T. (2020). Working capital management in the Russian automotive industry supply chain. International Journal of Production Economics, 221, 107474. https://doi.org/10.1016/j.ijpe.2019.08.009
  44. Psaradakis, Z., & Spagnolo, N. (2003). On the determination of the number of regimes in Markov‐switching autoregressive models. Journal of Time Series Analysis, 24(2), 237-252. https://doi.org/10.1111/1467-9892.00305
  45. Qasim, T. B., Iqbal, G. Z., Hassan, M. U., & Ali, H. (2021). Application of Markov Regime Switching Autoregressive Model to Gold Prices in Pakistan. Review of Economics and Development Studies, 7(3), 309-323. https://doi.org/10.47067/reads.v7i3.368
  46. Rafique, M. (2011). Effect of profitability & financial leverage on capital structure: A case of Pakistan’s automobile industry. Available at SSRN 1911395. http://dx.doi.org/10.2139/ssrn.1911395, Access time: 12.08.2025
  47. Rajulwar, V. V., Shyrokykh, T., Stirling, R., Jarnerud, T., Korobeinikov, Y., Bose, S.,. & Sridhar, S. (2023). Steel, aluminium, and FRP-composites: The race to zero carbon emissions. Energies, 16(19), 6904. https://doi.org/10.3390/en16196904
  48. Ray, S. (2011). Assessing corporate financial distress in the automobile industry of India: An application of Altman’s model. Research Journal of Finance and Accounting, 2(3), 155-168. https://core.ac.uk/download/pdf/234629197.pdf
  49. Ravenhill, J. (2001). From national champions to global partnerships: the Korean auto industry, financial crisis and globalisation. MIT Japan Program, Working Paper https://dspace.mit.edu/bitstream/handle/1721.1/16569/JP-WP-01-04-52137339.pdf?sequence=1, Access time: 12.08.2025
  50. Rodríguez-González, R. M., Maldonado-Guzman, G., Madrid-Guijarro, A., & Garza-Reyes, J. A. (2022). Does the circular economy affect financial performance? The mediating role of sustainable supply chain management in the automotive industry. Journal of Cleaner Production, 379, 134670. https://doi.org/10.1016/j.jclepro.2022.134670
  51. Rosli, M. (2006). The Automobile Industry And Performance Of Malaysian Auto Production. Journal of Economic Cooperation Among Islamic Countries, 27(1), 89-114
  52. Saufnay, L., & Demonceau, J. F. (2024). Establishment of reliable relative price predictions for high‐strength steel members. Steel Construction, 17(3), 136-145. https://doi.org/10.1002/stco.202300013.
  53. Sheng, Y., Xu, X., & Rozelle, S. (2024). Market structure, resource allocation, and industry productivity growth: Firm-level evidence from China's steel industry. China Economic Review, 83, 102102. https://doi.org/10.1016/j.chieco.2023.102102
  54. Sondinti, K., & Reddy, L. (2024). Financial Optimisation in the Automotive Industry: Leveraging Cloud-Driven Big Data and AI for Cost Reduction and Revenue Growth. Financial Optimisation in the Automotive Industry: Leveraging Cloud-Driven Big Data and AI for Cost Reduction and Revenue Growth (December 17, 2024). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5072164 Swalih, M., Adarsh, K., & Sulphey, M. (2021). A study on the financial soundness of the Indian automobile industry using Altman Z-Score. Accounting, 7(2), 295-298. https: 10.5267/j.ac.2020.12.001
  55. Şenol, Z. (2020). Analysis of the stock market by the Markov Switching Approach. Erciyes Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (49), 246-256. https://dergipark.org.tr/en/pub/erusosbilder/issue/58345/841936
  56. Upadhyay, A., Ayodele, J. O., Kumar, A., & Garza-Reyes, J. A. (2021). A review of challenges and opportunities of blockchain adoption for operational excellence in the UK automotive industry. Journal of Global Operations and Strategic Sourcing, 14(1), 7-60. https://doi.org/10.1108/JGOSS-05-2020-0024
  57. Vazquez, X. H., Sartal, A., & Lozano-Lozano, L. M. (2016). Watch the working capital of tier-two suppliers: a financial perspective of supply chain collaboration in the automotive industry. Supply Chain Management: An International Journal, 21(3), 321-333. https://doi.org/10.1108/SCM-03-2015-0104
  58. Wad, P. (2009). The automobile industry of Southeast Asia: Malaysia and Thailand. Journal of the Asia Pacific Economy, 14(2), 172-193. https://doi.org/10.1080/13547860902786029
  59. Zamri, F. I. M., Hibadullah, S. N., Fuzi, N. M., Desa, A. F. N. C., & Habidin, N. F. (2013). Green lean six sigma and financial performance in the Malaysian automotive industry. Business Management and Strategy, 4(1), 97-106. http://dx.doi.org/10.5296/bms.v4i1.2885
  60. Zhou, K., & Yang, S. (2016). Emission reduction of China׳ s steel industry: Progress and challenges. Renewable and Sustainable Energy Reviews, 61, 319-327. https://doi.org/10.1016/j.rser.2016.04.009
  61. Xu, J., Liu, F., & Xie, J. (2022). Is too much a good thing? The non-linear relationship between intellectual capital and financial competitiveness in the Chinese automotive industry. Journal of Business Economics and Management, 23(4), 773-796. https://doi.org/10.3846/jbem.2022.16406
  62. Fiaz, A., Khurshid, N., Satti, A., & Malik, M. S. (2021). Real exchange rate misalignment in Pakistan: An application of the regime-switching model. The Journal of Asian Finance, Economics and Business, 8(12), 63-73. DOI: 10.13106/jafeb. 2021.vol8.no12.0063,
  63. Rajulwar, V. V., Shyrokykh, T., Stirling, R., Jarnerud, T., Korobeinikov, Y., Bose, S.,. & Sridhar, S. (2023). Steel, aluminium, and FRP-composites: The race to zero carbon emissions. Energies, 16(19), 6904. https://doi.org/10.3390/en16196904